Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Sensors (Basel). 2020 Sep 21;20(18):5390. doi: 10.3390/s20185390.
Intent recognition in lower-limb assistive devices typically relies on neuromechanical sensing of an affected limb acquired through embedded device sensors. It remains unknown whether signals from more widespread sources such as the contralateral leg and torso positively influence intent recognition, and how specific locomotor tasks that place high demands on the neuromuscular system, such as changes of direction, contribute to intent recognition. In this study, we evaluated the performances of signals from varying mechanical modalities (accelerographic, gyroscopic, and joint angles) and locations (the trailing leg, leading leg and torso) during straight walking, changes of direction (cuts), and cuts to stair ascent with varying task anticipation. Biomechanical information from the torso demonstrated poor performance across all conditions. Unilateral (the trailing or leading leg) joint angle data provided the highest accuracy. Surprisingly, neither the fusion of unilateral and torso data nor the combination of multiple signal modalities improved recognition. For these fused modality data, similar trends but with diminished accuracy rates were reported during unanticipated conditions. Finally, for datasets that achieved a relatively accurate (≥90%) recognition of unanticipated tasks, these levels of recognition were achieved after the mid-swing of the trailing/transitioning leg, prior to a subsequent heel strike. These findings suggest that mechanical sensing of the legs and torso for the recognition of straight-line and transient locomotion can be implemented in a relatively flexible manner (i.e., signal modality, and from the leading or trailing legs) and, importantly, suggest that more widespread sensing is not always optimal.
下肢辅助设备中的意图识别通常依赖于通过嵌入式设备传感器获取的受影响肢体的神经机械传感。目前尚不清楚来自更广泛的来源（如对侧腿和躯干）的信号是否会积极影响意图识别，以及对神经肌肉系统要求较高的特定运动任务（如方向变化）如何有助于意图识别。在这项研究中，我们评估了在直走、变向（转弯）和变向到楼梯上升时不同机械模态（加速度计、陀螺仪和关节角度）和位置（后腿、前腿和躯干）的信号性能，且这些任务有不同的预期。躯干的生物力学信息在所有条件下表现都不佳。单侧（后腿或前腿）关节角度数据提供了最高的准确性。令人惊讶的是，单侧和躯干数据的融合，或多种信号模态的组合都没有提高识别率。对于这些融合模态数据，在未预期条件下报告了类似的趋势，但准确性降低。最后，对于达到相对准确（≥90%）的未预期任务识别的数据集，这些识别水平是在过渡腿的中间摆动之后、随后的脚跟撞击之前达到的。这些发现表明，用于识别直线和瞬态运动的腿部和躯干的机械传感可以以相对灵活的方式实现（即信号模态，以及来自前腿或后腿），并且重要的是，表明更广泛的传感并不总是最佳的。
