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

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3399
Author(s):  
Andreas Schicketmueller ◽  
Juliane Lamprecht ◽  
Marc Hofmann ◽  
Michael Sailer ◽  
Georg Rose
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Gait Training ◽  
Patient Specific ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Rehabilitation Process ◽  
Type 1 Error ◽  
Movement Data ◽  
Measurement Units

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.

scholarly journals Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4804 ◽  
Author(s):  
Andreas Schicketmueller ◽  
Georg Rose ◽  
Marc Hofmann
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Event Detection ◽  
Gait Training ◽  
Detection Algorithm ◽  
Inertial Measurement Units ◽  
Robot Assisted ◽  
Rehabilitation Process ◽  
Inertial Measurement ◽  
Measurement Units

Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process.

scholarly journals Patient-specific functional electrical stimulation strategy based on muscle synergy and walking posture analysis for gait rehabilitation of stroke patients

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110167
Author(s):  
Junghwan Lim ◽  
Taehyun Lim ◽  
Jungeun Lee ◽  
Junhyuk Sim ◽  
Hyungjun Chang ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Muscle Synergy ◽  
Patient Specific ◽  
Walking Ability ◽  
Muscle Coordination ◽  
Stroke Patients ◽  
Posture Analysis ◽  
Control Subjects ◽  
Healthy Control

Objective To evaluate a novel multi-channel functional electrical stimulation (FES) rehabilitation method based on the evaluation of patient-specific walking dysfunction. Methods This study investigated a novel multi-channel FES-based rehabilitation method that analysed the patient’s muscle synergy and walking posture. A patient-specific FES profile was produced in the pre-evaluation stage by comparing the muscle synergy and walking posture of the patient with those of healthy control subjects. During the rehabilitation phase, this profile was used to determine an appropriate FES pulse width and amplitude for stimulating the patient’s muscles as they walked across a flat surface. Results Two stroke patients with hemiplegic symptoms participated in a clinical evaluation of the proposed method involving a 4-week course of rehabilitation. An evaluation of the rehabilitation results based on a comparison of the pre- and post-rehabilitation muscle synergy and walking posture revealed that the rehabilitation enhanced the muscle synergy similarity between the patients and healthy control subjects and their quantitative walking performance, as measured by a 10-m walk test and walking speed, by up to 23.38% and 30.00%, respectively. Conclusion These results indicated that the proposed rehabilitation method improved walking ability by improving muscle coordination and adequately supporting weakened muscles in stroke patients.

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