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Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Vishwanath Bijalwan (Institute of Technology, Gopeshwar, Chamoli, India)
Vijay Bhaskar Semwal (Maulana Azad National Institute of Technology, Bhopal, India)
Vishal Gupta (ICFAI University Dehradun, Dehradun, India)

Industrial Robot

ISSN: 0143-991x

Article publication date: 16 August 2021

Issue publication date: 3 January 2022

692

Abstract

Purpose

This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.

Design/methodology/approach

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.

Findings

The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.

Practical implications

This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.

Originality/value

The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

Keywords

Acknowledgements

This paper is an extended version of work carried out under project funded by SERB, DST Government of India under the schema of Early Career Award, DST No. ECR/2018/000203 dated on 04/06/2019 and CRS Project of Veer Madho Singh Bhandari Uttarakhand Technological University Dehradun.

The author(s) would like to thank all participants who have actively participated in data capturing for different walking activities. The author(s) would like to express thanks to Human Locomotion Analysis Laboratory of the Institute of Technology Gopeshwar, Uttarakhand and Human Motion Capturing and Analysis Unit of MANIT Bhopal for providing the opportunity to collect data and providing the computing facility.

The author(s) would also like to thank Uttarakhand Technical University Dehradun for providing funding under collaborative research scheme grant. At last we would like to thank our student Shivam Pandey, CSE (fourth year), Institute of Technology Gopeshwar and Dr Vinod Pundir for helping us in data collection of 30 subjects. The data set is also available publicly for research purposes. One can download from here: https://sites.google.com/view/vsemwal/research/human-activities-gait-data-set

Citation

Bijalwan, V., Semwal, V.B. and Gupta, V. (2022), "Wearable sensor-based pattern mining for human activity recognition: deep learning approach", Industrial Robot, Vol. 49 No. 1, pp. 21-33. https://doi.org/10.1108/IR-09-2020-0187

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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