A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU - Feigl, Tobias; Kram, Sebastian; Woller, Philipp; Siddiqui, Ramiz H.; Philippsen, Michael; Mutschler, Christopher (Proceedings of the 10th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2019)
The main challenge in estimating human velocity from noisy Inertial Measurement Units (IMUs) are the errors that accumulate by integrating noisy accelerometer signals over a long time. Known approaches that work on step length estimation are optimized for a specific application, sensor position, and movement type, require an exhaustive (manual) parameter tuning, and can thus not be applied to other movement types or to a broader range of applications. Moreover, varying dynamics (as they are present for instance in sports applications) cause abrupt and unpredictable changes in step frequency or step length and hence result in erroneous velocity estimates. We use machine learning (ML) and deep learning (DL) to estimate a human’s velocity. Our approach is robust to varying motion states and orientation changes in dynamic situations. On data from a single un-calibrated IMU, our novel recurrent model not only outperforms the state-of-the-art on instantaneous velocity (≤0.10 m/s) and on traveled distance (≤29 m/km). It can also generalize to different and varying rates of motion and provides accurate and precise velocity estimates.

Accuracy-Aware Compression of Channel Impulse Responses using Deep Learning - Altstidl, Thomas Robert; Kram, Sebastian; Herrmann, Oskar; Stahlke, Maximilian; Feigl, Tobias; Mutschler, Christopher (Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021), 2021)
Ultra-wideband (UWB) systems based on Channel State Information (CSI) estimate the position of mobile nodes within an environment by using Channel Impulse Responses(CIRs) of multiple stationary nodes. These contain spatial information caused by environment interactions such as reflections and scattering. To estimate positions from CSI of stationary nodes, we must transmit them to a centralized node. This introduces considerable communication overhead. We present a large-scale study to determine whether CSI can be compressed into a small set of underlying latent variables that describe the most valuable information. We evaluate multiple neural network architectures containing encoding (compressing)and decoding (reconstructing) components and compare them to the state-of-the-art compression techniques Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). We show that fully connected autoencoders achieve the lowest error, outperforming both DCT and DWT. Further experiments prove that the reconstructed CSI can be used for positioning with only mild performance deterioration at a compression of >97% and even when trained on a different environmen
Keywords: Compression; Channel Impulse Response; Channel State Information; DCT; DWT; (Variational) Autoencoder.

Acoustical manipulation for redirected walking - Feigl, Tobias; Mutschler, Christopher; Philippsen, Michael; Kõre, Eliise (Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology (VRST '17), 2017)
Redirected Walking (RDW) manipulates a scene that is displayed to VR users so that they unknowingly compensate for scene motion and can thus explore a large virtual world on a limited space. So far, mostly visual manipulation techniques have been studied. This paper shows that users can also be manipulated by means of acoustical signals. In an experiment with a dynamically moving audio source we see deviations of up to 30% from a 20 m long straight-line walk for male participants and of up to 25% for females. Static audio has about two thirds of this impact.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD2.acoust

Adaptive Speculative Processing of Out-of-Order Event Streams - Mutschler, Christopher; Philippsen, Michael (ACM Transactions on Internet Technology, 2014)
Distributed event-based systems are used to detect meaningful events with low latency in high data-rate event streams that occur in surveillance, sports, finances, etc. However, both known approaches to dealing with the predominant out-of-order event arrival at the distributed detectors have their shortcomings: buffering approaches introduce latencies for event ordering, and stream revision approaches may result in system overloads due to unbounded retraction cascades. This article presents an adaptive speculative processing technique for out-of-order event streams that enhances typical buffering approaches. In contrast to other stream revision approaches developed so far, our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal. Our technique outperforms known approaches on both synthetical data and real sensor data from a realtime locating system (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average. © 2014 ACM.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD2.adapti
Keywords: Distributed event processing; Low latency; Message-oriented middleware; Out-of-order event processing; Publish/subscribe; Speculative processing

A Deep Learning Approach to Position Estimation from Channel Impulse Responses - Niitsoo, Arne; Edelhäußer, Thorsten; Hadaschik, Niels; Eberlein, Ernst; Mutschler, Christopher (Sensors, 2019)
Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.<br
Keywords: radio-based real-time locating systems; time difference of arrival; channel impulse response; time of arrival; position estimation; machine learning; deep learning; convolutional neural networks; distributed CNN

A Framework for Location-Based VR Applications - Lugrin, Jean-Luc; Kern, Florian; Kleinbeck, Constantin; Roth, Daniel; Daxer, Christian; Feigl, Tobias; Mutschler, Christopher; Latoschik, Marc Erich (Virtuelle und Erweiterte Realitat: 16. Workshop der GI-Fachgruppe VR/AR (Berichte aus der Informatik), 2019)
This paper presents a framework to develop and investigate location-based Virtual Reality (VR) applications. We demonstrate our framework by introducing a novel type of VR museum, designed to support a large number of simultaneous co-located users. These visitors are walking in a hangar-scale tracking zone (600 m 2 ), while sharing a ten times bigger virtual space (7000 m 2 ). Co-located VR applications like this one are opening novel VR perspectives. However, sharing a limitless virtual world using a large, but limited, tracking space is also raising numerous challenges: from financial considerations and technical implementation to interactions and evaluations (e.g., user’s representation, navigation, health & safety, monitoring). How to design, develop and evaluate such a VR system is still an open question. Here, we describe a fully implemented framework with its specific features and performance optimizations. We also illustrate our framework’s viability with a first VR application and discuss its potential benefits for education and future evaluation.<br
Keywords: Virtual Reality; Framework; Hangar-Scale Tracking; Multi-users; Museum

A Location-Based VR Museum - Lugrin, Jean-Luc; Kern, Florian; Schmidt, Ruben; Kleinbeck, Constantin; Roth, Daniel; Daxer, Christian; Feigl, Tobias; Mutschler, Christopher; Latoschik, Marc-Erich (Proceedings of the 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games 2018), 2018)
This poster presents a novel type of Virtual Reality (VR) application for education and culture: a location-based VR Museum, which is a large-room scale multi-user multi-zone virtual museum. This VR museum was designed to support over 100 simultaneous users, walking in a large tracking system (600 m2) and sharing a ten times bigger virtual space (7000 m2) containing indoor and outdoor dinosaur exhibitions. This poster is giving an overview of the system and its main features as well as discussing its potential benefits and future evaluation.
Keywords: VR Education; Large Tracking System; Avatar

