Compensatory movement detection by using near-infrared spectroscopy technology based on signal improvement method.

Introduction: Compensatory movements usually occur in stroke survivors with hemiplegia, which is detrimental to recovery. This paper proposes a compensatory movement detection method based on near-infrared spectroscopy (NIRS) technology and verifies its feasibility using a machine learning algorithm. We present a differential-based signal improvement (DBSI) method to enhance NIRS signal quality and discuss its effect on improving detection performance. Method: Ten healthy subjects and six stroke survivors performed three common rehabilitation training tasks while the activation of six trunk muscles was recorded using NIRS sensors. After data preprocessing, DBSI was applied to the NIRS signals, and two time-domain features (mean and variance) were extracted. An SVM algorithm was used to test the effect of the NIRS signal on compensatory behavior detection. Results: Classification results show that NIRS signals have good performance in compensatory detection, with accuracy rates of 97.76% in healthy subjects and 97.95% in stroke survivors. After using the DBSI method, the accuracy improved to 98.52% and 99.47%, respectively. Discussion: Compared with other compensatory motion detection methods, our proposed method based on NIRS technology has better classification performance. The study highlights the potential of NIRS technology for improving stroke rehabilitation and warrants further investigation.

LiDAR-Based Hand Contralateral Controlled Functional Electrical Stimulation System.

Contralateral controlled functional electrical stimulation (CCFES) can induce simultaneous movements in patients' bilateral hands. It has been clinically proven to be effective in improving hand motor control and dexterity. sEMG and bending sensor-based data gloves for detecting patients' motor intent have been developed with limitations. sEMG sensor signals are unstable and susceptible to noise. Data gloves composed of bending sensors require complicated calibration and tend to have data drift. In this paper, a LiDAR-based system for hand CCFES is proposed. The method utilized LiDAR to detect the patient's motion intention without contact in CCFES systems. It has been clinically proven that LiDARs can effectively distinguish the different motion amplitudes of hand gestures as quantitative evaluation sensors of functional electrical stimulation (FES). Training data for classifiers were collected from 9 healthy individuals and 15 stroke patients performing 4 gestures, including hand opening, fist clenching, wrist extension, and wrist flexion. The support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (kNN) were verified for their classification performance in offline hand gesture recognition tests. Experiments were also conducted on 6 stroke volunteers to evaluate gestures triggered by FES. The SVM classifier showed excellent classification performance for four hand gestures, with an average F1-score of 0.97 ± 0.05 in offline tests. As for online gesture recognition, an average F1-score of 0.92 ± 0.09 was obtained. In the evaluation experiments, between data from 50% and 100% movement amplitude, paired t-tests showed significant differences. The experimental results indicated that the proposed system showed promise for hand rehabilitation.

Design and Testing of a Dynamic Orthosis to Reduce Glenohumeral Subluxation With Omnidirectional Shoulder Motion.

OBJECTIVE: This paper aimed to develop an orthosis to apply a compensating force to improve the stability of the glenohumeral joint without resisting arm movement. METHODS: The proposed orthosis was based on a parallelogram structure to provide a pair of compensating forces to the glenohumeral joint center. Theoretical analysis was used to evaluate the additional moments caused by glenohumeral joint center shifting. Then, an experimental evaluation platform, composed of a torque sensor, a force sensor, and a 3D printed arm, was set up to assess the additional moments and compensating force. Finally, the proposed orthosis was compared with the traditional orthosis to compare the subluxation reduction and the movement restriction when worn by stroke patients. RESULTS: There was only a maximum additional moment of 0.87 Nm for the glenohumeral center shifting. During 3D printed arm movement, the moment correlation coefficient between with and without the proposed orthosis was greater than 0.98, and the compensating force was larger than 90% of the arm weight. The proposed orthosis reduced subluxation by 12.5±3.5 mm, and the traditional orthosis reduced subluxation by 7.7±2.2 mm, indicating that the subluxation reduction of the proposed orthosis was more effective ( ). Meanwhile, the proposed orthosis's motion restriction joint was significantly smaller than traditional orthosis ( ). CONCLUSION: The proposed orthosis provided sufficient gravity compensation without resisting arm movement. SIGNIFICANCE: The proposed orthosis can improve the shoulder's stability during shoulder movement, potentially improving the rehabilitation effect of patients with shoulder subluxation.