Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training

Sensors

卷:20

Marc Hofmann¹  Andreas Schicketmueller¹  Georg Rose²  Michael Sailer³  Juliane Lamprecht³ 

[1] HASOMED GmbH, Paul-Ecke-Str. 1, 39114 Magdeburg, Germany;

[2] Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg, Universitaetsplatz 2, 39106 Magdeburg, Germany;

[3] MEDIAN Neurological Rehabilitation Center Magdeburg, Gustav-Ricker-Str. 4, 39120 Magdeburg, Germany;

关键词: stroke;    inertial measurement unit (IMU);    functional electrical stimulation (FES);    gait event detection;    hybrid robotic rehabilitation system;   

DOI  :  10.3390/s20123399

来源: DOAJ

【 摘 要 】

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

【 授权许可】

Unknown