Open Access

Peer-reviewed

Research Article

Main Article Content

S.M. Namal Arosha Senanayakecorresponding author

Abstract

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.

Keywords
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. https://doi.org/10.25082/ACE.2020.01.002

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