IoT for Movement Recognation and Prehabilitation Support System

Internet of Things Gateway Edge for Movement Monitoring in a Smart Healthcare System

“…Our previous work on WSD movement detection capabilities in different body locations found that the ankle was the best location for the accurate identification of patient movements during physical activities used in cancer prehabilitation programmes [1,10]. The WSD size, power consumption, computation functionality requirements, data storage, and communication requirements have been discussed in detail in a previous studies [1,20,21]. For example, low-power 3D-accelerometer Microduino sensors have been integrated within different supported electronic boards.…”

“…The gateway (RPi) functionalities are designed and implemented to support a mixedmode prehabilitation program [11,21]. This program is a hybrid model that uses key components of existing prehabilitation models (e.g., exercise testing and an initial supervised exercise session) but also integrates wearable technology and the IoT to enhance exercise engagement in non-supervised environments.…”

“…This program is a hybrid model that uses key components of existing prehabilitation models (e.g., exercise testing and an initial supervised exercise session) but also integrates wearable technology and the IoT to enhance exercise engagement in non-supervised environments. A conceptual IoT system aims to support this model by adopting the boundaries and key elements of a mixed-mode prehabilitation program supported by mathematical formulas for converting physical motion into a numerical code [21]. Therefore, the features of each component of the IoT (WSD, RPi, and cloud) are required to support the pre-operative prehabilitation program requirements.…”

“…Electronics 2023, 12, x FOR PEER REVIEW 6 of 14 numerical code [21]. Therefore, the features of each component of the IoT (WSD, RPi, and cloud) are required to support the pre-operative prehabilitation program requirements.…”

“…The presence of this overlap can lead to the inaccurate recognition of certain activities, potentially resulting in miscalculations or the omission of credited efforts. Several studies have utilised machine learning and deep learning methodologies to address this concern [20,21]. The current study used specialised AI techniques (specifically those based on knowledge bases and logical approaches) to improve activity recognition and accurately quantify physical effort.…”