Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks

Khaleel Asyraaf Mat Sanusi; Daniele Di Mitri; Bibeg Limbu; Roland Klemke

doi:10.3390/s21093121

Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks

Sensors (Basel). 2021 Apr 30;21(9):3121. doi: 10.3390/s21093121.

Authors

Khaleel Asyraaf Mat Sanusi¹, Daniele Di Mitri², Bibeg Limbu³, Roland Klemke^{1

4}

Affiliations

¹ Cologne Game Lab, TH Köln, 51063 Cologne, Germany.
² DIPF|Leibniz Institute for Research and Information in Education, 60323 Frankfurt, Germany.
³ Leiden Delft Erasmus-Center for Education and Learning, Technical University Delft, 2628 CD Delft, The Netherlands.
⁴ Technology-Enhanced Learning & Innovation, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands.

Abstract

Beginner table-tennis players require constant real-time feedback while learning the fundamental techniques. However, due to various constraints such as the mentor's inability to be around all the time, expensive sensors and equipment for sports training, beginners are unable to get the immediate real-time feedback they need during training. Sensors have been widely used to train beginners and novices for various skills development, including psychomotor skills. Sensors enable the collection of multimodal data which can be utilised with machine learning to classify training mistakes, give feedback, and further improve the learning outcomes. In this paper, we introduce the Table Tennis Tutor (T3), a multi-sensor system consisting of a smartphone device with its built-in sensors for collecting motion data and a Microsoft Kinect for tracking body position. We focused on the forehand stroke mistake detection. We collected a dataset recording an experienced table tennis player performing 260 short forehand strokes (correct) and mimicking 250 long forehand strokes (mistake). We analysed and annotated the multimodal data for training a recurrent neural network that classifies correct and incorrect strokes. To investigate the accuracy level of the aforementioned sensors, three combinations were validated in this study: smartphone sensors only, the Kinect only, and both devices combined. The results of the study show that smartphone sensors alone perform sub-par than the Kinect, but similar with better precision together with the Kinect. To further strengthen T3's potential for training, an expert interview session was held virtually with a table tennis coach to investigate the coach's perception of having a real-time feedback system to assist beginners during training sessions. The outcome of the interview shows positive expectations and provided more inputs that can be beneficial for the future implementations of the T3.

Keywords: activity recognition; learning analytics; multimodal data; neural networks; psychomotor learning; sensors; table tennis.