ELISS compliant IPPT training system (motion sensors)

• Main Author: Tan, Brighten Yan Hui
• Other Authors: Oh Hong Lye
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/156493
• Institution: Nanyang Technological University

The Individual Physical Proficiency Test (IPPT) is a mandatory requirement for all eligible National Service (NS) men in Singapore. Currently, IPPT comprises of 3 main stations, the 2.4 kilometres run, as well as the static stations which are the push-up station, and the sit-up station. The static stations are conducted with the help of the Electronic IPPT Scoring System (ELISS), which allows for an automated evaluation process that is accurate. However, the ELISS machines are not available for public use and thus there is no available alternative that NS men can train to the standards of the ELISS. With the help of sensors and a mobile application (app), this problem can be solved. Motion recognition can be utilized with sensors by implementing an Attitude and Heading Reference System (AHRS). By placing sensors with motion recognition on the user, we would be able to detect if a repetition is completed. Using a mobile app, we can utilize Bluetooth Low Energy (BLE) to communicate with the sensor board and provide a user-friendly interface in evaluating their IPPT static performance. For our project, we used Adafruit Feather nRF52840 Sense as the sensor board. We first started by testing the feasibility of using the sensor board to detect repetitions done by the user for IPPT static stations. Once we established that it was possible, we started to work on the mobile app for a user interface that would be easy for the user to monitor their performance. User testing was conducted to gather feedback of the whole setup and improvements were made. The goal of this project is to allow users to train for IPPT by themselves with the standards adhering to the ELISS using just 2 sensor boards and a mobile app. The result from this would determine if the sensor boards coupled with a mobile app can be a viable solution for the training of IPPT static stations.