ActiWise: Insight on Human Activity Recognition Using Deep Learning Approaches

ActiWise: Insight on Human Activity Recognition Using Deep Learning Approaches

Danthuluru Sri Datta Manikanta Varma, Dubbaka Gayatri Devi, Geethika Mandava and Nagulapalli Girish Babu ,

In the guidance of B.Renuka Sai (Assistant Professor )

Department of Computer Science and Engineering in Artificial Intelligence-Machine Learning (CSM),

Raghu Institute of Technology.

Email: 203j1a4210@raghuinstech.com , 203j1a4212@raghuinstech.com, 203j1a4215@raghinstech.com, 203j1a4241@raghuinstech.com

Abstract- In this study, we investigate the fusion of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for human activity recognition (HAR). By integrating hierarchical spatial features extracted by CNNs with LSTM networks' temporal modelling capabilities, our approach excels in discerning nuanced patterns from raw sensor data collected via wearable devices. Through rigorous experimentation and validation, our CNN+LSTM model demonstrates robust performance in accurately classifying a spectrum of human activities. This research advances HAR methodologies, shedding light on the synergistic interplay between spatial and temporal modelling in activity recognition, with implications across healthcare, sports analytics, and human-computer interaction domains.

Index Terms- Human activity recognition, deep learning, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM).