目的:本研究旨在通过开发智能便携式设备来评估早期检测老年人疲劳步态模式的可行性。
方法:智能设备结合了七个力传感器和单个惯性测量单元(IMU),以测量区域足底力和脚运动学。收集了18名老年人在跑步机上轻快行走60分钟的数据。通过五次交叉验证,以包装方式使用前向顺序特征选择确定每个识别模型的最佳特征集。通过留一主题交叉验证从四个机器学习模型中选择识别模型。
结果:最能代表疲劳状态的五个选定特征包括内侧和外侧弓的冲动(增加,p=0.002和p<0.001),在矢状面中的接触角和角度的旋转范围(增加,p<0.001),以及由此产生的摆动角加速度的可变性(减小,p<0.001)。基于IMU和足底力双信号源的检测精度为99%,高于基于单一来源的95%精度。智能便携式设备表现出出色的通用性(从93%到100%),实时性能(2.79ms),和便携性(32克)。
结论:所提出的智能设备可以高精度且实时地检测疲劳模式。
结论:该装置的应用具有降低老年人步态疲劳损伤风险的潜力。
OBJECTIVE: This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device.
METHODS: The smart device incorporated seven force sensors and a single inertial measurement unit (
IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60 min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation.
RESULTS: Five selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of
IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79 ms), and portability (32 g).
CONCLUSIONS: The proposed smart device can detect fatigue patterns with high precision and in real time.
CONCLUSIONS: The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.