Approximative Event Processing on Sensor Data Streams (Best Poster and Demostration Award) - Löffler, Christoffer; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (DEBS'15), 2015)
Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But they struggle with noise or incompleteness that is seen in the unprecedented amount of data generated by the Internet of Things We present a generic approach that deals with uncertain data in the middleware layer of distributed event-based systems and is hence transparent for developers. Our approach calculates alternative paths to improve the overall result of the data analysis. It dynamically generates, updates, and evaluates Bayesian Networks based on probability measures and rules defined by developers. An evaluation on position data shows that the improved detection rate justifies the computational overhead.
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD2.approx
Keywords: event processing; streaming; cep; event processing; uncertainty; bayesian networks

Automatische Generierung von AUTOSAR Software Component Descriptions. - Mutschler, Christopher (2009)
UnivIS-Import:2016-07-26:Pub.2009.tech.IMMD.IMMD2.automa

Beyond Replication: Augmenting Social Behaviors in Multi-User Social Virtual Realities - Roth, Daniel; Kleinbeck, Constantin; Feigl, Tobias; Mutschler, Christopher; Latoschik, Marc-Erich (Proceedings of the 25th IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2018), 2018)
This paper presents a novel approach for the augmentation of so- cial behaviors in virtual reality (VR). We designed three visual transformations for behavioral phenomena crucial to everyday so- cial interactions: eye contact, joint attention, and grouping. To evaluate the approach, we let users interact socially in a virtual mu- seum using a large-scale multi-user tracking environment. Using a between-subject design (N = 125) we formed groups of five par- ticipants. Participants were represented as simplified avatars and experienced the virtual museum simultaneously, either with or with- out the augmentations. Our results indicate that our approach can significantly increase social presence in multi-user environments and that the augmented experience appears more thought-provoking. Furthermore, the augmentations seem also to affect the actual behav- ior of participants with regard to more eye contact and more focus on avatars/objects in the scene. We interpret these findings as first indicators for the potential of social augmentations to impact social perception and behavior in VR.

Complementary Semi-Deterministic Clusters for Realistic Statistical Channel Models for Positioning - Alawieh, Mohammad; Eberlein, Ernst; Jaeckel, Stephan; Franke, Norbert; Ghimire, Birendra; Feigl, Tobias; Yammine, George; Mutschler, Christopher (2022)
Positioning benefits from channel models that capture geometric effects and, in particular, from the signal properties of the first arriving path and the spatial consistency of the propagation condition of multiple links. The models that capture the physical effects observed in a realistic deployment scenario are essential for assessing the potential benefits of enhancements in positioning methods. Channel models based on ray-tracing simulations and statistical channel models, which are current state-of-the-art methods employed to evaluate performance of positioning in 3GPP systems, do not fully capture important aspects applicable to positioning. Hence, we propose an extension of existing statistical channel models with semi-deterministic clusters (SDCs). SDCs allow channels to be simulated using three types of clusters: fixed-, specular-, and random-clusters. Our results show that the proposed model aligns with measurements obtained in a real deployment scenario. Thus, our channel models can be used to develop advanced positioning solutions based on machine learning, which enable positioning with centimeter level accuracy in NLOS and multipath scenarios.<br
Keywords: Positioning; channel model; 5G; artificial intel- ligence; machine learning; spatial consistency

Contact Tracing with the Exposure Notification Framework in the German Corona-Warn-App - Meyer, Steffen; Windisch, Thomas; Perl, Adrian; Dzibela, Daniel; Marzilger, Robert; Witt, Nicolas; Benzler, Justus; Kirchner, Göran; Feigl, Tobias (Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021), 2021)
Digital Contact Tracing (CT) protocols based on Bluetooth are best implemented at the system level to save resources and preserve security aspects. Combined with a government-monitored software platform, these CT-protocols can then be used to support controlling pandemics such as COVID-19. However, it is unclear how these protocols have to be parameterized to ensure the most accurate and reliable CT. This paper describes how we derived optimal parameters for a decentralized CT from extensive measurement campaigns that we carried out together with Deutsche Telekom (DT) and SAP under the supervision of the Robert Koch Institut (RKI). We examined the Google/Apple Exposure Notification Framework (ENF), which in combination with the front-end, i.e., the German Corona-Warn-App (CWA), enables digital CT in Germany. With centimeter accurate optical reference systems we show that optimal parameters are application-specific. However, they cause impractical high resource costs. In contrast, optimized general parameters offer an everyday compromise between energy costs, applicability, accuracy, and reliability of the EN
Keywords: Digital Decentralized Contact Tracing; COVID-19; SARS-CoV-2; German Corona-Warn-App; CWA; ENF.

Convolutional Neural Networks for Position Estimation in TDoA-based Locating Systems - Niitsoo, Arne; Edelhaeusser, Thorsten; Mutschler, Christopher (2018 NINTH INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN 2018), 2018)
Object localization and tracking is essential for many applications including logistics and industry. Many local Time-of-Flight (ToF)-based locating systems use synchronized antennas to receive radio signals emitted by mobile tags. They detect the Time-of-Arrival (TOA) of the signal at each antenna and trilaterate the position from the Time Difference-of-Arrival (TDoA) between antennas. However, in multipath scenarios it is difficult to extract the correct ToA. This causes wrong positions. This paper proposes a signal processing method that uses deep learning to estimate the absolute tag position directly from the raw channel impulse response (CIR) data. We use the CIR together with ground truth positional data to train a convolutional neural network (CNN) that not only estimates non-linearities in the signal propagation space but also analyzes the signal for multipath effects. Our evaluation shows that our position estimation works in multipath environments and also outperforms classical signal processing in line-of-sight situations.
CRIS-Team WoS Importer:2019-11-26

