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Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks
(2021)
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.
How Digital Strategy and Management Games Can Facilitate the Practice of Dynamic Decision-Making
(2020)
This paper examines how digital strategy and management games that have been initially designed for entertainment can facilitate the practice of dynamic decision-making. Based on a comparative qualitative analysis of 17 games—organized into categories derived from a conceptual model of decision-making design—this article illustrates two ways in which these games may be useful in supporting the learning of dynamic decision-making in educational practice:
(1) Players must take over the role of a decider and solve situations in which players must pursue different conflicting goals by making a continuous series of decisions on a variety of actions and measures; (2) three of the features of the games are considered to structure players’ practice of decision-making and foster processes of learning through the curation of possible decisions, the offering of lucid feedback and the modification of time. This article also highlights the games’ shortcomings, from an educational perspective, as players’ decisions are restricted by the numbers of choices they can make within the game, and certain choices are rewarded more than others. An educational application of the games must, therefore, entail a critical reflection of players’ limited choices inside a necessarily biased system.
Sensors can monitor physical attributes and record multimodal data in order to provide feedback. The application calligraphy trainer, exploits these affordances in the context of handwriting learning. It records the expert’s handwriting performance to compute an expert model. The application then uses the expert model to provide guidance and feedback to the learners.
However, new learners can be overwhelmed by the feedback as handwriting learning is a tedious task. This paper presents the pilot study done with the calligraphy trainer to evaluate the mental effort induced by various types of feedback provided by the application. Ten participants, five in the control group and five in the treatment group, who were Ph.D. students in the technology-enhanced learning domain, took part in the study. The participants used the application to learn three characters from the Devanagari script. The results show higher mental effort in the treatment group when all types of feedback are provided simultaneously. The mental efforts for individual feedback were similar to the control group. In conclusion, the feedback provided by the calligraphy trainer does not impose high mental effort and, therefore, the design considerations of the calligraphy trainer can be insightful for multimodal feedback designers.