sEMG-Based Trunk Compensation Detection in Rehabilitation Training

Abstract

Stroke patients often use trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, which results in a reduced rehabilitation training effect. Detecting trunk compensations can improve the effect of rehabilitation training. This study investigates the feasibility of a surface electromyography-based trunk compensation detection (sEMG-bTCD) method. Five healthy subjects and nine stroke subjects with cognitive and comprehension skills were recruited to participate in the experiments. The sEMG signals from nine superficial trunk muscles were collected during three rehabilitation training tasks (reach-forward-back, reach-side-to-side, and reach-up-to-down motions) without compensation and with three common trunk compensations [lean-forward (LF), trunk rotation (TR), and shoulder elevation (SE)]. Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value (MAV), waveform length, and the fourth order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent TCD performance was achieved in healthy participants by using support vector machine (SVM) classifier (LF: accuracy = 94.0%, AUC = 0.97, F1 = 0.94; TR: accuracy = 95.8%, AUC = 0.99, F1 = 0.96; SE: accuracy = 100.0%, AUC = 1.00, F1 = 1.00). By using SVM classifier, TCD performance in stroke participants was also obtained (LF: accuracy = 74.8%, AUC = 0.90, F1 = 0.73; TR: accuracy = 67.1%, AUC = 0.85, F1 = 0.71; SE: accuracy = 91.3%, AUC = 0.98, F1 = 0.90). Compared with the methods based on cameras or inertial sensors, better detection performance was obtained in both healthy and stroke participants. The results demonstrated the feasibility of the sEMG-bTCD method, and it helps to prompt the stroke patients to correct their incorrect posture, thereby improving the effectiveness of rehabilitation training.

A Novel Quantitative Spasticity Evaluation Method Based on Surface Electromyogram Signals and Adaptive Neuro Fuzzy Inference System

“…After feature extraction, with a server configured with 48 GB graphics card and 128 GB of running memory, the mean processing delays of 1-2-3-4-5-6 ANFIS model were about 8, 10, 11, 15, 15, and 16 s, respectively. In addition, the study shows that sEMG signals can be used for compensation detection (Ma et al, 2019) and rehabilitation robot control (Koh et al, 2017). As the sEMG-ANFIS method was based on a 128 ms window, it can be combined with a compensation detection method to monitor the spasticity state and compensation pattern in real time and as feedback control in the application of a rehabilitation robot.…”

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

“…Physiological signal sensing technologies include electromyogram (8/72, 11% studies), electroencephalogram (EEG) [ 33 , 80 ], and fMRI [ 71 ] systems. According to the reviewed studies, sEMG signals of upper limb muscles (including, but not limited to, biceps, triceps, upper trapezius, pectoralis major, brachioradialis, anterior, middle, and posterior deltoids) and trunk muscles (left or right rectus abdominis, left or right obliquus externus abdominis, left or right thoracic erector spinae, left or right lumbar erector spinae, and descending part of the trapezius) not only helped to discriminate true recovery and compensation [ 29 , 30 , 33 , 84 ] but also could be used as features for automatic compensation detection [ 39 , 53 , 77 ]. Chen et al [ 77 ] confirmed that using a generative adversarial network with sEMG signals as features could achieve excellent detection performance (accuracy=94.58%, +1.15% to –1.15%) of trunk compensatory movements.…”

Network of Embedded Systems for Motor Rehabilitation Post-Stroke of Upper Limbs using ESP8266 and ESPNOW protocol

“…Stroke is considered to be one of the main causes of disability in the world Burton et al (2018) and about 80 % of stroke patients have upper limb motor dysfunction Ma et al (2019). That is why it is so important to find ways to assist in the rehabilitation process of these patients.…”