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Hand Posture Recognition: IR, sEMG and IMU

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
Polfreman, Richard
26424c3d-b750-4868-bf6e-2bbb3990df84
Polfreman, Richard
26424c3d-b750-4868-bf6e-2bbb3990df84

Polfreman, Richard (2018) Hand Posture Recognition: IR, sEMG and IMU. New Interfaces for Musical Expression 2018, Blacksburg, VA, United States. 03 - 06 Jun 2018. 6 pp.

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 HandPostureRecognition_NIME_2018_final - Accepted Manuscript
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More information

Submitted date: 27 February 2018
Accepted/In Press date: 10 April 2018
Published date: 3 June 2018
Venue - Dates: New Interfaces for Musical Expression 2018, Blacksburg, VA, United States, 2018-06-03 - 2018-06-06
Keywords: Hand posture, Gesture recognition, Motion capture

Identifiers

Local EPrints ID: 421302
URI: https://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: 04 Jun 2018 16:30

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