Realtime Gym AI

Realtime Gym Ai

Bhushan Bari, Shital Khairnar, Anand Suryawanshi, Ankit Tamboli

Prof.  Sunil M. Kale

Department of computer Engineering of Sandip Institute of  Technology and research Centre, Nashik

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ABSTRACT:                                                                                                                                                                          

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms.

 Key Words: Posture detection, Machine Learning, Deep Learning.