TY - GEN
T1 - Comparing the placement of two arm-worn devices for recognizing dynamic hand gestures
AU - Kefer, Kathrin
AU - Holzmann, Clemens
AU - Findling, Rainhard Dieter
N1 - Publisher Copyright:
© 2016 ACM.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - Dynamic hand gestures have become increasingly popular as an input modality for interactive systems. There exists a variety of arm-worn devices for the recognition of hand gestures, which differ not only in their capabilities, but also in the arm positions where they are worn. The aim of this paper is to investigate the effect of placement of such devices on the accuracy for recognizing dynamic hand gestures (e.g. waving the hand). This is relevant as different devices require different positions and thus differ in the achievable recognition accuracy. We have chosen two positions on the forearm: on the wrist and right below the elbow. These positions are interesing as smartwatches are usually worn on the wrist and devices using EMG sensors for the detection of static hand gestures (e.g. spreading the fingers) have to be worn right below the elbow. We used an LG G Watch worn on the wrist and a Myo armband from Thalmic Labs worn below the elbow. Both are equipped with three-axis accelerometers, which we used for gesture recognition. Our hypothesis was that the wristworn device would have a better recognition accuracy, as dynamic hand gestures have a bigger action radius on the wrist and therefore lead to bigger acceleration values. We conducted a comparative study with nine participants that performed eight simple, dynamic gestures on both devices. We tested the 4320 gesture samples with different classifiers and feature sets. Although the recognition results for the wrist-worn device were higher, the difference was not significant due to the substantial variation across participants.
AB - Dynamic hand gestures have become increasingly popular as an input modality for interactive systems. There exists a variety of arm-worn devices for the recognition of hand gestures, which differ not only in their capabilities, but also in the arm positions where they are worn. The aim of this paper is to investigate the effect of placement of such devices on the accuracy for recognizing dynamic hand gestures (e.g. waving the hand). This is relevant as different devices require different positions and thus differ in the achievable recognition accuracy. We have chosen two positions on the forearm: on the wrist and right below the elbow. These positions are interesing as smartwatches are usually worn on the wrist and devices using EMG sensors for the detection of static hand gestures (e.g. spreading the fingers) have to be worn right below the elbow. We used an LG G Watch worn on the wrist and a Myo armband from Thalmic Labs worn below the elbow. Both are equipped with three-axis accelerometers, which we used for gesture recognition. Our hypothesis was that the wristworn device would have a better recognition accuracy, as dynamic hand gestures have a bigger action radius on the wrist and therefore lead to bigger acceleration values. We conducted a comparative study with nine participants that performed eight simple, dynamic gestures on both devices. We tested the 4320 gesture samples with different classifiers and feature sets. Although the recognition results for the wrist-worn device were higher, the difference was not significant due to the substantial variation across participants.
KW - Accelerometer
KW - Arm-worn devices
KW - Gesture recognition
KW - Hand gestures
KW - Sensor placement
UR - http://www.scopus.com/inward/record.url?scp=85015060577&partnerID=8YFLogxK
U2 - 10.1145/3007120.3007146
DO - 10.1145/3007120.3007146
M3 - Conference contribution
SN - 978-1-4503-4806-5
T3 - ACM International Conference Proceeding Series
SP - 99
EP - 104
BT - 14th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2016 - Proceedings
A2 - Abdulrazak, Bessam
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Pardede, Eric
A2 - Anderst-Kotsis, Gabriele
PB - Association for Computing Machinery
T2 - 14th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2016
Y2 - 28 November 2016 through 30 November 2016
ER -