Stroke patients with Upper Limb Disability (ULD) are re-acclimated to their lost motor capability through therapeutic interventions, following assessment by Physiotherapists (PTs) using various qualitative assessment protocols. However, the assessments are often biased and prone to errors. Real-time visualization and quantitative analysis of various Performance Metrics (PMs) of patient's motion data, such as - Range of Motion (RoM), Repetition Rate (RR), Velocity (V), etc., may be vital for proper assessment. In this study, we present Renovo, a wearable inertial sensor-based therapeutic system, which assists PTs with real-time visualization and quantitative patient assessment, while providing patients with progress feedback. We showcase the results of a three-week pilot study on the rehabilitation of ULD patients (N=16), in 3 successive sessions at one-week interval, following evaluation both by Renovo and PTs (N=5). Results suggest that sensor-based quantitative assessment reduces the possibility of human error and bias, enhancing efficiency of rehabilitation.
翻译:通过治疗干预措施,在物理治疗师(PTs)利用各种定性评估程序进行评估后,上林病患者通过治疗干预重新适应丧失的运动能力,但评估往往有偏向,容易出错;对患者运动数据的各种性能计量(PM)实时进行可视化和定量分析,如-运动范围(ROM)、复发率(RRR)、速度(V)等,对适当评估可能至关重要;在本研究中,我们介绍了可磨旧惯性传感器治疗系统Renovo,该系统协助PTs进行实时可视化和定量病人评估,同时向病人提供进展反馈;我们连续三次在一周内,在Renovo和PTs(N=5)评价后,连续三次就ULD病人的康复情况进行为期三周的试点研究(N=16),结果显示,基于传感器的定量评估减少了人类错误和偏见的可能性,提高了康复的效率。