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Tactile signatures and hand motion intent recognition for wearable assistive devices

Within the field of robotics and autonomous systems, intent recognition is crucial when human and robot workspaces overlap. This is especially true with wearable devices and in particular those used for assistive or rehabilitative purposes. This paper reports results on the use of tactile patterns t...

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Hauptverfasser: Stefanou, Thekla (VerfasserIn) , Chance, Greg (VerfasserIn) , Assaf, Tareq (VerfasserIn) , Dogramadzi, Sanja (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 21 November 2019
In: Frontiers in robotics and AI
Year: 2019, Jahrgang: 6
ISSN:2296-9144
DOI:10.3389/frobt.2019.00124
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.3389/frobt.2019.00124
Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/frobt.2019.00124/full
Volltext
Verfasserangaben:Thekla Stefanou, Greg Chance, Tareq Assaf and Sanja Dogramadzi

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520 |a Within the field of robotics and autonomous systems, intent recognition is crucial when human and robot workspaces overlap. This is especially true with wearable devices and in particular those used for assistive or rehabilitative purposes. 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 gripping. To investigate this concept a series of experiments with healthy participants were carried out using a tactile arm brace (TAB) on the forearm to perform 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 on the TAB aligned with the anticipated force patterns. Furthermore, there was a linear separability of the data across the four grip types. 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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