Continuous Classification of Locomotor Transitions Performed Under Altered Cutting Style, Complexity and Anticipation

Kazemimoghadam, Mahdieh; Li, Wentao; Fey, Nicholas P.

doi:10.1109/biorob.2018.8487746

Cited by 3 publications

(2 citation statements)

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“…For instance, in order to detect unanticipated tasks, substantial amount of unanticipated information (i.e., five bouts) were included in the training data. We expect that accuracy rates for unanticipated locomotion modes would diminish using training data with reduced number of target task repetitions [ 24 ]. Thus, the potential impact of training data on the outcomes should be taken into consideration when selecting optimal signal sources.…”

Section: Discussionmentioning

confidence: 99%

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Biomechanical Signals of Varied Modality and Location Contribute Differently to Recognition of Transient Locomotion

Kazemimoghadam

Fey

2020

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

“…For classifying anticipated locomotor tasks, the classifier was trained with only anticipated data. For unanticipated tasks, it was trained with all anticipated and unanticipated trials [ 24 ]. Leave-one-out cross validation was applied to each subject’s data.…”

Section: Methodsmentioning

confidence: 99%