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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Cognitive IoT-Based Smart Fitness Diagnosis and Recommendation System Using a Three-Dimensional CNN with Hierarchical Particle Swarm Optimization
verfasst von : Chalumuru Suresh, M. Ravikanth, B. Srivani, Thatavarti Satish
Erschienen in: Smart Sensors for Industrial Internet of Things
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Abstract
Due to the worldwide increase in economic development, fitness centers are rising quickly all over the globe. In this view, people require methodical and realistic supervision for building their body. At the same time, Cognitive Internet of Things (CIoT) is capable of learning, sensing, decision-making, and transferring intelligence to humans. Recently, several methods have been evolved to address the fitness and health, where the wearable fitness models let down the market rate. In this paper, a Cognitive IoT-Based Smart Fitness Diagnosis and Recommendation System using a three-dimensional convolutional neural network (CNN) with hierarchical particle swarm optimization (PSO) algorithm has been presented. The proposed model is applied to observe the health statuses of exercisers. The system offers proper supervision to the exercisers. A set of experiments takes place, and the simulation results highlight the betterment of the proposed model.