Delay Estimation in Dense Multipath Environments using Time Series Segmentation - Kram, Sebastian; Kraus, Christopher; Stahlke, Maximilian; Feigl, Tobias; Thielecke, Jörn; Mutschler, Christopher (Proc. Intl. Conf. IEEE Wireless Communications and Networking Conference (WCNC), 2022)
Channel measurements at sufficiently high bandwidth in multipath-rich environments include a variety of delay information, which, if accurately extracted, can be exploited for accurate positioning. While previous methods are limited in practice as they rely on iteratively extracting a fixed number of delays, we instead formulate the delay extraction problem as a time series segmentation task. For this, we propose a pipeline built upon the U-Net convolutional neural network architecture. Unlike the state of the art our pipeline extracts an arbitrary number of delays without prior knowledge, includes a threshold for weighting between detection rate and false alarms, and does not rely on computationally demanding operations such as eigenvalue decomposition. We evaluate the presented method with synthetic data of different noise configurations and signal bandwidths and a publicly available dataset, achieving considerable performance gains w.r.t. detection performance and tracking accuracy. Furthermore, we show that the proposed method is far less computationally demanding in inference.<br

Distributed Low-Latency Out-of-Order Event Processing for High Data Rate Sensor Streams - Mutschler, Christopher; Philippsen, Michael (Proceedings of 27th International Parallel and Distributed Processing Symposium (IPDPS'13), 2013)
Event-based Systems (EBS) are used to detect and analyze meaningful events in surveillance, sports, finances and many other areas. With rising data and event rates and with correlations among these events, sequential event processing becomes infeasible and needs to be distributed. Existing approaches cannot deal with the ubiquity of out-of-order event arrival that is introduced by network delays when distributing EBS. Order-less event processing may result in a system failure. We present a low-latency approach based on K-slack that achieves ordered event processing on high data rate sensor and event streams without a-priori knowledge. Slack buffers are dynamically adjusted to fit the disorder in the streams without using local or global clocks. The middleware transparently reorders the event input streams so that events can still be aggregated and processed to a granularity that satisfies the demands of the application. On a Real time Locating System (RTLS) our system performs accurate low-latency event detection under the predominance of out-of-order vent arrival and with a close to linear performance scale-up when the system is distributed over several threads and machines. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD2.distri
Keywords: Event-based Processing; Message-oriented Middleware; Scalability and Performance; Publish/Subscribe

Dynamic Low-Latency Distributed Event Processing of Sensor Data Streams - Mutschler, Christopher; Philippsen, Michael (Parallel-Algorithmen und Rechnerstrukturen (PARS 2013), 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD2.dynami

Estimating TOA Reliability with Variational Autoencoders - Stahlke, Maximilian; Kram, Sebastian; Ott, Felix; Feigl, Tobias; Mutschler, Christopher (IEEE Sensors Journal, 2021)
Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in the time of flight (TOF) estimation and hence to a degraded localization accuracy. A major topic in high-precision RF-based localization is the identification of such anomalous signals that negatively affect the localization performance, and to mitigate the errors introduced by them. As such signal and error characteristics depend on the environment, data-driven approaches have shown to be promising. However, there is a trade-off to a bad generalization and a need for an extensive and time-consuming recording of training data associated with it. We propose to use generative deep learning models for out-of-distribution detection based on channel impulse responses (CIRs). We use a Variational Autoencoder (VAE) to predict an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our experiments show that a VAE trained only on line-of-sight (LOS) training data generalizes well to new environments and detects non-line-of-sight CIRs with an accuracy of 85%. We also show that integrating our anomaly score into a TOF-based extended Kalman filter (EKF) improves tracking performance by over 25%.
Created from Fastlane, Scopus look-up
Keywords: channel quality estimation; CIR; Deep Learning; Feature extraction; Location awareness; NLOS identification; NLOS mitigation; Receivers; Reliability; Sensors; Support vector machines; Training; UWB; VAE

Evaluation Criteria for Inside-Out Indoor Positioning Systems Based on Machine Learning - Löffler, Christoffer; Riechel, Sascha; Fischer, Janina; Mutschler, Christopher (International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018)

Evolutionary Algorithms that use Runtime Migration of Detector Processes to Reduce Latency in Event-Based Systems - Löffler, Christoffer; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 2013 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013), 2013)
Event-based systems (EBS) are widely used to efficiently process massively parallel data streams. In distributed event processing the allocation of event detectors to machines is crucial for both the latency and efficiency, and a naive allocation may even cause a system failure. But since data streams, network traffic, and event loads cannot be predicted sufficiently well the optimal detector allocation cannot be found a-priori and must instead be determined at runtime. This paper describes how evolutionary algorithms (EA) can be used to minimize both network and processing latency by means of runtime migration of event detectors. The paper qualitatively evaluates the algorithms on synthetical data streams in a distributed event-based system. We show that some EAs work efficiently even with large numbers of event detectors and machines and that a hybrid of Cuckoo Search and Particle Swarm Optimization outperforms others. © 2013 IEE
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD2.evolut
Keywords: Distributed computing; Evolutionary algorithms; Heuristics; Particle swarm optimization; Publish/Subscribe

Head-to-Body-Pose Classification in No-Pose VR Tracking Systems - Feigl, Tobias; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 25th IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2018), 2018)
Pose tracking does not yet reliably work in large-scale interactive multi-user VR. Our novel head orientation estimation combines a single inertial sensor located at the user’s head with inaccurate posi- tional tracking. We exploit that users tend to walk in their viewing direction and classify head and body motion to estimate heading drift. This enables low-cost long-time stable head orientation. We evaluate our method and show that we sustain immersion.
Keywords: VR; headtracking; inertialsensorfusion; immersion; large-scale; machine learning; motion sickness

Hierarchical learning for model predictive collision avoidance - Landgraf, Daniel; Völz, Andreas; Kontes, Georgios; Graichen, Knut; Mutschler, Christopher (IFAC PapersOnLine, 2022)

Human Compensation Strategies for Orientation Drifts - Feigl, Tobias; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 25th IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2018), 2018)
No-Pose (NP) tracking systems rely on a single sensor located at the user’s head to determine the position of the head. They estimate the head orientation with inertial sensors and analyze the body mo- tion to compensate their drift. However with orientation drift, VR users implicitly lean their heads and bodies sidewards. Hence, to determine the sensor drift and to explicitly adjust the orientation of the VR display there is a need to understand and consider both the user’s head and body orientations. This paper studies the effects of head orientation drift around the yaw axis on the user’s absolute head and body orientations when walking naturally in the VR. We study how much drift accumulates over time, how a user experiences and tolerates it, and how a user applies strategies to compensate for larger drifts.
Keywords: VR; orientation drift; head tracking; inertial sensors

