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Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.
Electrical stimulation is used for example to treat neuronal disorders and depression with deep brain stimulation or transcranial electrical stimulation. Depending on the application, different electrodes are used and thus different electrical characteristics exist, which have to be handled by the stimulator. Without a measuring device the user would have to rely on the stimulator being able to deliver the needed stimulation signal. Therefore, the objective of this paper is to present a method to increase the level of confidence with characterization and modelling of the electrical behavior by using the example of one channel of our stimulation device for experimental use. In several simulation studies with an electrode model with values in a typical range for cortical applications the influence of the load onto the stimulator and the possibility to pre-estimate measuring signals in complex networks are shown.
Deep brain stimulation (DBS) is an established therapy for movement disorders such as in Parkinson's disease (PD) and essential tremor (ET). Adjusting the stimulation parameters, however, is a labour-intensive process and often requires several patient visits. Physicians prefer objective tools to improve (or maintain) the performance in DBS. Wearable motion sensors (WMS) are able to detect some manifestations of pathological signs, such as tremor in PD. However, the interpretation of sensor data is often highly technical and methods to visualise tremor data of patients undergoing DBS in a clinical setting are lacking. This work aims to visualise the dynamics of tremor responses to DBS parameter changes with WMS while patients performing clinical hand movements. To this end, we attended DBS programming sessions of two patients with the aim to visualise certain aspects of the clinical examination. PD tremor and ET were effectively quantified by acceleration amplitude and frequency. Tremor dynamics were analysed and visualised based on setpoints, movement transitions and stability aspects. These methods have not yet been employed and examples demonstrate how tremor dynamics can be visualised with simple analysis techniques. We therefore provide a base for future research work on visualisation tools in order to assist clinicians who frequently encounter patients for DBS therapy. This could lead to benefits in terms of enhanced evaluation of treatment efficacy in the future.
For the assessment of human reaction time, a test environment was developed. This system consists of an embedded device with organic light-emitting diode (OLED) displays with push buttons for the combined presentation of visual stimulation and registration of the haptic human reaction. The test leader can define the test sequence with the aid of a graphical user interface (GUI) on a personal computer (PC). The validation of the system was proved by measuring the latency times of the whole system, which are conditioned by the specific hard- and software constellation. Through the investigation of the display’s light radiation by a photodiode and the recorded current consumption, latency times and their variance were specified. In the fastest mode the system can reach an error limit of 60 μs.