The Application of Inertial Measurement Units and Wearable Sensors to Measure Selected Physiological Indicators in Archery


  • Universiti Malaysia Pahang, Innovative Manufacturing Mechatronics and Sports Lab (iMAMs), Faculty of Manufacturing Engineering, Pahang, 26600, Malaysia
  • Universiti Sultan Zainal Abidin, Faculty of Applied Social Sciences, Terengganu, 21300, Malaysia
  • University Malaysia Pahang, Faculty of Mechanical Engineering, Pahang, 26600, Malaysia
  • National Defense University of Malaysia, Faculty of Medicine and Defense Health, Kuala Lumpur, Kem Sungai Besi, 57000, Malaysia


The requirement for objective techniques to observe physical action in its distinctive measurements has prompted the improvement and broad utilisation of motion sensors called Inertial Measurement Units (IMUs), which measures bodily movements. However, although these sensors have been utilised to measure postural balance in both clinical and some specific sports, little or no effort have been made to apply these sensors to the measurement of other physiological indicators in the sport of archery. This study aims to ascertain the postural balance, hand movement, muscular activation as well as heart rate of an archer. An archer was instructed to perform two balance standings, two hand movements and his muscular activations of flexor and extensor digitorum, as well as heart rate, were recorded using Shimmer sensors. The mean movement of x and y-axis of the archer was used to correlate with the Pearson correlation for testing the validity of the sensors. Kolmogorov/Smirnov test was utilised to measure the reliability of the sensors over test re-test in two different tests. The coefficient of determination indicates some positive and negative significant relationships between some indicators. The Kolmogorov/Smirnov test re-test reveals a significant difference between all the indicators in both tests A and B, p < 0.001. The archer was able to present two types of postural standings and exhibited two hands movement while holding the bow. However, his heart rate demonstrated some variability during the executions of the movement in both tests. Thus, it could be concluded that the fusion sensors are reliable in measuring the aforementioned physiological indicators.


Archery, Inertial measurements units, Movement analysis, Physiological indicators, Wearable sensors

Subject Collection


Subject Discipline


Full Text:


Ahmadi A, Mitchell E, Destelle F, Gowing M, OConnor NE, Richter C, Moran K. Automatic activity classification and movement assessment during a sports training session using wearable inertial sensors. 2014 11th IEEE International Conference on Wearable and Implantable Body Sensor Networks; 2014 Jun. p. 98–103.

Giggins O, Kelly D, Caulfield B. Evaluating rehabilitation exercise performance using a single inertial measurement unit. Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering; 2013 May; p. 49–56. Available from:

Pernek I, Hummel KA, Kokol P. Exercise repetition detection for resistance training based on smartphones. Personal and Ubiquitous Computing. 2013; 17(4):771–82. Available from:

Rawson ES, Walsh TM. Estimation of resistance exercise energy expenditure using accelerometry. Med Sci Sports Exerc. 2010; 42(3):622–8. PMid:19952824. Available from:

Kavanagh JJ, Menz HB. Accelerometry: A technique for quantifying movement patterns during walking. Gait Posture. 2008; 28(1):1–15. PMid:18178436. Available from:

Zhang M, Sawchuk AA. A customizable framework of body area sensor network for rehabilitation. 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologi; 2009 Nov. Available from:

Michahelles F, Schiele B. Sensing and monitoring professional skiers. IEEE Pervasive Computing. 2005; 4(3):40–5. Available from:

Ghasemzadeh H, Loseu V, Jafari R. Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf. Journal of Ambient Intelligence and Smart Environments. 2009; 1(2):173–84,

Lin JF, Kulić D. Human pose recovery using wireless inertial measurement units. Physiol Meas. 2012; 33(12):2099–115. PMid:23174667. Available from:

Rocchi L, Chiari L, Cappello A, Horak FB. Identification of distinct characteristics of postural sway in Parkinson's disease: A feature selection procedure based on principal component analysis. Neurosci Lett. 2006; 394(2):140–5. PMid:16269212. Available from:

Chen KY, Bassett DR. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc. 2005; 37(11 Suppl):S490–500. PMid:16294112. Available from:

Saunders JB, Inman VT, Eberhart HD. The major determinants in normal and pathological gait. J Bone Joint Surg Am. 1953; 35-A(3):543–58. PMid:13069544. Available from:

Altini M, Penders J, Roebbers H. An Android-based body area network gateway for mobile health applications. Wireless Health. 2010; 188–9.

Yi WJ, Jia W, Saniie J. Mobile sensor data collector using Android smartphone. IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS); 2012 Aug. 956–9.

Available from:

Abdullah MR, Musa RM, Maliki ABHM, Kosni NA, Suppiah PK. Development of tablet application based notational analysis system and the establishment of its reliability in soccer. J Physical Edu Sport. 2016; 16(3):951–7.

Musa RM, Abdullah MR, Maliki AB, Kosni NA, Haque M. The application of principal components analysis to recognize essential physical fitness components among youth development archers of terengganu, Malaysia. Indian Journal of Science and Technology. 2016; 11(9); 44–6. Available from:


  • There are currently no refbacks.