Name
Abstract | ||
|---|---|---|
BSTRACT Proprioception is the body’s ability to sense the position and movement of each limb, as well as the amount of effort exerted onto or by them. Methods to assess proprioception have been introduced before, yet there is little to no study on assessing the degree of proprioception on body parts for use cases like gesture recognition wearable computing. We propose the use of Fitts’ law coupled with the N-Back task to evaluate proprioception of the hand. We evaluate 15 distinct points at the back of the hand and assess the musing extended 3D Fitts’ law. Our results show that the index of difficulty of tapping point from thumb to pinky increases gradually with a linear regression factor of 0.1144. Additionally, participants perform the tap before performing the N-Back task. From these results, we discuss the fundamental limitations and suggest how Fitts’ law can be further extended to assess proprioception |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411763.3451699 | Conference on Human Factors in Computing Systems |
Keywords | DocType | Citations |
proprioception, kinaesthesia, Fitts' Law, N-Back | Conference | 0 |
PageRank | References | Authors |
0.34 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tamil Selvan Gunasekaran | 1 | 0 | 1.01 |
| Ryo Hajika | 2 | 0 | 0.34 |
| Chloe Dolma Si Ying Haigh | 3 | 0 | 0.34 |
| Yun Suen Pai | 4 | 21 | 10.09 |
| Danielle M. Lottridge | 5 | 13 | 3.05 |
| Mark Billinghurst | 6 | 5357 | 542.78 |