MSTCN: A multiscale temporal convolutional network for user independent human activity recognition [version 2; peer review: 2 approved, 1 approved with reservations]

• Published in: F1000 research Vol. 10; p. 1261
• Main Authors: Raja Sekaran, Sarmela, Pang, Ying Han, Ling, Goh Fan, Yin, Ooi Shih
• Format: Journal Article
• Language: English
• Published: London Faculty of 1000 Ltd 2022; F1000 Research Limited; F1000 Research Ltd
• Subjects: Classification; Deep learning; dilated convolution; eng; Engineering; human activity recognition; Long short-term memory; Neural networks; one-dimensional inertial sensor; smartphone; Smartphones; Support vector machines; temporal convolutional network; Temporal variations; Time series; one-dimensional inertial sensor; smartphone; dilated convolution; human activity recognition; temporal convolutional network
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.73175.2

Background: In recent years, human activity recognition (HAR) has been an active research topic due to its widespread application in various fields such as healthcare, sports, patient monitoring, etc. HAR approaches can be categorised as handcrafted feature methods (HCF) and deep learning methods (DL). HCF involves complex data pre-processing and manual feature extraction in which the models may be exposed to high bias and crucial implicit pattern loss. Hence, DL approaches are introduced due to their exceptional recognition performance. Convolutional Neural Network (CNN) extracts spatial features while preserving localisation. However, it hardly captures temporal features. Recurrent Neural Network (RNN) learns temporal features, but it is susceptible to gradient vanishing and suffers from short-term memory problems. Unlike RNN, Long-Short Term Memory network has a relatively longer-term dependency. However, it consumes higher computation and memory because it computes and stores partial results at each level. Methods: This work proposes a novel multiscale temporal convolutional network (MSTCN) based on the Inception model with a temporal convolutional architecture. Unlike HCF methods, MSTCN requires minimal pre-processing and no manual feature engineering. Further, multiple separable convolutions with different-sized kernels are used in MSTCN for multiscale feature extraction. Dilations are applied to each separable convolution to enlarge the receptive fields without increasing the model parameters. Moreover, residual connections are utilised to prevent information loss and gradient vanishing. These features enable MSTCN to possess a longer effective history while maintaining a relatively low in-network computation. Results: The performance of MSTCN is evaluated on UCI and WISDM datasets using a subject independent protocol with no overlapping subjects between the training and testing sets. MSTCN achieves accuracies of 97.42 on UCI and 96.09 on WISDM. Conclusion: The proposed MSTCN dominates the other state-of-the-art methods by acquiring high recognition accuracies without requiring any manual feature engineering.