Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD $\alpha$) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD $\alpha$ system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification.

Online surveys are an important means of data collection in marketing and research, but conventional survey designs are often perceived as dull and unengaging, resulting in negative respondent behavior. Gamification has been proposed to make online surveys more pleasant to fill and, consequently, to improve the quality of survey results. This work applied gamification to an existing survey targeted at teenagers and young adults. The gamified survey was evaluated in a study with 60 participants regarding the psychological and behavioral outcomes of gamification. Results indicate that gamification successfully increased the users’ perceived fun, the average time spent, as well as their willingness to use and recommend the survey, without introducing a strong bias in survey results, albeit with a lower overall response rate.

This publication describes the analysis and development of a serious game for the rehabilitation of stroke patients, which was conducted with the assistance of an experienced physiotherapist using a user-centered design. After a stroke, patients often complain about balance related problems or partial paralysis. Normal rehabilitation training depends on balance and strength exercises. In such cases balance plates are often used as training devices, but patients often report lack of motivation especially when training at home. The research objective of this publication was to develop a serious game using the Nintendo Wii Fit Balance Board. From a physiotherapeutic view it should help patients suffering from a stroke throughout their rehabilitation, especially to increase the motivation to do their exercises. Since every patient is different, the developed serious game can be customized by a physiotherapist for the individual patient's needs. A lot of customization options provide optimal support for the entire rehabilitation process.

Stroke is affecting different age groups worldwide with tremendous consequences. Most stroke survivors suffer from motor impairment, where rehabilitation helps to regain lost abilities. Rehabilitation is a long process that focuses on repeating exercises, which are monotonous and time-consuming. As a consequence, patients loose interest and motivation. Serious games offer the possibility to deliver rehabilitation exercises in combination with entertaining and motivating aspects. A variety of serious games using additional hardware is available to support stroke patients. The authors propose a new and innovative serious game, which rely only on integrated sensors of a smartphone. Development was based on a user-centred design approach divided into 2 phases. In phase 1 requirements were identified in cooperation with a therapist and 13 stroke patients. The developed serious game included different levels of difficulty for movement, gesture and touch exercises. Additional feedback and requirements from therapists and medical doctors were included in phase 2, which resulted in an advanced solution with additional levels. Gathered feedback showed a positive attitude towards the proposed solution.

The particular responsibility of referees in combat sports lies in their decision-making to enforce the rules of the sport, which requires considerable experience and a multitude of skills, including perception, categorization, memory processing, and information integration. As a cost-effective alternative to in-tournament training, this research aims to evaluate the novel video-based serious game called “JudgED” to train martial arts referees’ decision-making processes through immediate feedback. The effectiveness of the JudgED game was assessed by (a) measuring decision accuracy and specific reaction time, (b) calculating a theoretical probability of correct scoring, and (c) comparing these results with real competition judging agreement data. A field study was conducted to analyze the performance of 16 kickboxing referees. The study involved two video-based tests in the serious game. The performance data for JudgED were obtained via a procedure that compares the players’ inputs in the serious game with expert-defined decisions. The results were compared to real-competition data gathered through qualitative analysis of kickboxing fights (n = 400 fights/1200 bouts) at the WAKO World Championships 2021. The findings showed an average decision accuracy of 43.011% and an average reaction time of 1.022 s. For further comparison, binominal distribution for the probability of correct final decisions (between 15.3% and 67.2%) in JudgED and Fleiss’ Kappa interrater reliability for JudgED (Ring: $\kappa$ = 0.371; Tatami: $\kappa$ = 0.398; p < 0.001) and tournament decisions (by bout: $\kappa$ = 0.114; by fight $\kappa$ = 0.063; by outcome $\kappa$ = 0.166; p < 0.001) were calculated. The results suggest that more training is required to improve referee decision accuracy, and JudgED bears the potential to work as a suitable supporting system.

We present a novel concept for an audience voting system for judged sports events. It is based on wireless wearable motion sensors utilizing the natural behaviour of sports spectators -clapping and cheering. This paper describes initial results from a user study that guided our design process. We further present two preliminary prototypes focusing on hardware and design feasibility respectively. Results confirm our assumption that the system does not provide objective results, but has the potential to increase the spectator experience by giving audience members the feeling of being part of the voting process.