COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR HUMAN ACTIVITY RECOGNITION
- Dept. of Manufacturing and Industrial Management, COEP Technological University Pune, India.
- Dept. of Instrumentation and Control, COEP Technological University Pune, India.
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Human Activity Recognition (HAR) using wearable sensor data is a cornerstone of mobile health and context- aware computing. While deep learning has significantly advanced HAR accuracy the computational demands of complex architectures often conflict with the limited resources of edge devices like smartphones and wearables . This creates a critical trade off between predictive performance and practical deployability. This paper presents a systematic comparative analysis of five distinct deep learning architectures: a baseline Multi-Layer Perceptron (MLP), a 1D Convolutional Neural Network (1D-CNN), a Long Short-Term Memory (LSTM) network, a hybrid CNN-LSTM model , and a Transformer-based model . We evaluate these models on the public UCI-HAR dataset, focusing not only on classification accuracy and F1-score but also on crucial efficiency metrics: model size (parameters) and inference latency. Our findings reveal that while the Transformer achieves the highest F1- score (0.931), its substantial computational cost makes it less suitable for real-time edge applications. The hybrid CNN-LSTM architecture emerges as the most balanced solution, delivering competitive accuracy (0.925 F1-score) with significantly lower latency and a more compact model size. This study provides a clear, data- driven framework for selecting appropriate HAR models based on specific deployment constraints.
[Vaishnavi Talnikar, Girish Lakhekar and Hrishikesh Jadhav (2025); COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR HUMAN ACTIVITY RECOGNITION Int. J. of Adv. Res. (Jul). 90-97] (ISSN 2320-5407). www.journalijar.com
Dept. of Manufacturing and Industrial Management, COEP Technological University Pune, India
India