Universiti Teknologi Malaysia Institutional Repository

Deep stair walking detection using wearable inertial sensor via long short-term memory network

Gan, Wei Nie and Ghazali, Nurul Fathiah and Shahar, Norazman and As'ari, Muhammad Amir (2020) Deep stair walking detection using wearable inertial sensor via long short-term memory network. Bulletin of Electrical Engineering and Informatics, 9 (1). pp. 238-246. ISSN 2089-3191


Official URL: http://dx.doi.org/10.11591/eei.v9i1.1685


This paper proposes a stair walking detection via Long Short-Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.

Item Type:Article
Uncontrolled Keywords:Deep learning, LSTM network
Subjects:Q Science > QP Physiology
Divisions:Biosciences and Medical Engineering
ID Code:90862
Deposited By: Widya Wahid
Deposited On:31 May 2021 21:22
Last Modified:31 May 2021 21:22

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