Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices
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 patt...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-11-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/frobt.2019.00124/full |
| _version_ | 1818577754290913280 |
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| author | Thekla Stefanou Greg Chance Tareq Assaf Sanja Dogramadzi |
| author_facet | Thekla Stefanou Greg Chance Tareq Assaf Sanja Dogramadzi |
| author_sort | Thekla Stefanou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T06:34:56Z |
| format | Article |
| id | doaj.art-f13ed85142764f1dbc110a18db69142b |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-16T06:34:56Z |
| publishDate | 2019-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-f13ed85142764f1dbc110a18db69142b2022-12-21T22:40:48ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442019-11-01610.3389/frobt.2019.00124467367Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive DevicesThekla Stefanou0Greg Chance1Tareq Assaf2Sanja Dogramadzi3ZITI, Heidelberg University, Heidelberg, GermanyBristol Robotics Laboratory, Department of Computer Science, University of Bristol, Bristol, United KingdomDepartment of Electronic and Electrical Engineering, University of Bath, Bath, United KingdomBristol Robotics Laboratory, Department of Engineering Design and Mathematics, University of the West England, Bristol, United KingdomWithin 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.https://www.frontiersin.org/article/10.3389/frobt.2019.00124/fullmotion intentwearable sensorsupper-limbtactile sensingassistive devices |
| spellingShingle | Thekla Stefanou Greg Chance Tareq Assaf Sanja Dogramadzi Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices Frontiers in Robotics and AI motion intent wearable sensors upper-limb tactile sensing assistive devices |
| title | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
| title_full | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
| title_fullStr | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
| title_full_unstemmed | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
| title_short | Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices |
| title_sort | tactile signatures and hand motion intent recognition for wearable assistive devices |
| topic | motion intent wearable sensors upper-limb tactile sensing assistive devices |
| url | https://www.frontiersin.org/article/10.3389/frobt.2019.00124/full |
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