Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Tianjin 300192, China.
Sensors (Basel). 2024 Jul 1;24(13):4273. doi: 10.3390/s24134273.
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 ( < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups ( > 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.
本研究旨在将卷积神经网络（CNN）和随机森林模型集成到康复评估设备中，提供全面的步态分析，以帮助医生通过区分不同行走模式下的步态特征来评估康复进展。该设备配备了加速度计和六轴力传感器，可在康复过程中监测身体对称性和上肢力量。从正常和异常行走组中收集数据。对受试者施加膝关节限制器，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）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（<0.05），而 MA 有车辆辅助（MA-V）和 SA 有车辆辅助（SA-V）组之间没有显著差异（>0.05）。力传感器数据显示正常行走组集中良好，SA-V 组分散更多。CNN 和随机森林模型准确识别了步态状况，平均准确率分别达到 88.4%和 92.3%，证明了上述方法为运动障碍患者提供了更准确的步态评估。
