Wencke Chodan

Journal of Rehabilitation and Assistive Technologies Engineering (RATE)

Introduction: Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. Methods: In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automated algorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists. Results: There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests. Conclusions: The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings

45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020) <45, 2020, online>

Electrocardiograms constitute the key diagnostic tool for cardiologists. While their diagnostic value is yet unparalleled, electrode placement is prone to errors, and sticky electrodes pose a risk for skin irritations and may detach in long-term measurements. Heart.AI presents a fundamentally new approach, transforming motion-based seismocardiograms into electrocardiograms interpretable by cardiologists. Measurements are conducted simply by placing a sensor on the user’s chest. To generate the transformation model, we trained a convolutional autoencoder with the publicly available CEBS dataset. The transformed ECG strongly correlates with the ground truth (r=.94, p<.01), and important features (number of R-peaks, QRS-complex durations) are modeled realistically (Bland-Altman analyses, p>0.12). On a 5- point Likert scale, 15 cardiologists rated the morphological and rhythmological validity as high (4.63/5 and 4.8/5, respectively). Our electrodeless approach solves crucial problems of ECG measurements while being scalable, accessible and inexpensive. It contributes to telemedicine, especially in low-income and rural regions worldwide.

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <12, 2019, Rhodes, Greece>

ACM International Conference Proceedings Series (ICPS), 01608

Sleep state detection is important to distinguish between a healthy sleep and sleep disorders. Common sleep state analysis methods consist of identifying signals of EEG, EOG, or EMG etc. that can only be assessed in sleep laboratories. The respiration rate and pattern are also affected by the sleep states but are not included in the sleep state analysis method. Since sleep is very important for the recreation of humans, we assume that sleep is mirroring the strain of the day and the general health condition. In our research, we identified a certain respiration rate pattern during sleep in 5 out of 17 healthy persons that might be an identifier for sleep states or for interactions of daytime activity and sleep. Therefore, we introduce this new respiration pattern as “pumping breathing” and compare it with other known respiration patterns.

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <12, 2019, Rhodes, Greece>

ACM International Conference Proceedings Series (ICPS), 01608

People who suffer from difficulties in ambulating can be supported by using wheeled walking frames, also called rollators. Mechanical rollators are very helpful and provide physical stability but their functionality is limited. Electro-powered rollators can support the user whenever motor power is needed, e.g., when walking uphill or crossing the curbside of a sidewalk. The full potential of electric and smart rollators is not yet used. In this paper, we describe a new Robotic Rollator (RoRo) concept. The aim of RoRo is to guide elderly people autonomously through clinics and rehabilitation homes, e.g., to lead them to the radiology department or to the physiotherapist’s office. Furthermore, RoRo trains the elderly and examines their mobility, stability, and strength, as well as their visual-spatial and cognitive abilities. For this purpose, RoRo is equipped with additional sensors to monitor vital data of the user and to relate them to the physical load. The autonomous rollator RoRo interacts in the closed controlled indoor environments with infrared markers (that cannot be seen by humans) to allow spatial positioning. In addition to the technological aims of RoRo, another focus of the ongoing project lies on a balanced interaction between RoRo and the patient to motivate therapeutic exercises, physical activity (like going for a walk), and simple entertainment. In the future, the autonomous rollator may become a social robot that trains and accompanies the user like a personal acquaintance.

iWOAR 2019

International Workshop on Sensor-based Activity Recognition (iWOAR) <6, 2019, Rostock, Germany>

ACM International Conference Proceedings Series

Stress at work is a major cause of health problems for the employees and of costs for companies and the healthcare system. To prevent stress-related disorders, first both the stress level and the exposition to possible stressors must be known. The SEBA system assesses both and produces live data streams that are constantly and automatically evaluated. The system is a head-worn portable device. Multiple sensors assess biosignals of the users that are known to be sensitive towards the feeling of stress (e.g., pulse, eye blink rate, breathing rate, brain activity) and ambient conditions that could influence the feeling of stress (e.g., air quality, flickering lights, temperature, draft). SEBA classifies the individual stress level using a neural network and sends the processed data to a mobile application for visualization purposes. In this paper, we introduce the concept of the SEBA system, including its hardware, sensors, firmware, and software. The SEBA system is currently being development; the paper outlines the current state of development and possible obstacles.