3-D Reconstruction of Historical Documents using an X-Ray C-Arm CT System - Stromer, Daniel; Christlein, Vincent; Anton, Gisela; Kugler, Patrick; Maier, Andreas (Proceedings of the 31th conference on Image and Vision Computing New Zealand 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.3dreco_2
Keywords: X-Ray imaging; Iron gall ink reconstruction; Historical Document Analysis

A data collection and communication module for telemedicine and mhealth systems - Blank, Peter; Kugler, Patrick; Schuldhaus, Dominik; Eskofier, Björn (Proceedings of the 9th International Conference on Body Area Networks, 2014)
Wearable body sensors have become an important basis for today's medical and fitness applications. To assist athletes or to take care of elderly people in everyday life situations, sensor data can be collected and processed to give helpful feedback. However, the data collection process of multiple or different sensor systems still had to be done manually by the user or an expert, which usually takes a lot of time and can lead to errors. This paper presents an embedded data collection and communication module based on an AT90USB microcontroller, which can automatically acquire data from various wired and wireless sensors (e.g. via USB or Bluetooth® ). The obtained data can be cached, preprocessed and transmitted to a specified central server using LAN, Wi-Fi or GMS/GPRS. Through unified expansion slots, additional communication devices and a BeagleBoneTM embedded can be extended to handle many different sensor systems. Moreover, wired sensors can be charged through appropriate circuits. This data collection and communication module operates without any input settings and special knowledge by the user. Five prototypes with different configurations and extension units concerning communication interfaces and computation power have been built up. The evaluation of the transfer reliability with 100%, whereupon 98% of data could be transmitted at once and the remaining 2% with the next attempt, confirms the stability of data transmission.
UnivIS-Import:2016-06-01:Pub.2014.tech.IMMD.IMMD5.adatac
Keywords: Autonomous; BeagleBone; Data acquisition and transmission; Embedded computing; GSM/GPRS; mHealth; Microcontroller; Telemedicine; USB; Wearable sensors

Approaching the accuracy-cost conflict in embedded classification system design - Jensen, Ulf; Kugler, Patrick; Ring, Matthias; Eskofier, Björn (Pattern Analysis and Applications, 2016)
Smart embedded systems often run sophisticated pattern recognition algorithms and are found in many areas like automotive, sports and medicine. The developer of such a system is often confronted with the accuracy–cost conflict as the resulting system should be as accurate as possible while being able to run on resource constraint hardware. This article introduces a method to support the solution of this design conflict with accuracy–cost reports. These reports compare classification systems regarding their classification rate (accuracy) and the mathematical operations and parameters of the working phase (cost). Our method is used to deduce the specific cost of various popular pattern recognition algorithms and to derive the overall cost of a classification system. We also show how our analysis can be used to estimate the computational cost for specific hardware architectures. A software toolbox to create accuracy–cost reports was implemented to facilitate the automatic classification system comparison with the presented methodology. The software is available for download and as supplementary material. We performed different experiments on synthetic and real-world data to underline the value of this analysis. Accurate and computationally cheap classification systems were easily identified. We were even able to find a better implementation candidate in an existing embedded classification problem. This work is the first step towards a comprehensive support tool for the design of embedded classification systems.
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.approa
Keywords: Machine learning; Real-time systems; Cost estimation; Classification system design

Automatic Recognition of Parkinsons Disease Using Surface Electromyography During Standardized Gait Tests - Kugler, Patrick; Jaremenko, Christian; Schlachetzki, Johannes; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 2013)
Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for P

A Wearable Sensor System for Sports and Fitness Applications - Blank, Peter; Kugler, Patrick; Schlarb, Heiko; Eskofier, Björn (Book of Abstracts of the 19th Annual Congress of the European College of Sport Science, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.aweara

Biometric and Mobile Gait Analysis for Early Detection and Therapy Monitoring in Parkinson's Disease - Barth, Jens; Klucken, Jochen; Kugler, Patrick; Kammerer, Thomas; Steidl, Ralph; Winkler, Jürgen; Hornegger, Joachim; Eskofier, Björn (Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE, 2011)
Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists. © 2011 IEEE.

