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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Nurul Fathiah Ghazali
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Deep learning in sport video analysis: a review Keerthana Rangasamy; Muhammad Amir As’ari; Nur Azmina Rahmad; Nurul Fathiah Ghazali; Saharudin Ismail
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14730

Abstract

Sport is a competitive field, where it is an element of measurement for a countries development.  Due to this reason, sport analysis has become one of the major contribution in analysing and improving the performance level of an athlete.  Video-based modality has become a crucial tool used in sport analysis by coaches and performance analysis.  There were wide variety of techniques used in sport video analysis.  The main purpose of this review paper is to compare and update review between traditional handcrafted approach and deep learning approach in sport video analysis based on human activity recognition, overview of recent study in video based human activity recognition in sport analysis and finally concluded with future potential direction in sport video analysis.
Deep stair walking detection using wearable inertial sensor via long short-term memory network Gan Wei Nie; Nurul Fathiah Ghazali; Norazman Shahar; Muhammad Amir As'ari
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.522 KB) | DOI: 10.11591/eei.v9i1.1685

Abstract

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.
Co-Authors Gan Wei Nie Keerthana Rangasamy Muhammad Amir As'ari Muhammad Amir As’ari Norazman Shahar Nur Azmina Rahmad Saharudin Ismail