Validation of a Low-cost Inertial Exercise Tracker

Sarvenaz Salehi, Didier Stricker

Abstract

This work validates the application of a low-cost inertial tracking suit, for strength exercise monitoring. The procedure includes an offline processing for body-IMU calibration, online tracking and identification of lower body motion. We proposed an optimal movement pattern of the body-IMU calibration method from our previous work. Here in order to reproduce real extreme situations, we used data from different types of movements with high acceleration intensity. For such movements, an optimal orientation tracking approach is introduced which requires no accelerometer measurements and it thus minimizes error of existing outliers. The online tracking algorithm is based on an extended Kalman filter(EKF), which estimates the position of upper and lower legs with respect to the pelvis along with hip and knee joint angles. This method benefits from the estimated values in calibration process i.e. joint axes and positions, as well as biomechanical constraints of lower body. Therefore it requires no aiding sensors such as magnetometer. The algorithm was evaluated using optical tracker for two types of exercises:squat and abd/adduction which resulted average Root Mean Square Error(RMSE) of 9cm. Additionally, this work presents a personalized exercise identification approach, where an online template matching algorithm is applied and optimised using Zero Velocity Crossing(ZVC) for feature extraction. This results reducing the execution time to 93% and improving the accuracy to 33%.

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Paper Citation


in Harvard Style

Salehi S. and Stricker D. (2020). Validation of a Low-cost Inertial Exercise Tracker.In Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-403-9, pages 97-104. DOI: 10.5220/0008965800970104


in Bibtex Style

@conference{sensornets20,
author={Sarvenaz Salehi and Didier Stricker},
title={Validation of a Low-cost Inertial Exercise Tracker},
booktitle={Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2020},
pages={97-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008965800970104},
isbn={978-989-758-403-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Validation of a Low-cost Inertial Exercise Tracker
SN - 978-989-758-403-9
AU - Salehi S.
AU - Stricker D.
PY - 2020
SP - 97
EP - 104
DO - 10.5220/0008965800970104