Classification of Surfaces and Inclinations During Outdoor Running Using Shoe-Mounted Inertial Sensors - Schuldhaus, Dominik; Kugler, Patrick; Leible, Magnus; Jensen, Ulf; Schlarb, Heiko; Eskofier, Björn (Proceedings of 21st International Conference on Pattern Recognition, 2012)
Embedded mobile systems for analysis and classificationbecome more and more important in the field ofsports and sports science. Small and lightweight sensorsin sportswear offer the possibility to monitor theathletes in a realistic environment, e.g. during an outdoorrun. During the activity, the sportswear can automaticallyadapt to the current environment and henceoptimizes the performance of the athlete. A major needis a running shoe, which can automatically be adaptedto the current ground.In this paper, a classification system was developed,which distinguished between different surfaces and inclinationsbased on inertial sensors. They were placedon the heel of a running shoe and acquired kinematicdata of 21 subjects. For each subject, several roundsof an one hour outdoor run were available and wereused for the evaluation of the system. The classificationsystem reached a mean classification rate of more than80 %.

Comparison of Real-Time Classification Systems for Arrhythmia Detection on Android-based Mobile Devices - Leutheuser, Heike; Gradl, Stefan; Kugler, Patrick; Anneken, Lars; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (IEEE EMBC 2014, 2014)

Comparison of SART and eTV Reconstruction for increased C-arm CT Volume Coverage by proper Detector Rotation in Liver Imaging - Stromer, Daniel; Amrehn, Mario; Huang, Yixing; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this work, we present a method to increase the lateral field-of-view of a C-arm CT system by rotating the detector such that the diagonal of the detector lies on the u-axis of the detector's coordinate system. We investigated three different 3-D scan trajectories for liver imaging of an obese patient (waist circumference 130 cm) — a Short Scan, a Large Volume Scan and a Helical Large Volume Scan. We reconstructed a data set of the Visible Human Project with the SART and the eTV algorithm. Tests revealed that the coverage was increased with the presented method by 25.3 % for the Short Scan and 28.5 % for the Large Volume Scan. Performing helical scans compensated the axial data loss. The two implemented iterative approaches both provide acceptable results, with the eTV algorithm reducing the RMSE compared to SART by about 29 %. Given a liver imaging task, the rotated detector is able to image the entire liver section of the abdomen with a single Large Volume Sca
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.compar_1
Keywords: Diamond Scan; eTV; Field-of-View Enlargement; Iterative Reconstruction; iTV; SART

Data Completeness Estimation for 3D C-Arm Scans with Rotated Detector to Enlarge the Lateral Field-of-View - Stromer, Daniel; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.dataco_2
Keywords: Field-of-View Enlargement; Detector Rotation

Discrete Estimation of Data Completeness for 3D Scan Trajectories with Detector Offset - Maier, Andreas; Kugler, Patrick; Lauritsch, Günter; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.discre_2
Keywords: Data Completeness; Computed Tomography; Tuy's Condition

ER-Force Team Description Paper for RoboCup 2009 - Blank, Peter; Bleier, Michael; Drexler, Sebastian; Kallwies, Jan; Kugler, Patrick; Lahmann, Dominik; Nordhus, Philipp; Riess, Christian; Swadzba, Thaddäus; Tully, Jan (RoboCup 2009: Robot Soccer World Cup XIII Proceedings, 2009)

ER-Force Team Description Paper for RoboCup 2010 - Blank, Peter; Bleier, Michael; Kallwies, Jan; Kugler, Patrick; Lahmann, Dominik; Nordhus, Philipp; Riess, Christian (RoboCup 2010: Robot Soccer World Cup XIV Proceedings, 2010)

ER-Force Team Description Paper for RoboCup 2011 - Bauer, Florian; Blank, Peter; Bleier, Michael; Dohrn, Hannes; Eischer, Michael; Friedrich, Stefan; Hauck, Adrian; Kallwies, Jan; Kugler, Patrick; Lahmann, Dominik; Nordhus, Philipp; Reck, Benjamin; et al. (RoboCup 2011: Robot Soccer World Cup XV Proceedings, 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.erforc

Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors - Jakob, Carolin; Kugler, Patrick; Hebenstreit, Felix; Reinfelder, Samuel; Jensen, Ulf; Schuldhaus, Dominik; Lochmann, Matthias; Eskofier, Björn (BodyNets 2013, 2013)

Feedback-Training mit tragbaren Sensor-Netzwerken - Kugler, Patrick; Jensen, Ulf; Eskofier, Björn; Hornegger, Joachim (INFORMATIK 2010 Service Science - Neue Perspektiven für die Informatik - Band 1, 2010)
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD5.feedba
Keywords: feedback training; inertial sensors; digital sports

Feedback training with portable sensor networks - Kugler, Patrick; Jensen, Ulf; Eskofier, Björn; Hornegger, Joachim (40th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Service Science - Neue Perspektiven fur die Informatik, INFORMATIK 2010, 2010)

Measurement of angles in time-of-flight data for the automatic supervision of training exercises - Soutschek, Stefan; Kornhuber, Johannes; Maier, Andreas; Bauer, Sebastian; Kugler, Patrick; Hornegger, Joachim; Bebenek, Michael; Steckmann, Sven; von Stengel, Simon; Kemmler, Wolfgang (2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health 2010, 2010)
In this paper we present a novel automatic system to supervise and support rehabilitation and fitness exercises. Goal of the system is to enable autonomous training of rehabilitation patients to support their recovery from movement restrictions as well as the prevention of age-related diseases such as osteoporosis. The system uses time-of-flight technology in order to measure movement angles in real-time. This novel setup is compared to a standard tracking system using visual markers. The measurements of both systems correlate with r values between 0.99 and 0.97 depending on the task domain. The mean error of the angle measurements is between 13 and 17 degrees.

miPod - A Wearable Sports and Fitness Sensor - Blank, Peter; Kugler, Patrick; Eskofier, Björn (10. Symposium der dvs Sportinformatik, 2014)
UnivIS-Import:2016-06-01:Pub.2014.tech.IMMD.IMMD5.mipoda_3

Mobile biometrische Ganganalyse - Klucken, Jochen; Barth, Jens; Maertens, Katharina; Eskofier, Björn; Kugler, Patrick; Steidl, Ralph; Hornegger, Joachim; Winkler, Jürgen (Nervenarzt, 2011)

Mobile biosensor-based gait analysis: A diagnostic and therapeutic tool in Parkinson's disease - Klucken, Jochen; Barth, Jens; Maertens, Katharina; Eskofier, Björn; Kugler, Patrick; Steidl, Ralph; Hornegger, Joachim; Winkler, Jürgen; Klucken, Jochen (Nervenarzt, 2011)
Summary: Parkinson's disease (PD) is characterized by progressive motor and non-motor symptoms, leading to distinct diagnostic and therapeutic challenges in all stages of the disease. This study investigated a mobile biosensor-based gait analysis system for patients in early and intermediate stages of PD compared to controls. Subjects wearing a motion sensor-equipped shoe performed a standardized gait exercise. Accelerometer-and gyroscope-based signals were analysed using a complex set of pattern recognition algorithms. The analysis was able (1) to distinguish between PD patients and controls, (2) to identify patients at an early stage of the disease and (3) to distinguish between early and intermediate stage patients. Thus, using this mobile biosensor-based analysis system we were able to obtain objective classifications of gait characteristics in PD. Future studies will show that mobile biosensor-based movement detection technology will support identification of early PD stages and allow objective characterization of motor fluctuations in advanced stages of the disease. This will provide an important and supportive tool for patients, caregivers and therapists. © 2011 Springer-Verlag.