IALE: Imitating Active Learner Ensembles - Löffler, Christoffer; Mutschler, Christopher (Journal of Machine Learning Research, 2022)
Active learning prioritizes the labeling of the most informative data samples. However, the performance of active learning heuristics depends on both the structure of the underlying model architecture and the data. We propose IALE, an imitation learning scheme that imitates the selection of the best-performing expert heuristic at each stage of the learning cycle in a batch-mode pool-based setting. We use Dagger to train a transferable policy on a dataset and later apply it to different datasets and deep classifier architectures. The policy reflects on the best choices from multiple expert heuristics given the current state of the active learning process, and learns to select samples in a complementary way that unifies the expert strategies. Our experiments on well-known image datasets show that we outperform state of the art imitation learners and heuristics.
Keywords: active learning; deep neural networks; imitation learning; dataset aggregation; transferable policy

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach - Stahlke, Maximilian; Yammine, George; Feigl, Tobias; Eskofier, Björn; Mutschler, Christopher (IEEE Transactions on Machine Learning in Communications and Networking, 2023)
Fingerprinting (FP) significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. Recently, channel charting (CC) has been investigated because it is an unsupervised method that does not need labeled measurement data. CC uses pairwise-distance metrics to estimate the physical distance between channel-state-information (CSI) measurements in the proximity. CC can then learn the underlying manifold of the channel measurements. While CC has shown promising results in modelling the local geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending. We therefore contribute a novel distance metric for CC that approaches a global linear correlation to the physical distance based on time-synchronized single-input/single-output (SISO) CSIs. This allows to learn the environment's global geometry in a channel chart without annotations. We leverage a Siamese network, which enables CC-assisted FP only using a linear transformation from the chart to the real-world coordinates. We compare our approach to the state-of-the-art of CC on two different real-world data sets recorded with a 5G and UWB radio setup. Unlike FP-based localization which needs a large number of labeled data points to achieve a superior localization accuracy, our approach outperforms FP when only a few labeled data samples are available.
Keywords: Channel charting; machine learning; fingerprinting; localization; 5G; UWB; time-based measurements.

Indoor Positioning using OFDM-based Visible Light Communication System - Ghimire, Birendra; Seitz, Jochen; Mutschler, Christopher (2018 NINTH INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN 2018), 2018)
CRIS-Team WoS Importer:2019-11-26

Inter-Satellite Ranging in the Low Earth Orbit - Alawieh, Mohammad; Hadaschik, Niels; Franke, Norbert; Mutschler, Christopher (Proceedings of the 10th IEEE/IET International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP'16), 2016)
Many satellite systems require knowledge about inter-satellite distances. Inter-satellite links provide direct connectivity between satellites and may be used for ranging, while they remove the need of dedicated hardware. This paper addresses inter-satellite ranging at the Low Earth Orbit (LEO) using S-Band signals. We take the requirements from recent missions and future applications into account and analyze the factors that limit ranging. We show how these factors affect the quality of distance estimation in terms of the Cramér-Rao Lower Bound for ranging. Two-way-ranging (TWR) provides the best accuracy and sustains the low-cost objective of small satellites. We further propose an enhanced TWR message exchange that enables on-the-fly delay calibration and sub-sample corrections. We implemented the transceiver modules on a Software-Defined Radio (SDR) platform and evaluated them with real-world data. The results show that the proposed ranging algorithm achieves an accuracy of few centimeters.
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD2.inters

Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach - Ott, Felix; Ruegamer, David; Heublein, Lucas; Bischl, Bernd; Mutschler, Christopher (2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022), 2022)
CRIS-Team WoS Importer:2022-07-08

Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks - Altstidl, Thomas Robert; Nguyen, An; Schwinn, Leo; Köferl, Franz; Mutschler, Christopher; Eskofier, Björn; Zanca, Dario (Proc. Intl. Joint Conf. Neural Netw. (IJCNN), 2023)
The widespread success of convolutional neural networks may largely be attributed to their intrinsic property of translation equivariance. However, convolutions are not equivariant to variations in scale and fail to generalize to objects of different sizes. Despite recent advances in this field, it remains unclear how well current methods generalize to unobserved scales on real-world data and to what extent scale equivariance plays a role. To address this, we propose the novel Scaled and Translated Image Recognition (STIR) benchmark based on four different domains. Additionally, we introduce a new family of models that applies many re-scaled kernels with shared weights in parallel and then selects the most appropriate one. Our experimental results on STIR show that both the existing and proposed approaches can improve generalization across scales compared to standard convolutions. We also demonstrate that our family of models is able to generalize well towards larger scales and improve scale equivariance. Moreover, due to their unique design we can validate that kernel selection is consistent with input scale. Even so, none of the evaluated models maintain their performance for large differences in scale, demonstrating that a general understanding of how scale equivariance can improve generalization and robustness is still lacking.<br
Keywords: Image recognition; Neural networks; Benchmark testing; Robustness; Convolutional neural networks; Kernel; Standards

Latency Minimization of Order-Preserving Distributed Event-Based Systems - Mutschler, Christopher (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)

Learning Event Detection Rules with Noise Hidden Markov Models - Mutschler, Christopher; Philippsen, Michael (Proceedings of the 2012 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2012), 2012)
Complex Event Processing (CEP) is a popular method to monitor processes in several contexts, especially when dealing with incidents at distinct points in time. Specific temporal combinations of various events are often of special interest for automatic detection. For the description of such patterns, one can either implement rules in some higher programming language or use some Event Description Language (EDL). Both is complicated and error-prone for non-engineers, because it varies greatly from natural language. Therefore, we present a method, by which a domain expert can simply signal the occurrence of a significant incident at a specific point in time. The system then infers rules for automatically detecting such occurrences later on. At the core of our approach is an extension of hidden Markov models (HMM) called noise hidden Markov models (nHMM) that can be trained with existing, low-level event data. The nHMM can be applied online without any intervention of programming experts. An evaluation on both synthetic and real event data shows the efficiency of our approach even under the presence of highly frequent, insignificant events and uncertainty in the data. © 2012 IEEE.
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.IMMD2.learni
Keywords: Complex Event Processing; Hidden Markov Models; Event Streams; Knowledge Discovery

Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-Scale Industry Environments - Feigl, Tobias; Porada, Andreas; Steiner, Steve; Löffler, Christoffer; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, 2020)
Augmented Reality (AR) systems are envisioned to soon be used as smart tools across many Industry 4.0 scenarios. The main promise is that such systems will make workers more productive when they can obtain additional situationally coordinated information both seemlessly and hands-free. This paper studies the applicability of today’s popular AR systems (Apple ARKit, Google ARCore, and Microsoft Hololens) in such an industrial context (large area of 1,600m 2 , long walking distances of 60m between cubicles, and dynamic environments with volatile natural features). With an elaborate measurement campaign that employs a submillimeter accurate optical localization system, we show that for such a context, i.e., when a reliable and accurate tracking of a user matters, the Simultaneous Localization and Mapping (SLAM) techniques of these AR systems are a showstopper. Out of the box, these AR systems are far from useful even for normal motion behavior. They accumulate an average error of about 17m per 120m, with a scaling error of up to 14.4cm/m that is quasi-directly proportional to the path length. By adding natural features, the tracking reliability can be improved, but not enough.<br
Keywords: Augmented Reality (AR); Simultaneous Localization and Mapping (SLAM); Industry 4.0; Apple ARKit; Google ARCore; Microsoft Hololens

Low-Complexity PDoA-based Localization - Sackenreuter, Benjamin; Hadaschik, Niels; Fassbinder, Marc; Mutschler, Christopher (Proceedings of the 7th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016), 2016)
Localization of wireless nodes within the IoT received much attention lately. However, strong constraints on power consumption, scalability, and complexity of the nodes pose a big challenge for localization techniques. This paper presents a concept for energy-efficient low-complexity localization based on Phase Difference of Arrival (PDoA). Besides a novel method for reference transmitter selection we propose a waveform, well-suited for PDoA measurements, and evaluate its ranging performance. We compare multiple signal classification (MUSIC), linear fitting, and mean phase difference and compare their estimation variance to the Cramer Rao Lower Bound (CRLB). Our system concept allows for the mitigation of near-far effects for reference and tag signals at the receiver nodes, and an efficient implementation of a wideband frequency hopping scheme.
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD2.lowcom

microYOLO: Towards Single-Shot Object Detection on Microcontrollers - Deutel, Mark; Mutschler, Christopher; Teich, Jürgen (International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part V, 2025)
This work-in-progress paper presents results on the feasibility of single-shot object detection on microcontrollers using YOLO. Single-shot object detectors like YOLO are widely used, however due to their complexity mainly on larger GPU-based platforms. We present microYOLO, which can be used on Cortex-M based microcontrollers, such as the OpenMV H7 R2, achieving about 3.5 FPS when classifying 128x128 RGB images while using less than 800 KB Flash and less than 350 KB RAM. Furthermore, we share experimental results for three different object detection tasks, analyzing the accuracy of microYOLO on them.<br
Keywords: TinyML; Microcontrollers; Object Detection

Multipath Delay Estimation in Complex Environments using Transformer - Ott, Jonathan; Stahlke, Maximilian; Kram, Sebastian; Feigl, Tobias; Mutschler, Christopher (Proc. 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023), 2023)
Modern radio frequency based positioning systems exploit multipath propagation to achieve accurate and robust positioning at a minimum effort in infrastructure. A key concept is exploitation of multipath component (MPC) delays from channel measurements, which have a direct relation to the geometry of the environment. This is a challenging task given complex multipath-rich environments and limited bandwidths. However, downstream tasks suffer from false or missed detections, which is why reliable MPC detection and delay estimation is crucial. We propose an MPC delay estimation pipeline based on a Transformer (TF) neural network, which implicitly estimates the number and delays of the MPCs. We achieve subsample accuracy without using computational expensive super-resolution techniques. Our approach outperforms state-of-the art on detection and delay estimation of MPCs on different bandwidths. We also show that our approach can easily be fine-tuned on real world data with very few labeled data samples, making it a well-suited candidate for real world deployments.<br
Keywords: Multipath Delay Estimation; Machine Learning; Deep Learning; Transformer; Attention Mechanism; 5G; UWB

Non-Line-of-Sight Detection for Radio Localization using Deep State Space Models - Brasseler, Leon; Stahlke, Maximilian; Altstidl, Thomas Robert; Feigl, Tobias; Mutschler, Christopher (The fourteenth International Conference on Indoor Positioning and Indoor Navigation 2024 (IPIN 2024), 2024)
Localization based on channel impulse responses (CIRs) of radio frequency (RF) signals yields centimeter-accurate positions under optimal line-of-sight (LOS) propagation condi- tions. However, in real indoor environments, e.g., in car manufac- turing, non-line-of-sight (NLOS) situations dominate. Here, mul- tipath propagation affects the time-of-arrival (ToA) estimation and downstream multilateration and localization accuracy. The detection and subsequent mitigation of NLOS per transceiver line compensates for these effects. To detect NLOS, the state-of-the- art employs either supervised or unsupervised learning methods that require the acquisition of expensive reference data or do not generalize to changes or unknown environments. This is due to, among other things, the fact that they cannot exploit spatial and temporal information from CIR signal streams. Thus, we propose a generative deep state space model (SSM) for NLOS detection on CIRs that exploits time and space. Our ultra-wideband (UWB) experiments show that our dynamical variational autoencoder (DVAE) detects NLOS signals from sequences of CIRs more accurately than the state-of-the-art and is robust to unknown environment
Keywords: CIR; Dynamical Variational Autoencoder; NLOS detection; State Space Model; UWB.

