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Test and Validation of a Smart Exercise Bike for Motor Rehabilitation in Individuals with Parkinsons Disease

机译:帕金森氏病患者运动康复智能健身车的测试与验证

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摘要

To assess and validate the Smart Exercise Bike designed for Parkinson's Disease (PD) rehabilitation, forty-seven individuals with PD were randomly assigned to either the static or dynamic cycling group, and completed three sessions of exercise. Heart rate, cadence and power data were captured and recorded for each patient during exercise. Motor function for each subject was assessed with the UPDRS Motor III test before and after the three exercise sessions to evaluate the effect of exercise on functional abilities. Individuals who completed three sessions of dynamic cycling showed an average of 13.8% improvement in the UPDRS, while individuals in the static cycling group worsened by 1.6% in UPDRS.To distinguish the static and dynamic cycling groups by biomechanical and physiological features, the complexity of the recorded signals (cadence, power, and heart rate) was examined using approximate entropy (ApEn), sample entropy (SaEn) and spectral entropy (SpEn) as measures of variability. A multiple linear regression (MLR) model was used to relate these features to changes in motor function as measured by the UPDRS Motor III scale. Pattern variability in cadence was greater in the dynamic group when compared to the static group. In contrast, variability in power was greater for the static group. UPDRS Motor III scores predicted from the pattern variability data were correlated to measured scores in both groups. These results support our previous study which explained how variability analysis results for biomechanical and physiological parameters of exercise can be used to predict improvements in motor function.
机译:为了评估和验证专为帕金森氏病(PD)康复而设计的智能运动自行车,将47名PD患者随机分配至静态或动态自行车组,并完成了三节运动。在运动过程中捕获并记录每位患者的心率,节奏和力量数据。在三个锻炼之前和之后,通过UPDRS Motor III测试评估每个受试者的运动功能,以评估运动对功能能力的影响。完成三节动态骑行的个人显示UPDRS平均改善13.8%,而静态骑行组的个人在UPDRS中恶化1.6%。通过生物力学和生理学特征区分静态和动态骑行组的复杂性使用近似熵(ApEn),样本熵(SaEn)和谱熵(SpEn)作为变化的度量来检查记录的信号(节奏,功率和心率)。使用UPDRS Motor III量表测量的多元线性回归(MLR)模型将这些特征与运动功能的变化联系起来。与静态组相比,动态组的节奏节奏变化更大。相反,静态组的功率变化较大。从模式变异性数据预测的UPDRS Motor III分数与两组的测得分数相关。这些结果支持了我们先前的研究,该研究解释了运动的生物力学和生理参数的变异性分析结果如何可以用来预测运动功能的改善。

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