Monitoring Subjective Perception and Physiological State of Athletes or Patients in Real-Time using a Mobile Phone - Kugler, Patrick; Bührle, Elmar; Eskofier, Björn; Kühner, Pascal; Schlarb, Heiko; Hornegger, Joachim (Proceedings of the Embedded World Conference 2010, 2010)

Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices - Gradl, Stefan; Kugler, Patrick; Lohmüller, Clemens; Eskofier, Björn (Engineering in Medicine and Biology Society, EMBC, 2012, 2012)
We developed an application for Android™-based mobile devices that allows real-time electrocardiogram (ECG) monitoring and automated arrhythmia detection by analyzing ECG parameters. ECG data provided by pre-recorded files or acquired live by accessing a Shimmer™ sensor node via Bluetooth™ can be processed and evaluated. The application is based on the Pan-Tompkins algorithm for QRS-detection and contains further algorithm blocks to detect abnormal heartbeats. The algorithm was validated using the MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia databases. More than 99% of all QRS complexes were detected correctly by the algorithm. Overall sensitivity for abnormal beat detection was 89.5% with a specificity of 80.6%. The application is available for download and may be used for real-time ECG-monitoring on mobile devices. © 2012 IEE

Schlafstadienbestimmung mit Hilfe bewegungsbasierter Sensortechnologie - Traxdorf, Maximilian; Gradl, Stefan; Kugler, Patrick; Leutheuser, Heike; Lauten, Juliane; Eskofier, Björn; Angerer, Florian; Iro, Heinrich (Proceedings of the 84th Annual Meeting of the German Society of Oto-Rhino-Laryngology, Head and Neck Surgery, 2013)

Software-based Performance and Complexity Analysis for the Design of Embedded Classification Systems - Ring, Matthias; Jensen, Ulf; Kugler, Patrick; Eskofier, Björn (Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR), 2012)
Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true for example for the fields of sports, automotive and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources. In this paper, we present a solution to the two main challenges, namely obtaining a classification system with low computational complexity and at the same time high classification accuracy. For the first challenge, we propose complexity measures on the mathematical operation and parameter level, because the abstraction level of the commonly used Landau notation is too high in the context of embedded system implementation. For the second challenge, we present a software toolbox that trains different classification systems, compares their classification rate, and finally analyzes the complexity of the trained system. To give an impression of the importance of such complexity measures when dealing with limited hardware resources, we present the example analysis of the popular Pima Indians Diabetes data set, where considerable complexity differences between classification systems were revealed.

Somnography using unobtrusive motion sensors and Android-based mobile phones - Gradl, Stefan; Leutheuser, Heike; Kugler, Patrick; Biermann, Teresa; Kreil, Sebastian; Kornhuber, Johannes; Bergner, Matthias; Eskofier, Björn (IEEE EMBC 2013, 2013)
Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50%of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. © 2013 IEEE.

Stride Segmentation During Free Walk Movements Using Multi-dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data - Barth, Jens; Oberndorfer, Cäcilia; Pasluosta, Cristian Federico; Schülein, Samuel; Gaßner, Heiko; Reinfelder, Samuel; Kugler, Patrick; Schuldhaus, Dominik; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (Sensors, 2015)
UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.stride
Keywords: inertial sensors; stride segmentation; accelerometer; gyroscope; dynamic time warping; free walk; gait analysis; Parkinsons disease; geriatric patients; movement impairments

Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease - Klucken, Jochen; Barth, Jens; Kugler, Patrick; Schlachetzki, Johannes; Henze, Thore; Marxreiter, Franz; Kohl, Zacharias; Steidl, Ralph; Hornegger, Joachim; Eskofier, Björn; Winkler, Jürgen (PLoS ONE, 2013)
Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease. © 2013 Klucken et al.
EVALuna2:25318

Unobtrusive and Energy-Efficient Swimming Exercise Tracking Using On-Node Processing - Jensen, Ulf; Blank, Peter; Kugler, Patrick; Eskofier, Björn (IEEE Sensors Journal, 2016)
UnivIS-Import:2016-06-27:Pub.2016.tech.IMMD.IMMD5.unobtr
Keywords: inertial measurement unit; swimming exercise tracking; on-node processing; wearable computing; body sensor network

Visualization of Changes in Muscular Activation during Barefoot and Shod Running - Kugler, Patrick; von Tscharner, Vinzenz; Eskofier, Björn; Hornegger, Joachim (Proceeding of 17th Congress of the European Society of Biomechanics, 2010)