Name
Title | ||
|---|---|---|
Wearable Tactile Sensor Brace for Motion Intent Recognition in Upper-Limb Rehabilitation |
Abstract | ||
|---|---|---|
Motion intent recognition is an important part of autonomous rehabilitative and assistive devices. The focus of this paper is on upper limb motion intent detection for use in wearable rehabilitative devices. Our aim is to capture the tactile cues that arise during weak muscle contractions. We introduce the tactile arm brace (TAB) and analyse the various patterns recorded. Depending on the arm muscles that contract to perform a particular hand motion, different parts of the TAB will experience variations in the interaction forces. A study involving 12 healthy subjects was conducted using the TAB and a bespoke gripping device, designed and built to measure gripping forces. Tactile signatures on the brace change when the fingers extend or grip; this validates our hypothesis based on the forearm muscle physiology. Gripping models were generated to relate low-strength gripping, between 0kg and 2kg, and TAB force readings. The best recorded sensitivity (ratio between the gripper force and the FSR), found in the proximity of the posterior radial forearm, was 0.052. Using all TAB sensors, the K-fold cross validation method produced an RMSE of 5.48N. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIOROB.2018.8487721 | 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) |
Keywords | Field | DocType |
wearable tactile sensor brace,motion intent recognition,upper-limb rehabilitation,assistive devices,upper limb motion intent detection,wearable rehabilitative devices,tactile cues,weak muscle contractions,tactile arm brace,interaction forces,tactile signatures,forearm muscle physiology,low-strength gripping,TAB force readings,TAB sensors,arm muscles,hand motion,gripping forces,autonomous rehabilitative device,gripper force,K-fold cross validation method | Sensory cue,Rehabilitation,Brace,Upper limb,Wrist,Wearable computer,Computer science,Forearm,Physical medicine and rehabilitation,Tactile sensor | Conference |
ISSN | ISBN | Citations |
2155-1774 | 978-1-5386-8184-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thekla Stefanou | 1 | 0 | 0.34 |
| Greg Chance | 2 | 3 | 2.07 |
| Tareq Assaf | 3 | 16 | 4.55 |
| Alexander Lenz | 4 | 0 | 1.01 |
| Sanja Dogramadzi | 5 | 43 | 16.60 |