On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers - Deutel, Mark; Hannig, Frank; Mutschler, Christopher; Teich, Jürgen (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024)
On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is a resource-intensive task, making the implementation and execution of DNN training algorithms on MCUs challenging due to low processor speeds, constrained throughput, limited floating-point support, and memory constraints. In this work, we explore on-device training DNNs for different sized Cortex-M MCUs (Cortex-M0+, Cortex-M4, and Cortex-M7). We present a method that enables efficient training of DNNs completely in place on the MCU using fully quantized training (FQT) and dynamic partial gradient updates. We demonstrate the feasibility of our approach on multiple vision and time-series datasets and provide insights into the tradeoff between training accuracy, memory overhead, energy, and latency on real hardware. The results show that compared to related work, our approach requires 34.8% less memory and has a 49.0% lower latency per training sample, with dynamic partial gradient updates allowing a speedup of up to 8.7 compared to fully updating all weights.<br
Keywords: On-Device Training; TinyML; Efficient AI; Embedded Machine Learning; Microcontrollers

Online Data-Mining of trajectories in Real Time Location Systems - Mutschler, Christopher (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2010)
UnivIS-Import:2016-07-26:Pub.2010.tech.IMMD.IMMD2.online

Online Data Stream Mining on Interactive Trajectories in Soccer Games - Mutschler, Christopher; Kókai, Gabriella; Edelhäußer, Thorsten (Proceedings of the 2nd International Conference on Positioning and Context-Awareness (PoCA 2011), 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD2.online

Optical Camera Communication for Active Marker Identification in Camera-based Positioning Systems - Gorse, Lorenz; Löffler, Christoffer; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 15th Workshop on Positioning, Navigation and Communications (WPNC'18), 2018)
Outside-in camera-based localization systems deter- mine the position of mobile objects (with markers attached to them) by observing a tracking area with multiple camera anchors. Up to now the identification of the objects in the camera images only works for close objects (with passive 3- dimensional marker constellations) or when tracking gaps or a slow identification are acceptable (with an active LED that blinks a unique identification code). We present a novel Optical Camera Communication method whose LEDs are never switched off completely to allow continu- ous tracking. We encode bits with variations of light intensity and show how an outside-in optical localization system can reliably identify the markers within a few frames. The identification works with a bit error ratio of 7.5·10−5. It works for moving markers (with speeds of at least up to 4 m/s), for distant markers (with distances of at least up to 44m), for multiple markers, and under moderate ambient infrared light.

Predictive Load Management in Smart Grid Environments - Mutschler, Christopher; Löffler, Christoffer; Witt, Nicolas; Edelhäußer, Thorsten; Philippsen, Michael (Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems (DEBS'14), 2014)
The DEBS 2014 Grand Challenge targets the monitoring and prediction of energy loads of smart plugs installed in private households. This paper presents details of our middleware solution and efficient median calculation, shows how we address data quality issues, and provides insights into our enhanced prediction based on hidden Markov models. The evaluation on the smart grid data set shows that we process up to 244k input events per second with an average detection latency of only 13.3ms, and that our system efficiently scales across nodes to increase throughput. Our prediction model significantly outperforms the median-based prediction as it deviates much less from the real load values, and as it consumes considerably less memory.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD2.grandc
Keywords: event processing; streaming; cep

Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization - Ott, Jonathan; Pirkl, Jonas; Stahlke, Maximilian; Feigl, Tobias; Mutschler, Christopher (The fourteenth edition of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2024)
Artificial Intelligence (AI)-based radio fingerprint- ing (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model and data orchestration of FP are time-consuming and costly, as it requires many reference positions and extensive measurement campaigns for each environment. Instead, modern unsupervised and self-supervised learning schemes require less reference data for localization, but either their accuracy is low or they require additional sensor information, rendering them impractical. In this paper we propose a self-supervised learning framework that pre-trains a general transformer (TF) neural network on 5G channel measurements that we collect on-the-fly without expensive equipment. Our novel pretext task randomly masks and drops input information to learn to reconstruct it. So, it implicitly learns the spatiotemporal patterns and information of the propagation environment that enable FP-based localization. Most interestingly, when we optimize this pre-trained model for localization in a given environment, it achieves the accuracy of state-of-the-art methods but requires ten times less reference data and significantly reduces the time from training to operatio
Keywords: Channel Impulse Response; Deep Learning; Generative Pre-trained Transformer (GPT); Pre-training; Radio-based Localization; Self-Supervised; Transformer.

Real-Time Gait Reconstruction for Virtual Reality Using a Single Sensor - Feigl, Tobias; Gruner, Lisa; Mutschler, Christopher; Roth, Daniel (Adjunct Proceedings of the 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2020, 2020)
Embodying users through avatars based on motion tracking and reconstruction is an ongoing challenge for VR application developers. High quality VR systems use full-body tracking or inverse kinematics to reconstruct the motion of the lower extremities and control the avatar animation. Mobile systems are limited to the motion sensing of head-mounted displays (HMDs) and typically cannot offer this.We propose an approach to reconstruct gait motions from a single head-mounted accelerometer. We train our models to map head motions to corresponding ground truth gait phases. To reconstruct leg motion, the models predict gait phases to trigger equivalent synthetic animations. We designed four models: A threshold-based, a correlation-based, a Support Vector Machine (SVM)-based and a bidirectional long-Term short-Term memory (BLSTM)-based model. Our experiments show that, while the BLSTM approach is the most accurate, only the correlation approach runs on a mobile VR system in real time with sufficient accuracy. Our user study with 21 test subjects examined the effects of our approach on simulator sickness and showed significantly less negative effects on disorientation.
CRIS-Team Scopus Importer:2021-03-29
Keywords: Human-centered computing; Interaction paradigms; Virtual reality

Recurrent Neural Networks on Drifting Time-of-Flight Measurements - Feigl, Tobias; Nowak, Thorsten; Philippsen, Michael; Edelhäußer, Thorsten; Mutschler, Christopher (Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018), 2018)
Kalman filters (KFs) are popular methods to es- timate position information from a set of time-of-flight (ToF) values in radio frequency (RF)-based locating systems. Such filters are proven to be optimal under zero-mean Gaussian error distributions. In presence of multipath propagation ToF measurement errors drift due to small-scale motion. This results in changing phases of the multipath components (MPCs) which cause a drift on the ToF measurements. Thus, on a short- term scale the ToF measurements have a non-constant bias that changes while moving. KFs cannot distinguish between the drifting measurement errors and the true motion of the tracked object. Hence, very rigid motion models have to be used for the KF which commonly causes the filters to diverge. Therefore, the KF cannot resolve the short-term errors of consecutive measurements and the long-term motion of the tracked object. This paper presents a data-driven approach that uses training sequences to derive a near-optimal position estimator. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) learns to interpret drifting errors in ToF measurements of a tracked dynamic object directly from raw ToF data. Our evaluation shows that our approach outperforms state-of-the- art KFs on both synthetically generated and real-world dynamic motion trajectories that include drifting ToF measurement errors.

