Chen Baojun, Zheng Enhao, Wang Qining
Intelligent Control Laboratory, College of Engineering, Peking University, Beijing 100871, China.
Sensors (Basel). 2014 Jul 10;14(7):12349-69. doi: 10.3390/s140712349.
Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.
运动意图预测对于控制动力下肢假肢以实现平稳的运动转换至关重要。在本研究中，我们开发了一种基于多传感器融合的运动意图预测系统，该系统可以识别当前的运动模式并提前检测运动转换。本研究招募了七名身体健全的受试者。测量了来自两个足底压力鞋垫和三个惯性测量单元（一个在大腿上，一个在小腿上，另一个在脚上）的信号。使用两级识别策略，通过线性判别分类器进行识别。本研究测试了六种运动模式和十种运动转换。对于七名身体健全的受试者，在稳定运动期间（即无运动转换）的识别准确率为99.71%±0.05%。在运动转换期间，所有转换均被正确检测到，并且大多数转换可以在转换到新的运动模式之前被检测到。在系统训练后的接下来五个小时内，未观察到识别性能有显著下降，并且训练可靠的分类器所需的实验试验次数较少。
