Upper Limbs Extension and Flexion Angles Calculation and Visualization Using Two Wearable Inertial Measurement Units

Main Article Content

Chun Ho Chen
Kok Beng Gan
Noor Azah Abd Aziz

Abstract

Human motion analysis is widely used in many fields such as physical rehabilitation, athlete training, health status diagnosis and many others. Range of motion (ROM) is an important parameter to evaluate limb’s performance during activity of daily. Goniometer is a device often used by physiotherapist to evaluate and analyse the ROM of individual’s limb movement. The objective of this works is to develop a system to measure ROM using multiple Inertial Measurement Units (IMU) and transfer data to host computer by using Bluetooth Low Energy (BLE). A software program was develop using Phyton to visualize the motion. In this works, Intel CurieNano board, a six degree of freedom IMU that consist of an accelerometer and a gyroscope was used to calculate the ROM using sensor fusion algorithm. The data from accelerometer and gyroscope were fused using the complementary filter to get the ROM. The motion data was acquired by IMU sensor was sent to a custom program developed in Python through BLE. This custom program displayed the acquired data and visualized the motion in 3D visual model. The IMU sensor was used to measure certain angles at 10°, 30°, 60° and 90° to test its accuracy. The results showed that the angle measurement using IMU sensors has correlation coefficient of 0.9967 where the reference method was goniometer.  The wrist flexion and extension angle have maximum error of 7.49° for flexion and minimum error of 1.14° for extension. The ROM measured using IMU sensor has maximum error of 3.57% compared to goniometer. It showed that the IMU’s ROM measurement method is as good as goniometer.


 


(Manuscript received: 21 March 2022 | Accepted: 6 May 2022 | Published: 8 July 2022)

Article Details

How to Cite
Chen, C. H., Gan, K. B., & Abd Aziz, N. A. (2022). Upper Limbs Extension and Flexion Angles Calculation and Visualization Using Two Wearable Inertial Measurement Units. International Journal on Robotics, Automation and Sciences, 4, 1–7. https://doi.org/10.33093/ijoras.2022.4.1
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Articles

References

OpenStax College. (2013). Anatomy and Physiology. Houston: OpenStax CNX. Retrieved from http://cnx.org/content/col11496/latest/ (accessed on 10 June 2020)

Pinto, R. S., Gomes, N., Radaelli, R., Botton, C. E., Brown, L. E. & Bottaro, M. (2012). Effect of range of motion on muscle strength and thickness. Journal of Strength and Conditioning Research 26(8): 2140–2145.

Gates, D. H., Walters, L. S., Cowley, J., Wilken, J. M. & Resnik, L. (2016). Range of motion requirements for upper limb activities of daily living. American Journal of Occupational Therapy 70(1):7001350010p1-7001350010p10

Trejo, R. L., Vazquez, J. P. G., Ramirez, M. L. G., Corral, L. E. V. & Marquez, I. R. 2017. Hand goniometric measurements using leap motion. 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017, Las Vegas, NV, USA.

Sandall, B. K., Schools, B. C. & Blair, N. (2016). Wearable Technology and Schools: Where are We and Where Do We Go From Here? Journal of Curriculum, Teaching, Learning and Leadership in Education: 1(1):74-83.

Nwaizu, H., Saatchi, R. & Burke, D. (2016). Accelerometer based human joints’ range of movement measurement. 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2016, Prague, Czech Republic.

Hazry, D., Sofian, M. & Azfar, A. Z. (2009). Study of Inertial Measurement Unit Sensor. International Conference on ManMachine Systems ICoMMS (October), Batu Ferringhi, Penang, Malaysia.

Long Tran. (2017). Data Fusion with 9 Degrees of Freedom Inertial Measurement Unit To Determine Object’s Orientation. Retrieved from https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1422&context=eesp (accessed on 10 June 2020)

Akintade & Kehinde. (2017). Comparison of Data Fusion Techniques for Human Knee Joint Range of Motion Measurement Using Inertial Sensors. International Journal of Electronics and Electrical Engineering 5(2): 127–134.

Bennett, C. L., Odom, C. & Ben-Asher, M. (2013). Knee angle estimation based on imu data and artificial neural networks. Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013, Miami. Florida, USA.

Jakob, C., Kugler, P., Hebenstreit, F., Reinfelder, S., Jensen, U., Schuldhaus, D., Lochmann, M., et al. (2013). Estimation of the knee flexion-extension angle during dynamic sport motions using body-worn inertial sensors. BODYNETS 2013 - 8th International Conference on Body Area Networks (April 2015), Brussels, Belgium

Siekkinen, M., Hiienkari, M., Nurminen, J. K. & Nieminen, J. (2012). How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4. 2012 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2012, Paris, France.

Lim, X. Y., Gan, K. B., & Abd Aziz, N. A. (2021). Deep ConvLSTM network with dataset resampling for upper body activity recognition using minimal number of IMU sensors. Applied Sciences (Basel, Switzerland), 11(8), 3543.

Ponvel, P., Singh, D. K. A., Beng, G. K., & Chai, S. C. (2019). Factors affecting upper extremity kinematics in healthy adults: A systematic review. Critical Reviews in Physical and Rehabilitation Medicine, 31(2), 101–123.

Zainal Abidin, S. B., Wan Jusoh, W. N. I., & Beng, G. K. (2018). Kinematic analysis on reaching activity for hemiparetic stroke subjects using simplified video processing method. Malaysian Journal of Fundamental and Applied Sciences, 14(3), 386–390.

Ramlee, M. H., & Gan, K. B. (2017). Function and biomechanics of upper limb in post-stroke patients — a systematic review. Journal of Mechanics in Medicine and Biology, 17(06), 1750099.

Ramlee, M. H., Beng, G. K., Bajuri, N., & Abdul Kadir, M. R. (2018). Finite element analysis of the wrist in stroke patients: the effects of hand grip. Medical & Biological Engineering & Computing, 56(7), 1161–1171.