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Stride segmentation of inertial sensor data using statistical methods for different walking activities

Published online by Cambridge University Press:  27 December 2021

Rahul Jain Open the ORCID record for Rahul Jain [Opens in a new window] ,
Vijay Bhaskar Semwal  and
Praveen Kaushik
Rahul Jain*
Affiliation:
Maulana Azad National Institute of Technology, Bhopal, India.
Vijay Bhaskar Semwal
Affiliation:
Maulana Azad National Institute of Technology, Bhopal, India.
Praveen Kaushik
Affiliation:
Maulana Azad National Institute of Technology, Bhopal, India.
*
*Corresponding author. E-mail: [email protected].
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Abstract

Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.

Keywords

stride segmentationhuman activity recognitionaccelerometrygait parameter estimationgait cycleinertial measurement unitautocorrelation functionwearable sensorsIoTedge computingtinyML
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2567 - 2580
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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