LAUSR

Lateral walking gait detection is necessary for the development of wearable devices applied to side stepping. To our knowledge, rarely work has been conducted to identify lateral walking gait by wearable sensors. Based on a hip exoskeleton, we presented a method for lateral walking gait phase detection only by two IMUs mounted on the shank. Experiments were conducted to detect narrow double support, swing of the leading leg, wide double support, and swing of trailing leg phases of 12 healthy subjects walking at various speeds. The performance of four different algorithms including thresholding (THR), modified $k$ -nearest neighbor named urban buildings indexing (UBI), random forest (RF), and neural networks (NNs) was evaluated. The occupation of space resources of NN is the smallest besides THR. The total recognition accuracy [mean and standard error of the mean (SEM)] of the RF-based, UBI-based, and NN-based systems was 97.07% ± 0.07% (off-line), 96.64% ± 0.16% (off-line), and 95.22% ± 0.60% (real-time), respectively. The recognition time of the models based on RF, UBI, NN, and THR was 13.3 ± 1.1, 5.7 ± 0.5, 2.6 ± 0.2, and 0.8 ± 0.2 ms, respectively. The recognition accuracy (cross-subjects) of RF-based, UBI-based, and NN-based systems was 91.72% ± 0.42%, 89.63% ± 0.48%, and 89.60% ± 0.43%, respectively. The results demonstrated that the proposed method can be applied to lateral walking gait detection.