Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks

• URL: http://arxiv.org/abs/2106.12666v1
• Date: Wed, 23 Jun 2021 21:49:17 GMT
• Title: Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks
• Authors: Anna Nedorubova, Alena Kadyrova, Aleksey Khlyupin
• Abstract summary: Human Activity Recognition (HAR) is a perspective and fast-paced Data Science field. We propose a new workflow to address the HAR problem and evaluate it on the UniMiB SHAR dataset. We succeed to reach 99.26 % accuracy and it is a worthy performance for this problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quite a few people in the world have to stay under permanent surveillance for health reasons; they include diabetic people or people with some other chronic conditions, the elderly and the disabled.These groups may face heightened risk of having life-threatening falls or of being struck by a syncope. Due to limited availability of resources a substantial part of people at risk can not receive necessary monitoring and thus are exposed to excessive danger. Nowadays, this problem is usually solved via applying Human Activity Recognition (HAR) methods. HAR is a perspective and fast-paced Data Science field, which has a wide range of application areas such as healthcare, sport, security etc. However, the currently techniques of recognition are markedly lacking in accuracy, hence, the present paper suggests a highly accurate method for human activity classification. Wepropose a new workflow to address the HAR problem and evaluate it on the UniMiB SHAR dataset, which consists of the accelerometer signals. The model we suggest is based on continuous wavelet transform (CWT) and convolutional neural networks (CNNs). Wavelet transform localizes signal features both in time and frequency domains and after that a CNN extracts these features and recognizes activity. It is also worth noting that CWT converts 1D accelerometer signal into 2D images and thus enables to obtain better results as 2D networks have a significantly higher predictive capacity. In the course of the work we build a convolutional neural network and vary such model parameters as number of spatial axes, number of layers, number of neurons in each layer, image size, type of mother wavelet, the order of zero moment of mother wavelet etc. Besides, we also apply models with residual blocks which resulted in significantly higher metric values. Finally, we succeed to reach 99.26 % accuracy and it is a worthy performance for this problem.

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