Badminton is a type of racquet sport that is played using racquets to strike a shuttlecock within a netted rectangular court, and it is one of the most popular sport in Malaysia. In this paper, the team expanded their previous adaptive novel lossless compact view invariant compression technique, named adaptive range of movement index (RoMI) by embedding an intelligent mapping algorithm. Badminton players are broadly classified into two handedness groups, which are left-handed players and right-handed players. The adaptive RoMI technique in the previous module is capable of identifying the labels of normalized RoMI and perform adaptive mapping for badminton players of different handedness. However, the mechanism requires manual judgement from humans to select the type of handedness. This technique will increase the error rate and effort of importing data properties with incorrect manual handedness input or data stream from different handedness datasets of badminton players. Thus, the proposed intelligent adaptive RoMI technique enables the analyzing system to outperform the previous module by detecting the labels of normalized RoMI automatically and by performing intelligent mapping. The motion analysis sys tem will then become much more efficient, intelligent hence simplifying data collection and the handedness invariant benchmarking approach.
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