Reliable Speculative Processing of Out-of-Order Event Streams in Generic Publish/Subscribe Middlewares - Mutschler, Christopher; Philippsen, Michael (Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems (DEBS'13), 2013)
In surveillance, sports, finances, etc., distributed event-based systems are used to detect meaningful events with low latency in high data rate event streams. Both known approaches to deal with the predominant out-of-order event arrival at the distributed detectors have their shortcomings: buffering approaches introduce latencies for event ordering and stream revision approaches may result in system overloads due to unbounded retraction cascades. This paper presents a speculative processing technique for out-of-order event streams that enhances typical buffering approaches. In contrast to other stream revision approaches our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal. Our technique outperforms known approaches on both synthetical data and real sensor data from a Realtime Locating System (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average. Copyright © 2013 ACM.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD2.reliab
Keywords: Distributed event processing; Message-oriented middleware; Out-of-order event processing; Publish/subscribe

RNN-aided Human Velocity Estimation from a Single IMU - Feigl, Tobias; Kram, Sebastian; Woller, Philipp; Siddiqui, Ramiz H.; Philippsen, Michael; Mutschler, Christopher (Sensors, 2020)
Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16m/s) and traveled distance (≤3m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.<br
Keywords: inertial navigation; human activity recognition; motion tracking; velocity estimation; machine learning

Robust ToA-Estimation using Convolutional Neural Networks on Randomized Channel Models - Feigl, Tobias; Eberlein, Ernst; Kram, Sebastian (Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021), 2021)
Many radio-based positioning systems use time-of-arrival (ToA). We obtain it from the first and direct path of arrival (FDPoA) in a corresponding set of multipath components (MPC) of the underlying channel state information (CSI). While detection of the FDPoA under Line-of-Sight (LoS) is simple, itis prone to errors in environments with specular and diffuse reflections, as well as nonlinear diffraction, absorption, and transmission of a signal. Such Obstructed- or Non-Line-of-Sight (OLoS, NLoS) situations lead to incorrect FDPoA and consequently to incorrect ToA estimates and inaccurate positions. State-of-the-art estimators suffer from a high computing effort and most of them fail at low signal-to-noise ratios (SNRs). We propose a deep learning (DL) approach to identify optimal FDPoAs as ToA directly from the raw CSI. Our 1D Convolutional Neural Network (CNN) learns the spatial distribution of MPCs of the CSI to predict correct estimates of the ToA. To train our DL model, we use QuaDRiGa to generate datasets with CIRs and ground truth ToAs for realistic 5G channel models. We found that Delay Spread (DS), k-Factor (kF), and SNR are appropriate metrics to cover most LoS-NLoS constellations in realistic datasets. We compare our DL model with the state-of-the-art estimators such as threshold (PEAK), inflection point (IFP), MUSIC, and DL and show that we consistently outperform them by about 17 % for SNRs below -10d
Keywords: TOA Channel Parameter Estimation; InflectionPoint; MUSIC; Machine Learning; Deep Learning

Runtime Migration of Stateful Event Detectors with Low-Latency Ordering Constraints - Mutschler, Christopher; Philippsen, Michael (Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications Workshops, 2013)
Runtime migration has been widely adopted to achieve several tasks such as load balancing, performance optimization, and fault-tolerance. However, existing migration techniques do not work for event detectors in distributed publish/subscribe systems that are used to analyze sensor data. Since low-latency time-constraints are no longer valid they reorder streams incorrectly and cause erroneous event detector states. This paper presents a safe runtime migration of stateful event detectors that respects low-latency time-constraints and seamlessly orders input events correctly on the migrated host. Event streams are only forwarded until timing delays are properly calibrated, the migrated event detector immediately stops processing after its state is transferred, and the processing overhead is negligible. On a Realtime Locating System (RTLS) we show that we can efficiently migrate event detectors at runtime between servers where other techniques would fail. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD2.runtim

Sick Moves! Motion Parameters as Indicators of Simulator Sickness - Feigl, Tobias; Roth, Daniel; Gradl, Stefan; Wirth, Markus; Latoschik, Marc Erich; Eskofier, Björn; Philippsen, Michael; Mutschler, Christopher (IEEE Transactions on Visualization and Computer Graphics, 2019)
We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.<br
Keywords: Human-centered computing; virtual reality; user studies; computing methodologies; perception; machine learning

Social Augmentations in Multi-User Virtual Reality: A Virtual Museum Experience - Roth, Daniel; Kleinbeck, Constantin; Feigl, Tobias; Mutschler, Christopher; Latoschik, Marc Erich (Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct), 2017)
This work in progress report demonstrates a novel approach for behavioral augmentations in Virtual Reality (VR). Using a large scale tracking system, groups of five users explored a virtual museum. We investigated how augmenting social interactions impacts this experience, by designing behavioral transformations for behavioral phenomena in social interactions. Preliminary data indicate a reduction of perceived isolation, and a more thought-provoking experience with active behavioral augmentations.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD2.social

Super-Resolution in RSS-based Direction-of-Arrival Estimation - Nowak, Thorsten; Hartmann, Markus; Thielecke, Jörn; Hadachik, Niels; Mutschler, Christopher (2018)
For the evolving Internet-of-Things ubiquitous posi- tioning is a core feature. Hence, energy- and location-awareness are essential properties of wireless sensor networks (WSNs). In terms low power consumption received signal strength (RSS)- based localization techniques outperform timing-based local- ization approaches. Therefore, RSS-based direction finding is prospective approach to location-aware, low-power sensor nodes. However, RSS-based direction-of-arrival (DOA) estimation is prone to multipath propagation. In this paper, a subspace-based approach to frequency-domain multipath resolution is presented. Resolution of multipath components allows for a RSS-based DOA estimation considering the power of the line of sight (LOS) component only. The impact of the multipath channel is considerably reduced with our approach. In contrast to common broadband DOA estimation techniques, the presented approach does not need phase-coherent receive channels or a synchronized sensor network. Hence, the proposed super-resolution technique is applicable to low-power sensor networks and brings accurate positioning to small-sized and energy-efficient sensor nodes.

