Laboratory of Robotics, University of Ljubljana, Ljubljana, Slovenia.
Med Eng Phys. 2013 Dec;35(12):1713-20. doi: 10.1016/j.medengphy.2013.07.003. Epub 2013 Aug 12.
This paper presents algorithms for detection of gait initiation and termination using wearable inertial measurement units and pressure-sensitive insoles. Body joint angles, joint angular velocities, ground reaction force and center of plantar pressure of each foot are obtained from these sensors and input into supervised machine learning algorithms. The proposed initiation detection method recognizes two events: gait onset (an anticipatory movement preceding foot lifting) and toe-off. The termination detection algorithm segments gait into steps, measures the signals over a buffer at the beginning of each step, and determines whether this measurement belongs to the final step. The approach is validated with 10 subjects at two gait speeds, using within-subject and subject-independent cross-validation. Results show that gait initiation can be detected timely and accurately, with few errors in the case of within-subject cross-validation and overall good performance in subject-independent cross-validation. Gait termination can be predicted in over 80% of trials well before the subject comes to a complete stop. Results also show that the two sensor types are equivalent in predicting gait initiation while inertial measurement units are generally superior in predicting gait termination. Potential use of the algorithms is foreseen primarily with assistive devices such as prostheses and exoskeletons.
本文提出了使用可穿戴惯性测量单元和压力感应鞋垫来检测步态起始和终止的算法。从这些传感器中获取身体关节角度、关节角速度、地面反力和每个脚的足底压力中心，并将其输入到监督机器学习算法中。所提出的起始检测方法可以识别两个事件：步态开始（抬脚前的预备运动）和足趾离地。终止检测算法将步态分段为步骤，在每个步骤的开头测量缓冲区中的信号，并确定该测量是否属于最后一步。该方法在两种步态速度下，通过受试者内和受试者间的交叉验证进行了验证。结果表明，在受试者内交叉验证的情况下，步态起始可以及时准确地检测到，错误较少，而在受试者间交叉验证的情况下总体性能良好。在受试者完全停止之前，可以在超过 80%的试验中很好地预测步态终止。结果还表明，两种传感器类型在预测步态起始方面等效，而惯性测量单元在预测步态终止方面通常更优。这些算法的潜在用途主要是在辅助设备（如假肢和外骨骼）中。
