Abstract
Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).
Highlights
According to a survey on the usage of prostheses [1], 28% of the users are categorized as “prosthesis rejecters”, who use their prostheses no more than once a year, mainly because of the clumsy control of commercial prostheses
With the simulated data sequence, we investigated how performance degradation was affected by the drift extent and the drift rate
We proposed a particle adaptive strategy to reduce the performance degradation in the long-term
Summary
According to a survey on the usage of prostheses [1], 28% of the users are categorized as “prosthesis rejecters”, who use their prostheses no more than once a year, mainly because of the clumsy control of commercial prostheses. When employing PR-based methods to realize myoelectric control, interfering factors such as temperature and humidity changes, skin impedance variation, muscular fatigue, electrode shifting and limb position changes will cause classification degradation [4,5,6], hindering the clinical application and the commercialization of the PR-based EMG control scheme After analyzing both industrial and academic demands, Farina et al [7] divided the demand for reliability of upper limb prosthesis control system into two parts: (a) the robustness to instantaneous changes such as the electrode shifting when donning and doffing, and arm posture variation; and (b) the adaptability to slow changes such as muscular fatigue and skin impedance variation.
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