Supervised Learning for Yaw Orientation Estimation - Feigl, Tobias; Mutschler, Christopher; Philippsen, Michael (Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018), 2018)
With free movement and multi-user capabilities, there is demand to open up Virtual Reality (VR) for large spaces. However, the cost of accurate camera-based tracking grows with the size of the space and the number of users. No-pose (NP) tracking is cheaper, but so far it cannot accurately and stably estimate the yaw orientation of the user’s head in the long-run. Our novel yaw orientation estimation combines a single inertial sensor located at the human’s head with inaccurate positional tracking. We exploit that humans tend to walk in their viewing direction and that they also tolerate some orientation drift. We classify head and body motion and estimate heading drift to enable low-cost long-time stable head orientation in NP tracking on 100 m × 100 m. Our evaluation shows that we estimate heading reasonably well.

The DEBS 2013 Grand Challenge - Mutschler, Christopher; Ziekow, Holger; Jerzak, Zbigniew (Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems (DEBS 2013), 2013)
The ACM DEBS 2013 Grand Challenge is the third in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The goal of the Grand Challenge competition is to implement a solution to a real-world problem provided by the Grand Challenge organizers. The 2013 edition of the Grand Challenge focuses on real-time, event-based sports analytics. The 2013 Grand Challenge data set was collected during a football match carried out at a Nuremberg Stadium in Germany and is complemented with a set of continuous analytical queries which provide detailed insight into the match statistics for both team managers and spectators. Copyright © 2013 ACM.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD2.thedeb
Keywords: event processing; streaming; cep

The OnHW Dataset: Online Handwriting Recognition from IMU-Enhanced Ballpoint Pens with Machine Learning - Ott, Felix; Wehbi, Mohamad; Hamann, Tim; Barth, Jens; Eskofier, Björn; Mutschler, Christopher (Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020)
This paper presents a handwriting recognition (HWR) system that deals with online character recognition in real-time. Our sensor-enhanced ballpoint pen delivers sensor data streams from triaxial acceleration, gyroscope, magnetometer and force signals at 100 Hz. As most existing datasets do not meet the requirements of online handwriting recognition and as they have been collected using specific equipment under constrained conditions, we propose a novel online handwriting dataset acquired from 119 writers consisting of 31,275 uppercase and lowercase English alphabet character recordings (52 classes) as part of the UbiComp 2020 Time Series Classification Challenge. Our novel OnHW-chars dataset allows for the evaluations of uppercase, lowercase and combined classification tasks, on both writer-dependent (WD) and writer-independent (WI) classes and we show that properly tuned machine learning pipelines as well as deep learning classifiers (such as CNNs, LSTMs, and BiLSTMs) yield accuracies up to 90 % for the WD task and 83 % for the WI task for uppercase characters. Our baseline implementations together with the rich and publicly available OnHW dataset serve as a baseline for future research in that area.<br

Towards a Distributed Self-Optimizing Event Processing System for Realtime Locating Systems (RTLS) - Mutschler, Christopher; Philippsen, Michael (DEBS PhD Workshops, 6th ACM International Conference on Distributed Event-Based Systems, 2012)
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.IMMD2.toward
Keywords: distributed event processing; publish/subscribe; middleware; fault-tolerance; mobility; self-management; sensor streams

Trajectory Behavior Language - Edelhäußer, Thorsten; Philippsen, Michael; Mutschler, Christopher (Proceedings of the 2nd International Conference on Positioning and Context-Awareness (PoCA 2011), 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD2.trajec

Uncertainty-Based Fingerprinting Model Monitoring for Radio Localization - Stahlke, Maximilian; Feigl, Tobias; Kram, Sebastian; Eskofier, Björn; Mutschler, Christopher (IEEE Journal of Indoor and Seamless Positioning and Navigation, 2024)
Indoor radio environments often consist of areas with mixed propagation conditions. In line-of-sight (LoS)-dominated areas, classic time-of-flight (ToF) methods reliably return accurate positions, while in nonline-of-sight (NLoS) dominated areas (AI-based) fingerprinting methods are required. However, fingerprinting methods are only cost-efficient if they are used exclusively in NLoS-dominated areas due to their expensive life cycle management. Systems that are both accurate and cost-efficient in LoS- and NLoS-dominated areas require identification of those areas to select the optimal localization method. To enable a reliable and robust life cycle management of fingerprinting, we must identify altered fingerprints to trigger update processes. In this article, we propose methods for uncertainty estimation of AI-based fingerprinting to determine its spatial boundaries and validity. Our experiments show that we can successfully identify spatial boundaries of the fingerprinting models and detect corrupted areas. In contrast to the state-of-the-art, our approach employs an intrinsic identification through out-of-distribution (OOD) detection, rendering external detection approaches unnecessary.<br

Uncertainty-based Fingerprinting Model Selection for Radio Localization - Stahlke, Maximilian; Feigl, Tobias; Kram, Sebastian; Eskofier, Björn; Mutschler, Christopher (Proc. 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023), 2023)
Indoor radio environments often consist of areas with mixed propagation conditions. In LoS-dominated areas, classic ToF methods reliably return optimal (accurate) positions, while in NLoS-dominated areas (AI-based) fingerprinting methods are required. However, these fingerprinting methods are only cost-efficient if they are used exclusively in NLoS-dominated areas due to an expensive life cycle management. Systems that are both accurate and cost-efficient in LoS- and NLoS-dominated areas require an identification of those areas to select the optimal localization method. In this paper we propose methods for uncertainty estimation of AI-based fingerprinting to determine its validity. Our experiments show that we can implicitly switch between classic and fingerprinting-based approaches to reliably estimate accurate positions, even in NLoS-dominated radio environments. Our approach works even if the AI models are only trained on radio data in certain areas of the environment. In contrast to the state-of-the-art, our approach intrinsically identifies the spatial boundaries of the AI model, and thus does not require prior area identification.<br
Keywords: Hybrid localization; Fingerprinting; UWB Lo- calization; Uncertainty Quantification

Virtual and augmented reality in sports-an overview and acceptance study - Gradl, Stefan; Eskofier, Björn; Eskofier, Dominc; Mutschler, Christopher; Otto, Stephan (Proc. of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'16), 2016)
The interest in virtual and augmented reality exploded during the last two years. We propose the use of these systems in the field of sports by combining this technology with a local area radio-based localization technology. This allows for novel application scenarios using virtual environments in team-based sports, which are outlined in this work. We conducted an online survey among 227 athletes about the acceptance of virtual reality headsets for training in different kind of sport disciplines.
Keywords: Augmented Reality; Head-Mounted Displays; Radio-based localization; Sport; Virtual Reality; Wearable Devices