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Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm

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A Correction to this article was published on 12 January 2023

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Abstract

Capturing time and frequency relationships of time series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use Continuous Wavelet Transform to encode the time series of sensor data as multi-channel images. Then, we utilize a Spatial Attention-aided Convolutional Neural Network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: Mutual Information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the Genetic Algorithm (GA). The K-Nearest Neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here https://github.com/apusarkar2195/HAR_WaveletTransform_SpatialAttention_FeatureSelection.

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Data Availability Statement

All the datasets analyzed during the current study can be downloaded using the following links UCI-HAR—https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones

WISDM—https://www.cis.fordham.edu/wisdm/dataset.php

MHEALTH—http://archive.ics.uci.edu/ml/datasets/mhealth+dataset

PAMAP2—https://archive.ics.uci.edu/ml/datasets/pamap2+physical+activity+monitoring

HHAR—http://archive.ics.uci.edu/ml/datasets/heterogeneity+activity+recognition.

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Acknowledgements

We are thankful to the Center for Microprocessor Applications for Training Education and Research (CMATER) research laboratory of the Computer Science and Engineering Department, Jadavpur University, Kolkata, India, for providing infrastructural support.

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  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Apu Sarkar, S. K. Sabbir Hossain & Ram Sarkar

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Sarkar, A., Hossain, S.K.S. & Sarkar, R. Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm. Neural Comput & Applic 35, 5165–5191 (2023). https://doi.org/10.1007/s00521-022-07911-0

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