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.

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.

The design and development of a serious game are complex due to different and often numerous stakeholders involved. Different guidelines for general best practices exist, but those are not specific and often do not include therapists or patients as essential stakeholders especially in the context of individualisation of a serious games. Although there are a lot of serious games in the area of (stroke) rehabilitation, design guidelines and indications of what is important are quite scarce. Identifying individualisation possibilities to adapt a serious game to the specific needs of patients was identified to support and improve the design and outcome of serious game development. A literature research and the analysis of previously designed serious games created the foundation for this research. The identified configuration possibilities, additional requirements, and the developed workflow were then evaluated with the gathered insights of therapists trough an online survey. 20 generic configuration possibilities for therapists, as well as seven requirements, were identified and are presented, which can be used when designing a serious game in the context of stroke. In addition a workflow, called “DeapSea” is proposed for supporting the design as well. These results should be used as an addition to already established design recommendations to deliver an adaptable and flexible serious game in the area of stroke—helping to fulfill individual patient needs from the point of therapists and other involved medical stakeholders within the 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.

Ankle injuries are amongst the most common musculoskeletal injuries. The necessity of prevention measurements before or an early rehabilitation start after an injury, is essential for (semi-) professional sports like soccer to decrease healing duration. Sensor-supported serious games could complement a therapeutic program to support resilience and motivation during the prevention or rehabilitation process. Therefore, the aim of this study is to develop and evaluate a user-centered prototype of a serious game using a commercial Off-The-Shelf MetaMotion IMU sensor. A semi-structured interview with a soccer club therapist, followed by an online questionnaire containing 48 questions (n = 91), was performed to ensure a user-centered approach. Based on this, a prototype, including five identified functional requirements and seven exercises (comprising: horizontal/vertical in- and eversion, dorsi- and plantarflexion, knee bend and squat, and toe and heel rise), was developed in an iterative process and evaluated by two participants with an acute ankle injury. The questionnaire outcomes showed averages of 3.3 ankle injuries per participant and 40 absence days per incident. Additionally, 85% of the participants reported needing more prevention time for such injuries. The evaluation phase (total training duration: 2 h 52 min) consisted of playing two different game types (1 and 2 degrees of freedom) and three different levels, where an avatar needs to be controlled while running and avoiding obstacles or collecting trophies. Both range of motion (ROM) and scores, which are directly measured by the game, showed significant improvements (ROM: t = 5.71; p < 0.01; Score: t = 3.98; p < 0.01) between the first and last session in both participants (P1: ROM +3.56°; Score +7.00%, P2: ROM +6.59°; Score +9.53%), indicating high effectiveness, despite a short training period (1 and 2 weeks). ROM improvement results and athlete feedback coincide in that the sensor-assisted serious game might be beneficial for ankle prevention and rehabilitation. At the same time, the increased scores indicate substantial motivation over several training sessions.