Open Access Peer-reviewed Research Article

Motion-core assistive tools using pervasive embedded intelligence

Main Article Content

S.M. Namal Arosha Senanayake corresponding author


Real-time human movement monitoring anywhere at any time is time critical depending on core human motion activities, in particular nation’s valuable asserts; athletes and soldiers considered as reference standard of any society. Light weight wearable technologies are the key measurements and instruments system integrated to develop human motion-core assistive tools (MAT) using pervasive embedded intelligence. Unlike many existing motion analysis models, motion-core models are based on domain specific data service architectures beyond cloud technologies using inner data structures and data models created. Four layered micro system architecture that consists of sensing, networking, service and Motion-core IoT (MIoT) is proposed. Knowledge base was designed as a distributed and networked data center based on transient and resident data addressing modes in order to guarantee the secure data accessing, propagating, visualizing and control between these two modes of operations. While transient data change and avail in relevant clouds storages, corresponding resident data and processed data retain inside local servers or/and private clouds. Data mapping and translation techniques are applied for the formation of complete motion-core data packet related to the test subject under consideration. Thus, hybrid MIoT system is  developed using 3D decision fusion models which are the internationally quantifiable standards for assessing human motion set by trainers, coachers, physiotherapists and orthopedics. MIoT built as motion-core assistive tools have been tested for rehabilitation monitoring, injury prevention and performance optimization of athletes, soldiers, and general public. The hybrid system introduced in this work is novel and proves lower down the latency and connectivity independence by allowing human movement analysis during daily active lifestyle.

light weight wearable technologies, micro system architecture, motion-core IoT (MIoT), transient data, resident data, 3D decision fusion

Article Details

Supporting Agencies
National Institute of Information and Communications Technology, Samsung Asia Pte Ltd, University of Brunei
How to Cite
Senanayake, S. N. (2021). Motion-core assistive tools using pervasive embedded intelligence. Advances in Computers and Electronics, 2(1), 10-21.


  1. Gravina R, Alinia P, Ghasemzadeh H, et al. “Multi-sensor fusion in body sensor networks: State-ofthe- art and research challenges. Information Fusion, 2017, 35: 68-80.
  2. Ji X and Liu H. Advances in View-Invariant Human Motion Analysis: A Review. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2010, 40(1): 13-24.
  3. Mariani B, Jim´enez MC, Vingerhoets FJG, et al. On-Shoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson’s Disease. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 155-158.
  4. Kruk E and Reijne MM. Accuracy of human motion capture systems for sport applications; state-ofthe- art review, European Journal of Sport Science, 2018, 18(6): 806-819.
  5. Liu S, Zhang J, Zhang Y, et al. A wearable motion capture device able to detect dynamic motion of human limbs. Nat Commun, 2020, 11: 5615.
  6. Alahakone AU and Senanayake SMNA. A Real-Time System with Assistive Feedback for Postural Control in Rehabilitation. IEEE/ASME Transactions on Mechatronics, 2010, 15(4): 226-233.
  7. Senanayake SMNA, Kadir NH, Suhaimi MSAB, et al. Master-Slave IoT for Active Healthy Life Style. Proceedings of 12th IEEE Conference on Human System Interaction, 978-1-7281-3980-7/19/$31.00 ©2019 IEEE, 2019, 155-161.
  8. Senanayake C, Senanayake SMNArosha. A Computational Method for Reliable Gait Event Detection and Abnormality Detection for Feedback in Rehabilitation, Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis, 2011, 14(10): 863-874.
  9. Hewett TE, Stasi SLD and Myer GD. American Journal of Sports Medicine, 2012, 216-224.
  10. Lauer R, Smith BT and Betz RR. Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy. IEEE Trans Biomed Eng. 52: 1531-1540.
  11. Senanayake ASMN and Wulandari P. Soft Real Time Data Driven IoT for Knee Rehabilitation, 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), Sydney, Australia. 2020, 1-7.
  12. Senanayake SMNA, Malik OA, Iskandar PM, et al. A Knowledge-Based Intelligent Framework for Anterior Cruciate Ligament Rehabilitation Monitoring. Journal of Applied Soft Computing (impact factor 2.140), Elsevier, 2014, 20: 127-141.
  13. Munro BJ, et al. The intelligent knee sleeve: a wearable biofeedback device. Sens. Actuators B Chem, 2008, 131(2): 541-547.
  14. Khudson D. Applying biomechanics in sports and rehabilitation, Chapter 12, in Fundamentals of Biomechanics, 2007, 245.
  15. Gouwanda D and Senanayake SMNA. Periodical gait asymmetry assessment using real-time wireless gyroscopes gait monitoring system. Journal of Medical Engineering & Technology, 2011, 35(8): 432-440.
  16. Sharma S, Chen K and Sheth A. Toward Practical Privacy-Preserving Analytics for IoT and Cloud- Based Healthcare Systems. IEEE Internet Computing, 2018, 22(2): 42-51.