Hand posture recognition: IR, sEMG and IMU
Hand posture recognition: IR, sEMG and IMU
Hands are important anatomical structures for musical performance, and recent developments in input device technology have allowed rather detailed capture of hand gestures using consumer-level products. While in some musical contexts, detailed hand and finger movements are required, in others it is sufficient to communicate discrete hand postures to indicate selection or other state changes. This research compared three approaches to capturing hand gestures where the shape of the hand, i.e. the relative positions and angles of finger joints, are an important part of the gesture. A number of sensor types can be used to capture information about hand posture, each of which has various practical advantages and disadvantages for music applications. This study compared three approaches, using optical, inertial and muscular information, with three sets of 5 hand postures (i.e. static gestures) and gesture recognition algorithms applied to the device data, aiming to determine which methods are most effective.
Hand posture, Gesture recognition, Motion capture
249–254
Polfreman, Richard
26424c3d-b750-4868-bf6e-2bbb3990df84
1 June 2018
Polfreman, Richard
26424c3d-b750-4868-bf6e-2bbb3990df84
Polfreman, Richard
(2018)
Hand posture recognition: IR, sEMG and IMU.
In Proceedings of the International Conference on New Interfaces for Musical Expression.
Zenodo.
.
(doi:10.5281/zenodo.1302571).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Hands are important anatomical structures for musical performance, and recent developments in input device technology have allowed rather detailed capture of hand gestures using consumer-level products. While in some musical contexts, detailed hand and finger movements are required, in others it is sufficient to communicate discrete hand postures to indicate selection or other state changes. This research compared three approaches to capturing hand gestures where the shape of the hand, i.e. the relative positions and angles of finger joints, are an important part of the gesture. A number of sensor types can be used to capture information about hand posture, each of which has various practical advantages and disadvantages for music applications. This study compared three approaches, using optical, inertial and muscular information, with three sets of 5 hand postures (i.e. static gestures) and gesture recognition algorithms applied to the device data, aiming to determine which methods are most effective.
Text
Hand Posture Recognition NIME 2018
- Accepted Manuscript
Text
nime 2018 paper 0054
- Version of Record
More information
Submitted date: 27 February 2018
Accepted/In Press date: 10 April 2018
e-pub ahead of print date: 1 June 2018
Published date: 1 June 2018
Venue - Dates:
International Conference on New Interfaces for Musical Expression (NIME’18) 2018, , Blacksburg, United States, 2018-06-03 - 2018-06-06
Keywords:
Hand posture, Gesture recognition, Motion capture
Identifiers
Local EPrints ID: 421302
URI: http://eprints.soton.ac.uk/id/eprint/421302
PURE UUID: e08f643b-eda5-457c-94eb-a21adb732f69
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Date deposited: 31 May 2018 16:31
Last modified: 16 Mar 2024 05:50
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