ArticleFrontiers in bioengineering and biotechnology2021
Continuous Classification of Locomotion in Response to Task Complexity and Anticipatory State.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 3 citations in OpenAlex.
- The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.Sensors (Basel, Switzerland) · 2024Article
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2 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveIntent recognition in lower-extremity assistive devices (e.g., prostheses and exoskeletons) is typically limited to either recognition of steady-state locomotion or changes of terrain (e.g., level ground to stair) occurring in a straight-line path and under anticipated condition. Stability is highly affected during non-steady changes of direction such as cuts especially when they are unanticipated, posing high risk of fall-related injuries. Here, we studied the influence of changes of direction and user anticipation on task recognition, and accordingly introduced classification schemes accommodating such effects.
methodsA linear discriminant analysis (LDA) classifier continuously classified straight-line walking, sidestep/crossover cuts (single transitions), and cuts-to-stair locomotion (mixed transitions) performed under varied task anticipatory conditions. Training paradigms with varying levels of anticipated/unanticipated exposures and analysis windows of size 100-600 ms were examined.
resultsMore accurate classification of anticipated relative to unanticipated tasks was observed. Including bouts of target task in the training data was necessary to improve generalization to unanticipated locomotion. Only up to two bouts of target task were sufficient to reduce errors to <20% in unanticipated mixed transitions, whereas, in single transitions and straight walking, substantial unanticipated information (i.e., five bouts) was necessary to achieve similar outcomes. Window size modifications did not have a significant influence on classification performance.
conclusionAdjusting the training paradigm helps to achieve classification schemes capable of adapting to changes of direction and task anticipatory state. SIGNIFICANCE: The findings could provide insight into developing classification schemes that can adapt to changes of direction and user anticipation. They could inform intent recognition strategies for controlling lower-limb assistive to robustly handle "unknown" circumstances, and thus deliver increased level of reliability and safety.
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