IEICE TRANSACTIONS on Information
A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer
Summary :
Automatic recognition of finger gestures can be used for promotion of life quality. For example, a senior citizen can control the home appliance, call for help in emergency, or even communicate with others through simple finger gestures. Here, we focus on one-stroke finger gesture, which are intuitive to be remembered and performed. In this paper, we proposed and evaluated an accelerometer-based method for detecting the predefined one-stroke finger gestures from the data collected using a MEMS 3D accelerometer worn on the index finger. As alternative to the optoelectronic, sonic and ultrasonic approaches, the accelerometer-based method is featured as self-contained, cost-effective, and can be used in noisy or private space. A compact wireless sensing mote integrated with the accelerometer, called MagicRing, is developed to be worn on the finger for real data collection. A general definition on one-stroke gesture is given out, and 12 kinds of one-stroke finger gestures are selected from human daily activities. A set of features is extracted among the candidate feature set including both traditional features like standard deviation, energy, entropy, and frequency of acceleration and a new type of feature called relative feature. Both subject-independent and subject-dependent experiment methods were evaluated on three kinds of representative classifiers. In the subject-independent experiment among 20 subjects, the decision tree classifier shows the best performance recognizing the finger gestures with an average accuracy rate for 86.92 %. In the subject-dependent experiment, the nearest neighbor classifier got the highest accuracy rate for 97.55 %.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.5 pp.1062-1072
- Publication Date
- 2011/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1062
- Type of Manuscript
- PAPER
- Category
- Rehabilitation Engineering and Assistive Technology
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Lei JING, Yinghui ZHOU, Zixue CHENG, Junbo WANG, "A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 5, pp. 1062-1072, May 2011, doi: 10.1587/transinf.E94.D.1062.
Abstract: Automatic recognition of finger gestures can be used for promotion of life quality. For example, a senior citizen can control the home appliance, call for help in emergency, or even communicate with others through simple finger gestures. Here, we focus on one-stroke finger gesture, which are intuitive to be remembered and performed. In this paper, we proposed and evaluated an accelerometer-based method for detecting the predefined one-stroke finger gestures from the data collected using a MEMS 3D accelerometer worn on the index finger. As alternative to the optoelectronic, sonic and ultrasonic approaches, the accelerometer-based method is featured as self-contained, cost-effective, and can be used in noisy or private space. A compact wireless sensing mote integrated with the accelerometer, called MagicRing, is developed to be worn on the finger for real data collection. A general definition on one-stroke gesture is given out, and 12 kinds of one-stroke finger gestures are selected from human daily activities. A set of features is extracted among the candidate feature set including both traditional features like standard deviation, energy, entropy, and frequency of acceleration and a new type of feature called relative feature. Both subject-independent and subject-dependent experiment methods were evaluated on three kinds of representative classifiers. In the subject-independent experiment among 20 subjects, the decision tree classifier shows the best performance recognizing the finger gestures with an average accuracy rate for 86.92 %. In the subject-dependent experiment, the nearest neighbor classifier got the highest accuracy rate for 97.55 %.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1062/_p
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@ARTICLE{e94-d_5_1062,
author={Lei JING, Yinghui ZHOU, Zixue CHENG, Junbo WANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer},
year={2011},
volume={E94-D},
number={5},
pages={1062-1072},
abstract={Automatic recognition of finger gestures can be used for promotion of life quality. For example, a senior citizen can control the home appliance, call for help in emergency, or even communicate with others through simple finger gestures. Here, we focus on one-stroke finger gesture, which are intuitive to be remembered and performed. In this paper, we proposed and evaluated an accelerometer-based method for detecting the predefined one-stroke finger gestures from the data collected using a MEMS 3D accelerometer worn on the index finger. As alternative to the optoelectronic, sonic and ultrasonic approaches, the accelerometer-based method is featured as self-contained, cost-effective, and can be used in noisy or private space. A compact wireless sensing mote integrated with the accelerometer, called MagicRing, is developed to be worn on the finger for real data collection. A general definition on one-stroke gesture is given out, and 12 kinds of one-stroke finger gestures are selected from human daily activities. A set of features is extracted among the candidate feature set including both traditional features like standard deviation, energy, entropy, and frequency of acceleration and a new type of feature called relative feature. Both subject-independent and subject-dependent experiment methods were evaluated on three kinds of representative classifiers. In the subject-independent experiment among 20 subjects, the decision tree classifier shows the best performance recognizing the finger gestures with an average accuracy rate for 86.92 %. In the subject-dependent experiment, the nearest neighbor classifier got the highest accuracy rate for 97.55 %.},
keywords={},
doi={10.1587/transinf.E94.D.1062},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer
T2 - IEICE TRANSACTIONS on Information
SP - 1062
EP - 1072
AU - Lei JING
AU - Yinghui ZHOU
AU - Zixue CHENG
AU - Junbo WANG
PY - 2011
DO - 10.1587/transinf.E94.D.1062
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2011
AB - Automatic recognition of finger gestures can be used for promotion of life quality. For example, a senior citizen can control the home appliance, call for help in emergency, or even communicate with others through simple finger gestures. Here, we focus on one-stroke finger gesture, which are intuitive to be remembered and performed. In this paper, we proposed and evaluated an accelerometer-based method for detecting the predefined one-stroke finger gestures from the data collected using a MEMS 3D accelerometer worn on the index finger. As alternative to the optoelectronic, sonic and ultrasonic approaches, the accelerometer-based method is featured as self-contained, cost-effective, and can be used in noisy or private space. A compact wireless sensing mote integrated with the accelerometer, called MagicRing, is developed to be worn on the finger for real data collection. A general definition on one-stroke gesture is given out, and 12 kinds of one-stroke finger gestures are selected from human daily activities. A set of features is extracted among the candidate feature set including both traditional features like standard deviation, energy, entropy, and frequency of acceleration and a new type of feature called relative feature. Both subject-independent and subject-dependent experiment methods were evaluated on three kinds of representative classifiers. In the subject-independent experiment among 20 subjects, the decision tree classifier shows the best performance recognizing the finger gestures with an average accuracy rate for 86.92 %. In the subject-dependent experiment, the nearest neighbor classifier got the highest accuracy rate for 97.55 %.
ER -
English
