期刊
FRONTIERS IN ROBOTICS AND AI
卷 6, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/frobt.2019.00124
关键词
motion intent; wearable sensors; upper-limb; tactile sensing; assistive devices
类别
资金
- EPSRC (Engineering and Physical Sciences Research Council) in the UK
Within the field of robotics and autonomous systems where there is a human in the loop, intent recognition plays an important role. This is especially true for wearable assistive devices used for rehabilitation, particularly post-stroke recovery. This paper reports results on the use of tactile patterns to detect weak muscle contractions in the forearm while at the same time associating these patterns with the muscle synergies during different grips. To investigate this concept, a series of experiments with healthy participants were carried out using a tactile arm brace (TAB) on the forearm while performing four different types of grip. The expected force patterns were established by analysing the muscle synergies of the four grip types and the forearm physiology. The results showed that the tactile signatures of the forearm recorded on the TAB align with the anticipated force patterns. Furthermore, a linear separability of the data across all four grip types was identified. Using the TAB data, machine learning algorithms achieved a 99% classification accuracy. The TAB results were highly comparable to a similar commercial intent recognition system based on a surface electromyography (sEMG) sensing.
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