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Gandomkar, Muhammad, Sarang, Reza, Gandomkar, Ziba. (1399). A Framework to Identify and Count Popular Exercises Using Smartphone Sensors Based on Machine learning. سامانه مدیریت نشریات علمی, 4(2), 29-37.
Muhammad Gandomkar; Reza Sarang; Ziba Gandomkar. "A Framework to Identify and Count Popular Exercises Using Smartphone Sensors Based on Machine learning". سامانه مدیریت نشریات علمی, 4, 2, 1399, 29-37.
Gandomkar, Muhammad, Sarang, Reza, Gandomkar, Ziba. (1399). 'A Framework to Identify and Count Popular Exercises Using Smartphone Sensors Based on Machine learning', سامانه مدیریت نشریات علمی, 4(2), pp. 29-37.
Gandomkar, Muhammad, Sarang, Reza, Gandomkar, Ziba. A Framework to Identify and Count Popular Exercises Using Smartphone Sensors Based on Machine learning. سامانه مدیریت نشریات علمی, 1399; 4(2): 29-37.

A Framework to Identify and Count Popular Exercises Using Smartphone Sensors Based on Machine learning

Journal of Advanced Sport Technology
دوره 4، شماره 2 - شماره پیاپی 7، مهر 2020، صفحه 29-37    اصل مقاله (893.8 K)
نوع مقاله: Original research papers
نویسندگان
Muhammad Gandomkar1؛ Reza Sarang* 2؛ Ziba Gandomkar3
1Department of Sports Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2Department of Sports Engineering, Faculty of Engineering, Science and Research Branch, IAU, Tehran
3Faculty of Medicine and Health, University of Sydney, Sydney, Australia
چکیده
Smartphones have wide range of sensors such as gyroscopes or inertial sensors, which can be used for recognizing and tracking exercises. A framework, called TrainingPal, was proposed to automatically identify five types of cardio exercises and five types of resistance exercises. Included exercises were running, walking, rowing, using elliptical machine, and jumping jack. Sit-up, bench dip, push-up, squat, and lunge were included as popular resistance exercises. In addition to recognition of each exercises, the proposed framework was able to count number of repetitions of each exercise. To train and test the proposed framework, data was collected from Samsung Galaxy S7 edge, which was attached to the outer side of arm approximately 10 to 12 cm below the shoulder. To avoid overfitting, we used leave-one-subject-out cross validation. An overall accuracy of 91.71% was achieved in identifying different types of exercises. The accuracy ranged from 100% for push-ups to 60.33% for bench dips. The accuracy of the proposed framework in counting the exercises was 90%. The results suggested that the proposed framework can be used for identifying and tracking of the included exercises. The framework can be extended to other wearable devices. 
کلیدواژه‌ها
Exercise recognition؛ Exercise tracking؛ Inertial sensors؛ Smartphone
مراجع

1. Duncan GE. Exercise, fitness, and cardiovascular disease risk in type 2 diabetes and the metabolic syndrome. Current diabetes reports. 2006;6(1):29-35.

2. Colberg SR, Sigal RJ, Fernhall B, Regensteiner JG, Blissmer BJ, Rubin RR, Chasan-Taber L, Albright AL, Braun B. Exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: joint position statement executive summary. Diabetes care. 2010;33(12):2692-6.

3. Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Comprehensive Physiology. 2011;2(2):1143-211.

4. de Rezende LF, Rabacow FM, Viscondi JY, do Carmo Luiz O, Matsudo VK, Lee IM. Effect of physical inactivity on major noncommunicable diseases and life expectancy in Brazil. Journal of Physical Activity and Health. 2015;12(3):299-306.

5.Verbrugge LM, Patrick DL. Seven chronic conditions: their impact on US adults' activity levels and use of medical services. American journal of public Health. 1995;85(2):173-82.

6.Sadeghi M, Haghdoost AA, Bahrampour A, Dehghani M. Modeling the burden of cardiovascular diseases in Iran from 2005 to 2025: The impact of demographic changes. Iranian journal of public health. 2017;46(4):506.

