Implementation of Rule-based Smartphone Motion Detection Systems

Eon-Ju Lee (이언주); Seung-Hui Ryou (유승희); So-Yun Lee (이소윤); Sung-Yoon Jeon (전성윤); Eun-Hwa Park (박은화); Jung-Ha Hwang (황정하); Doo-Hyun Choi (최두현)

doi:10.9708/jksci.2021.26.07.045

Implementation of Rule-based Smartphone Motion Detection Systems

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(7), pp.45-55
DOI : 10.9708/jksci.2021.26.07.045
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : June 9, 2021
Accepted : July 7, 2021
Published : July 30, 2021

Eon-Ju Lee ¹, Seung-Hui Ryou ¹, So-Yun Lee ¹, Sung-Yoon Jeon ¹, Eun-Hwa Park ¹, Jung-Ha Hwang ¹, Doo-Hyun Choi ¹

¹경북대학교

Accredited

ABSTRACT

Information obtained through various sensors embedded in a smartphone can be used to identify and analyze user’s movements and situations. In this paper, we propose two rule-based motion detection systems that can detect three alphabet motions, ‘I’, ‘S’, and ‘Z’ by analyzing data obtained by the acceleration and gyroscope sensors in a smartphone. First of all, the characteristics of acceleration and angular velocity for each motion are analyzed. Based on the analysis, two rule-based systems are proposed and implemented as an android application and it is used to verify the detection performance for each motion. Two rule-based systems show high recognition rate over 90% for each motion and the rule-based system using ensemble shows better performance than another one.

KEYWORDS

Smartphone Sensors, Acceleration Sensors, Gyroscope Sensors, Motion Recognition, Rule-Based System

Citation status

* References for papers published after 2023 are currently being built.

[web] Gallup / Research of usage and brand of smartphones, smart watches, wireless earphone from 2012 to 2020 / https://www.gallup.co.kr/gallupdb/reportContent.asp?seqNo=1134

[journal] 권승현 / 2016 / A Implementation of User Exercise Motion Recognition System Using Smart-Phone / 한국산학기술학회논문지 / 한국산학기술학회 17(10) : 396~402

[web] Yoo-jin Hong / Design and implementation of safe-walking mobile application using smartphone sensing data / https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE06602872

[web] Samsung / What is S pen air actions? / https://www.samsung.com/global/galaxy/what-is/air-actions/

[web] Samsung / Galaxy watch3 45mm(Bluetooth)” / https://www.samsung.com/sec/wearables/galaxy-watch3-r840/SM-R840NZKAKOO/

[web] Apple / Use raise to wake on your iPhone / https://support.apple.com/ko-kr/HT208081

[journal] 김종환 / 2014 / Design and Implementation of a Two-Phase Activity Recognition System Using Smartphone’s Accelerometers / 정보처리학회 논문지 / 한국정보처리학회 3(2) : 87~92

[confproc] Hyunju Cho / 2012 / Motion recognition with smart phone embedded 3-axis accelerometer sensor / 2012 IEEE International Conference on Systems, Man, and Cybernetics : 919~924

[confproc] Xian Wang / 2012 / Gesture recognition using mobile phone’s inertial sensors / Distributed Computing and Artificial Intelligence : 173~184

[confproc] Chunyu Xie / 2016 / Gesture recognition benchmark based on mobile phone / Chinese Conference on Biometric Recognition : 432~440

[journal] 이용철 / 2014 / Motion Recognition of Smartphone using Sensor Data / 멀티미디어학회논문지 / 한국멀티미디어학회 17(12) : 1446~1452

[journal] Tsung-Ming Tai / 2018 / Sensor-based continuous hand gesture recognition by long short-term memory / IEEE sensors letters 2(3) : 1~4

[journal] Yubo Shao / 2020 / AirSign : Smartphone Authentication by Signing in the Air / Sensors 21(1) : 104~

[confproc] Joa˜o Prudeˆncio / 2013 / Physical activity recognition from smartphone embedded sensors / Iberian Conference on Pattern Recognition and Image Analysis : 863~872

[confproc] Alvina Anjum / 2013 / Activity recognition using smartphone sensors / 2013 IEEE 10th consumer communications and networking conference : 914~919

[journal] Muhammad Shoaib / 2014 / Fusion of smartphone motion sensors for physical activity recognition / Sensors 14(6) : 10146~10176

[journal] Akram Bayat / 2014 / A study on human activity recognition using accelerometer data from smartphones / Procedia Computer Science 34 : 450~457

[journal] Xingfeng Wang / 2015 / Detecting User Activities with the Accelerometer on Android Smartphones / Journal of Multimedia Information System 2(2) : 233~240

[journal] Charissa Ann Ronao / 2016 / Human activity recognition with smartphone sensors using deep learning neural networks / Expert systems with applications 59 : 235~244

[confproc] Amari Vaughn / 2018 / Activity detection and analysis using smartphone sensors / 2018 IEEE International Conference on Information Reuse and Integration : 102~107

[journal] Robert-Andrei Voicu / 2019 / Human physical activity recognition using smartphone sensors / Sensors 19(3) : 458~

[confproc] Abdulrahman Alruban / 2019 / Physical activity recognition by utilising smartphone sensor signals / 8th International Conference on Pattern Recognition Applications and Methods : 342~351

[journal] Nadeem Ahmed / 2020 / Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model / Sensors 20(1) : 317~

[journal] 양혜경 / 2014 / Real-Time Physical Activity Recognition Using Tri-axis Accelerometer of Smart Phone / 멀티미디어학회논문지 / 한국멀티미디어학회 17(4) : 506~513

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Implementation of Rule-based Smartphone Motion Detection Systems

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Implementation of Rule-based Smartphone Motion Detection Systems

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (0)

REFERENCES (24) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.