Zhenyu Xu, Zijing Wu, Linlin Wang, Ziyue Ma, Juan Deng, Hong Sha, Hong Wang
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引用次数: 0
摘要
本研究旨在将卷积神经网络(CNN)和随机森林模型整合到康复评估设备中,在运动障碍评估中提供全面的步态分析,通过区分不同行走模式下的步态特征,帮助医生评估康复进展。该设备配备了加速度计和六轴力传感器,可监测康复过程中的身体对称性和上肢力量。从正常行走组和异常行走组收集数据。对受试者施加了膝关节限制器,以模拟不同程度的运动障碍。从收集的数据中提取特征,并使用 CNN 进行分析。使用随机森林模型权重对整体性能进行评分。观察到中度异常(MA)组和严重异常(SA)组(无车辆辅助)之间的平均加速度值存在显著差异(p < 0.05),而有车辆辅助的 MA 组(MA-V)和有车辆辅助的 SA 组(SA-V)之间则无显著差异(p > 0.05)。力传感器数据在正常行走组显示出良好的集中性,而在 SA-V 组则显示出更大的分散性。CNN 和随机森林模型能准确识别步态状况,平均准确率分别达到 88.4% 和 92.3%,证明上述方法能为运动障碍患者提供更准确的步态评估。
Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning
This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.
期刊介绍:
Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.