7. Imani A, Gharibi F, Dadashi O, Najafi M, Mirbagheri SM. Analysis of cardiovascular diseases costs and their effective factors in Tabriz hospitalized patients, 2015. Jundishapur Journal of Health Sciences. 2016;8(2).

8. James, Daniel. Wearable Technology in sport, a convergence of trends. Journal of Advanced Sport Technology. 2017; 1(1): 1-4.

9. Meamarbashi, Abbas. Designing a new wearable and Wireless Inertial Measurement Unit for the physical activity monitoring. Journal of Advanced Sport Technology.2018; 2(2): 1-5.

10.Foster CA. Monitoring training in athletes with reference to overtraining syndrome. Occupational Health and Industrial Medicine. 1998;4(39):189.

11. Foster C, Daines E, Hector L, Snyder AC, Welsh R. Athletic performance in relation to training load. Wisconsin medical journal. 1996;95(6):370-4.

12. Angeli A, Minetto M, Dovio A, Paccotti P. The overtraining syndrome in athletes: a stress-related disorder. Journal of endocrinological investigation. 2004;27(6):603-12.

13. Ciuti G, Ricotti L, Menciassi A, Dario P. MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy. Sensors. 2015;15(3):6441-68.

14. Arif M, Kattan A. Physical activities monitoring using wearable acceleration sensors attached to the body. PloS one. 2015;10(7):e0130851.

15. Bleser G, Steffen D, Reiss A, Weber M, Hendeby G, Fradet L. Personalized physical activity monitoring using wearable sensors. InSmart health 2015 (pp. 99-124). Springer, Cham.

16. Cuzzolin F, Sapienza M, Esser P, Saha S, Franssen MM, Collett J, Dawes H. Metric learning for Parkinsonian identification from IMU gait measurements. Gait & posture. 2017;54:127-32.

17. Sijobert B, Denys J, Coste CA, Geny C. IMU based detection of freezing of gait and festination in Parkinson's disease. In2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference (IFESS) 2014 Sep 17 (pp. 1-3). IEEE.

18. Roetenberg D, Luinge HJ, Baten CT, Veltink PH. Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation. IEEE Transactions on neural systems and rehabilitation engineering. 2005;13(3):395-405.

19. Wahlström J, Skog I, Händel P. IMU alignment for smartphone-based automotive navigation. In2015 18th International Conference on Information Fusion (Fusion) 2015 (pp. 1437-1443). IEEE.

20. Zhou P, Chan W, Jiang S, Ou J, Li M, Shen G. instant phone attitude estimation and its applications. InProceedings of the 20th annual international conference on Mobile computing and networking 2014 (pp. 333-336).

21. Guiry JJ, Van de Ven P, Nelson J. Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. Sensors. 2014;14(3):5687-701.

22. Shoaib M, Bosch S, Scholten H, Havinga PJ, Incel OD. Towards detection of bad habits by fusing smartphone and smartwatch sensors. In2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) 2015 (pp. 591-596). IEEE.

23. Mortazavi BJ, Pourhomayoun M, Alsheikh G, Alshurafa N, Lee SI, Sarrafzadeh M. Determining the single best axis for exercise repetition recognition and counting on smartwatches. In2014 11th International Conference on Wearable and Implantable Body Sensor Networks 2014 (pp. 33-38). IEEE.

24. Shuster MD, Oh SD. Three-axis attitude determination from vector observations. Journal of guidance and Control. 1981;4(1):70-7.

25. Khedr M, El-Sheimy N. A smartphone step counter using IMU and magnetometer for navigation and health monitoring applications. Sensors. 2017;17(11):2573.

26. Naghizadeh-Baghi, Abbas, and Farzad Nobakht Sareban. Designing a Web-based Health Document Automation for Fitness Clubs. Journal of Advanced Sport Technology. 2019; 3(2): 93-103.

 

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