Can HCI Lead the Way? Workshop on Exploring Conscious AI - Moradi, Hamid; Flaucher, Madeleine; Ott, Tabea; Ringgold, Veronika; Eskofier, Björn; Leutheuser, Heike (2024)
The discourse of consciousness in AI is as old as the field of artificial intelligence itself. With the substantial progress of AI in recent years and emerging models, the conversation about conscious AI is becoming even more crucial. Although theories try to define consciousness, there is no reliable common ground among scientists. In this workshop, we want to take a different approach by presenting the issue to the HCI community as the connecting point between human interaction and AI models. We want to collect information on how participants perceive a conscious AI, discuss methods to measure consciousness in these systems, and consider how to ethically interact with a conscious AI. We believe that this can be a starting point for opening up the discussion among people who are practically more involved with AI.<br
Keywords: Consciousness; Artificial Intelligence; Artificial General Intelligence; Strong AI; Ethics in AI

Detection of distorted gait and wearing-off phenomenon in Parkinson's disease patients during Levodopa therapy - Moradi, Hamid; Roth, Nils; Seifer, Ann-Kristin; Eskofier, Björn (2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22), 2022)
CRIS-Team WoS Importer:2023-01-27

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors - Seifer, Ann-Kristin; Dorschky, Eva; Küderle, Arne; Moradi, Hamid; Hannemann, Ronny; Eskofier, Björn (Sensors, 2023)
Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid’s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.
Keywords: earables; gait analysis; gait event detection; inertial sensor; SSA; wearables

Gaitmap – An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking - Küderle, Arne; Ullrich, Martin; Roth, Nils; Ollenschläger, Malte; Ibrahim, Alzhraa; Moradi, Hamid; Richer, Robert; Seifer, Ann-Kristin; Zürl, Matthias; Simpetru, Raul; Herzer, Liv; Prossel, Dominik; et al. (IEEE Open Journal of Engineering in Medicine and Biology, 2024)
Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.<br
Keywords: Pipelines; Benchmark testing; Software algorithms; Ecosystems; Machine learning algorithms; Estimation; Trajectory; Accelerometer; Walking; Biomarker; Biomechanics; Movement Analysis

Monitoring medication optimization in patients with Parkinson’s disease - Moradi, Hamid; Hannink, Julius; Stallforth, Sabine; Gladow, Till; Ringbauer, Stefan; Mayr, Martin; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023)
Medication optimization is a common component of the treatment strategy in patients with Parkinson’s disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient’s onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient’s need. Additionally, they help to observe the patient’s response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson’s disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal medication.Clinical relevance— Our proposed gait analysis method in this study provides objective, detailed, and punctual information to physicians. Revealing clinically relevant time points related to the patient’s need for medical adaption alleviates therapy optimization for physicians and reduces the duration of suboptimal treatment for patients. As the home-monitoring system acts remotely, embedding it in the medical care pathways could improve patients’ quality of life.<br

Step Length and Gait Speed Estimation using A Hearing Aid Integrated Accelerometer: A Comparison of Different Algorithms - Seifer, Ann-Kristin; Kuderle, Arne; Dorschky, Eva; Moradi, Hamid; Hannemann, Ronny; Eskofier, Björn (IEEE Journal of Biomedical and Health Informatics, 2024)
Gait is an indicator of a person's health status and abnormal gait patterns are associated with a higher risk of falls, dementia, and mental health disorders. Wearable sensors facilitate long-term assessment of walking in the user's home environment. Earables, wearable sensors that are worn at the ear, are gaining popularity for digital health assessments because they are unobtrusive and can easily be integrated into the user's daily routine, for example, in hearing aids. A comprehensive gait analysis pipeline for an ear-worn accelerometer that includes spatial-temporal parameters is currently not existing. Therefore, we propose and compare three algorithmic approaches to estimate step length and gait speed based on ear-worn accelerometer data: a biomechanical model, feature-based machine learning (ML) models, and a convolutional neural network. We evaluated their performance on a step and walking bout level and compared it with an optical motion capture system. The feature-based ML model achieved the best performance with a precision of 4.8 cm on a walking bout level. For gait speed, the machine learning approach achieved an absolute percentage error of 5.4% (4.0%). We find that the ML model is able to estimate step length and gait speed with clinically relevant precision. Furthermore, the model is insensitive to different age groups and sampling rates but sensitive to walking speed. To our knowledge, this work is the first contribution to estimating step length and gait speed using ear-worn accelerometers. Moreover, it lays the foundation for a comprehensive gait analysis framework for ear-worn sensors enabling continuous and long-term monitoring at home.
CRIS-Team Scopus Importer:2024-09-20
Keywords: Accelerometer; Artificial Intelligence; Earables; Gait Speed; Hearing Aid; IMU; Step Length