Gesture spotting algorithm for door opening using single wearable sensor

Mei Chuan Tseng; Kai Chun Liu; Chia Yeh Hsieh; Steen J. Hsu; Chia Tai Chan

doi:10.1109/ICASI.2018.8394398

Gesture spotting algorithm for door opening using single wearable sensor

Mei Chuan Tseng, Kai Chun Liu, Chia Yeh Hsieh, Steen J. Hsu, Chia Tai Chan

Department of Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Rehabilitation programs are critical for stroke patients to regain daily living skills. Accurate and objective assessment of the motor function is essential for health professionals to determine the appropriate clinical interventions and rehabilitation programs. Traditional assessment methods based on questionary and clinical observation have some issues in episodic examinations, subjective assessment and limited medical resources which tend to cause the difficulties for clinical professionals to observe real performance of daily activities performed by the patients in the home environment. In this paper, a door-opening activity monitoring system using a single wrist-worn inertial sensor to support assessing the performance of upper-limb movement during daily living is developed. The proposed approach could spot and identify three sub-gestures from complete door-opening gestures, including grasping door handle, rotating handle, opening door. The overall performance of the proposed approach achieves 0.933 in accuracy, 0.896 in precision and 0.904 in recall.

Original language	English
Title of host publication	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
Editors	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	854-856
Number of pages	3
ISBN (Electronic)	9781538643426
DOIs	https://doi.org/10.1109/ICASI.2018.8394398
State	Published - 22 Jun 2018
Event	4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan Duration: 13 Apr 2018 → 17 Apr 2018

Publication series

Name	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference	4th IEEE International Conference on Applied System Innovation, ICASI 2018
Country/Territory	Japan
City	Chiba
Period	13/04/18 → 17/04/18

Keywords

door-opening gesture spotting
inertial sensors
wearable sensors

Access to Document

10.1109/ICASI.2018.8394398

Cite this

Tseng, M. C., Liu, K. C., Hsieh, C. Y., Hsu, S. J., & Chan, C. T. (2018). Gesture spotting algorithm for door opening using single wearable sensor. In A. D. K.-T. Lam, S. D. Prior, & T.-H. Meen (Eds.), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018 (pp. 854-856). (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2018.8394398

Tseng, Mei Chuan ; Liu, Kai Chun ; Hsieh, Chia Yeh et al. / Gesture spotting algorithm for door opening using single wearable sensor. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 854-856 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

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title = "Gesture spotting algorithm for door opening using single wearable sensor",

abstract = "Rehabilitation programs are critical for stroke patients to regain daily living skills. Accurate and objective assessment of the motor function is essential for health professionals to determine the appropriate clinical interventions and rehabilitation programs. Traditional assessment methods based on questionary and clinical observation have some issues in episodic examinations, subjective assessment and limited medical resources which tend to cause the difficulties for clinical professionals to observe real performance of daily activities performed by the patients in the home environment. In this paper, a door-opening activity monitoring system using a single wrist-worn inertial sensor to support assessing the performance of upper-limb movement during daily living is developed. The proposed approach could spot and identify three sub-gestures from complete door-opening gestures, including grasping door handle, rotating handle, opening door. The overall performance of the proposed approach achieves 0.933 in accuracy, 0.896 in precision and 0.904 in recall.",

keywords = "door-opening gesture spotting, inertial sensors, wearable sensors",

author = "Tseng, {Mei Chuan} and Liu, {Kai Chun} and Hsieh, {Chia Yeh} and Hsu, {Steen J.} and Chan, {Chia Tai}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Conference on Applied System Innovation, ICASI 2018 ; Conference date: 13-04-2018 Through 17-04-2018",

year = "2018",

month = jun,

day = "22",

doi = "10.1109/ICASI.2018.8394398",

language = "English",

series = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "854--856",

editor = "Lam, {Artde Donald Kin-Tak} and Prior, {Stephen D.} and Teen-Hang Meen",

booktitle = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",

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Tseng, MC, Liu, KC, Hsieh, CY, Hsu, SJ & Chan, CT 2018, Gesture spotting algorithm for door opening using single wearable sensor. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 854-856, 4th IEEE International Conference on Applied System Innovation, ICASI 2018, Chiba, Japan, 13/04/18. https://doi.org/10.1109/ICASI.2018.8394398

Gesture spotting algorithm for door opening using single wearable sensor. / Tseng, Mei Chuan; Liu, Kai Chun; Hsieh, Chia Yeh et al.
Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 854-856 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Gesture spotting algorithm for door opening using single wearable sensor

AU - Tseng, Mei Chuan

AU - Liu, Kai Chun

AU - Hsieh, Chia Yeh

AU - Hsu, Steen J.

AU - Chan, Chia Tai

PY - 2018/6/22

Y1 - 2018/6/22

N2 - Rehabilitation programs are critical for stroke patients to regain daily living skills. Accurate and objective assessment of the motor function is essential for health professionals to determine the appropriate clinical interventions and rehabilitation programs. Traditional assessment methods based on questionary and clinical observation have some issues in episodic examinations, subjective assessment and limited medical resources which tend to cause the difficulties for clinical professionals to observe real performance of daily activities performed by the patients in the home environment. In this paper, a door-opening activity monitoring system using a single wrist-worn inertial sensor to support assessing the performance of upper-limb movement during daily living is developed. The proposed approach could spot and identify three sub-gestures from complete door-opening gestures, including grasping door handle, rotating handle, opening door. The overall performance of the proposed approach achieves 0.933 in accuracy, 0.896 in precision and 0.904 in recall.

AB - Rehabilitation programs are critical for stroke patients to regain daily living skills. Accurate and objective assessment of the motor function is essential for health professionals to determine the appropriate clinical interventions and rehabilitation programs. Traditional assessment methods based on questionary and clinical observation have some issues in episodic examinations, subjective assessment and limited medical resources which tend to cause the difficulties for clinical professionals to observe real performance of daily activities performed by the patients in the home environment. In this paper, a door-opening activity monitoring system using a single wrist-worn inertial sensor to support assessing the performance of upper-limb movement during daily living is developed. The proposed approach could spot and identify three sub-gestures from complete door-opening gestures, including grasping door handle, rotating handle, opening door. The overall performance of the proposed approach achieves 0.933 in accuracy, 0.896 in precision and 0.904 in recall.

KW - door-opening gesture spotting

KW - inertial sensors

KW - wearable sensors

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M3 - Conference contribution

AN - SCOPUS:85050320300

T3 - Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

SP - 854

EP - 856

BT - Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

A2 - Lam, Artde Donald Kin-Tak

A2 - Prior, Stephen D.

A2 - Meen, Teen-Hang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Applied System Innovation, ICASI 2018

Y2 - 13 April 2018 through 17 April 2018

ER -

Tseng MC, Liu KC, Hsieh CY, Hsu SJ, Chan CT. Gesture spotting algorithm for door opening using single wearable sensor. In Lam ADKT, Prior SD, Meen TH, editors, Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 854-856. (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). doi: 10.1109/ICASI.2018.8394398

Gesture spotting algorithm for door opening using single wearable sensor

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