IoT-Inspired Framework for Athlete Performance Assessment in Smart Sport Industry
Munish Bhatia
Abstract
Smart sports industry presents a novel vision with immense potential for effective decision-making services. Conspicuously, this research presents an Internet of Things-Fog computing inspired game-theoretic decision-making model for provisioning in-depth analysis of athlete performance in a time-sensitive manner. Specifically, sport-oriented parameters are acquired using smart devices using an energy-efficient mechanism, which is further classified and analyzed in terms of quantifiable parameters of the Probability-of-Performability (PoP) and form index value (FIV). Finally, a game-theoretic mathematical model has been proposed between the sports athlete and monitoring officials for effective decision-making services. For validation purposes, the simulation was performed over a challenging data set of four cricket players comprising of 80 120 data instances. Comparative analysis was performed with numerous state-of-the-art analytical techniques. Based on the simulation results, the presented model was able to register enhanced performance in terms of sensitivity (93.14%), specificity (93.97%), precision (94.56%), and f-measure (91.69%). Moreover, improved battery efficiency (25%) and stability (92.79%) were registered for the proposed technique.