The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries. On location tracking, a mobile and cloud-based Indoor Location Platform (ILP) which incorporates multimodal localization means and assisted by other sensor fusion techniques is developed. In this platform, GPS and non-GPS positioning systems such as Wi-Fi/BLE fingerprinting, IR-UWB positioning, sensor-based and a hybrid of these localization techniques are adopted to provide continuous tracking of the subject of interest in both indoor and outdoor environments. Extensive trials have been carried out in not only laboratory testbeds, but also in factories and other commercial premises. On health or lifestyle monitoring, harvesting of motion data and context reasoning, using the IntelliHealth Solutions were carried out to assess, monitor and to provide feedback on a person’s lifestyle. An intelligent knowledge base is formed and this enables the development of various transient wearable health OS solutions. In this project, wearable motion interfacing and reasoning devices for general public are developed to support trials and data collections involving people from public. 


The objective of this work is to provide biofeedback

visualization of natural daily human activities for fitness

development and performance enhancement using Master-Slave

IoT devices. Human test subjects with average age ± 38.6 were

used to obtain the cadence of human walking in order to determine

their Actual Health Status (AHS) and Transient Health Status

(THS). Primary parameters experimented were Single Left

Support (SLS) and Single Right Support (SRS) due to double

support in the cadence to be vanished during brisk walking and

running. This project utilized ZeBlok smart shoes equipped with

4 inertia measurement units (IMUs) on the toe, left ball, right ball

and heel and the linear accelerometer inside the insole interfaced

to the Bio-Informatics Cloud, deep neural network (DNN) tools

and Samsung smart watches as IoT devices for biofeedback

visualization in near real time. Master-Slave IoT hardware and

software integration using cloud computing is built in order to

establish knowledge base on Master-IoT device which provides

THS of cadence monitored by health professionals

(physiotherapist, doctors, trainers, athletes and soldiers). Slave-

IoT is built for AHS during natural walking in order to produce

biofeedback visualization report for health professionals and test

subject under consideration as personalized healthcare solution.

Thus, data availability and data redundancy provided using

Master-Slave IoT devices facilitate to obtain optimal solutions

during biofeedback visualization.

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