Intra- and inter-rater reliability of the Italian Fugl-Meyer assessment of upper and lower extremity

A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.

Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

Effectiveness of Proprioceptive Body Vibration Rehabilitation on Motor Function and Activities of Daily Living in Stroke Patients with Impaired Sensory Function

Stroke patients experience impaired sensory and motor functions, which impact their activities of daily living (ADL). The current study was designed to determine the best neurorehabilitation method to improve clinical outcomes, including the trunk-impairment scale (TIS), Berg balance scale (BBS), Fugl-Meyer assessment (FMA), and modified Barthel index (MBI), in stroke patients with impaired sensory function. Forty-four stroke survivors consistently underwent proprioceptive body vibration rehabilitation training (PBVT) or conventional physical therapy (CPT) for 30 min/session, 5 days a week for 8 weeks. Four clinical outcome variables-the FMA, TIS, BBS, and MBI-were examined pre- and post-intervention. We observed significant differences in the FMA, BBS, and MBI scores between the PBVT and CPT groups. PBVT and CPT showed significant improvements in FMA, BBS, TIS, and MBI scores. However, PVBT elicited more favorable results than CPT in patients with stroke and impaired sensory function. Collectively, this study provides the first clinical evidence of optimal neurorehabilitation in stroke patients with impaired sensory function.