TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Mat Sanusi, Khaleel Asyraaf
A1  - Mitri, Daniele Di
A1  - Limbu, Bibeg
A1  - Klemke, Roland
T1  - Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks
JF  - Sensors
N2  - 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.
KW  - Neuronales Netz
KW  - Multimodal Data
KW  - Neural Networks
KW  - Psychomotor Learning
KW  - Table Tennis
KW  - Activity Recognition
KW  - Sensors
KW  - Learning Analytics
KW  - Tischtennis
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-16855
SN  - 1424-8220
SS  - 1424-8220
U6  - https://doi.org/10.3390/s21093121
DO  - https://doi.org/10.3390/s21093121
VL  - 21
IS  - 9
SP  - 18
S1  - 18
PB  - MDPI
ER  -