Master-Slave IoT for Active Healthy Life Style

Senanayake, Smn Arosha; Kadir, Nursyuhada Hj; Suhaimi, Muhammad Syaiful Amri bin; Sasaki, Minoru

doi:10.1109/hsi47298.2019.8942640

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…But, few core human motion methods were addressed for real-time human movement monitoring anywhere at any time considered as time critical [6]. The necessity of biofeedback visualization and its control during actual human activities in real-time matters when it comes to nation's asserts; athletes, players and soldiers during training regime and international games [4,7].…”

Section: Introductionmentioning

confidence: 99%

Motion-core assistive tools using pervasive embedded intelligence

Senanayake

2021

Adv Comput Electron

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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.

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Section: Introductionmentioning

confidence: 99%

Motion-core assistive tools using pervasive embedded intelligence

Senanayake

2021

Adv Comput Electron

View full text Add to dashboard Cite

show abstract

Master-Slave IoT for Active Healthy Life Style

Cited by 1 publication

References 6 publications

Motion-core assistive tools using pervasive embedded intelligence

Motion-core assistive tools using pervasive embedded intelligence

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