DeapSea: Workflow-Supported Serious Game Design for Stroke Rehabilitation

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

Smart Boxing Glove “RD <i>α</i>”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning

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 α) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD α 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

Prevention and Rehabilitation Gaming Support for Ankle Injuries Usable by Semi-Professional Athletes Using Commercial Off-the-Shelf Sensors

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 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