Recognizing Full-Body Exercise Execution Errors Using the Teslasuit.

Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.

Quality Criteria for Serious Games: Serious Part, Game Part, and Balance.

Serious games are digital games that have an additional goal beyond entertainment. Recently, many studies have explored different quality criteria for serious games, including effectiveness and attractiveness. Unfortunately, the double mission of serious games, that is, simultaneous achievement of intended effects (serious part) and entertainment (game part), is not adequately considered in these studies. This paper aims to identify essential quality criteria for serious games. The fundamental goal of our research is to identify important factors of serious games and to adapt the existing principles and requirements from game-related literature to effective and attractive serious games. In addition to the review of the relevant literature, we also include workshop results. Furthermore, we analyzed and summarized 22 state-of-the-art serious games for education and health. The selected best-practice serious games either prove their effectiveness through scientific studies or by winning game awards. For the analysis of these games, we refer to "DIN SPEC 91380 Serious Games Metadata Format." A summarized text states quality criteria for both the serious and the game part, and especially the balance between them. We provide guidelines for high-quality serious games drawn from literature analysis and in close cooperation with domain experts.

A Survey of Full-Body Motion Reconstruction in Immersive Virtual Reality Applications.

Due to recent advances in virtual reality (VR) technology, the development of immersive VR applications that track body motions and visualize a full-body avatar is attracting increasing research interest. This paper reviews related research to gather and to critically analyze recent improvements regarding the potential of full-body motion reconstruction in VR applications. We conducted a systematic literature search, matching VR and full-body tracking related keywords on IEEE Xplore, PubMed, ACM, and Scopus. Fifty-three publications were included and assigned in three groups: studies using markerless and marker-based motion tracking systems as well as systems using inertial measurement units. All analyzed research publications track the motions of the user wearing a head-mounted display and visualize a full-body avatar. The analysis confirmed that a full-body avatar can enhance the sense of embodiment and can improve the immersion within the VR. The results indicated that the Kinect device is still the most frequently used sensor (27 out of 53). Furthermore, there is a trend to track the movements of multiple users simultaneously. Many studies that enable multiplayer mode in VR use marker-based systems (7 out of 17) because they are much more robust and can accurately track full-body movements of multiple users in real-time.

Development of a Classifier to Determine Factors Causing Cybersickness in Virtual Reality Environments.

Objective: The goal of this contribution is to develop a classifier able to determine if cybersickness (CS) has occurred after immersion in a virtual reality (VR) scenario, based on a combination of biosignals and game parameters. Methods: We collected electrocardiographic, electrooculographic, respiratory, and skin conductivity data from a total of 66 participants. In addition, we also captured relevant game parameters such as avatar linear and angular speed as well as acceleration, head movements, and on-screen collisions. The data were collected while the participants were in a 10-minute VR experience, which was developed in Unity. The experience forced rotation and lateral movements upon the participants to provoke CS. A baseline was captured during a first simple scenario. The data were then split in per-level, per-60-second, and per-30-second windows. Furthermore, participants filled a pre- and postimmersion simulator sickness questionnaire. Simulator sickness scores were then used as a reference for binary (CS vs. no CS) and ternary (no CS-mild CS-severe CS) classification patterns. Several classification methods (support vector machines, K-nearest neighbors, and neural networks) were tested. Results: A maximum classification accuracy of 82% was achieved for binary classification and 56% for ternary classification. Conclusion: Given the sample size and the variety of movement patterns presented in the demonstration, we conclude that a combination of biosignals and game parameters suffice to determine the occurrence of CS. However, substantial further research is required to improve binary classification accuracy to adequate values for real-life scenarios and to determine better approaches to classify its severity.

Recent advances in rehabilitation for Parkinson's Disease with Exergames: A Systematic Review.

OBJECTIVE: The goal of this contribution is to gather and to critically analyze recent evidence regarding the potential of exergaming for Parkinson's disease (PD) rehabilitation and to provide an up-to-date analysis of the current state of studies on exergame-based therapy in PD patients. METHODS: We performed our search based on the conclusions of a previous systematic review published in 2014. Inclusion criteria were articles published in the indexed databases Pubmed, Scopus, Sciencedirect, IEEE and Cochrane published since January 1, 2014. Exclusion criteria were papers with a target group other than PD patients exclusively, or contributions not based on exergames. Sixty-four publications out of 525 matches were selected. RESULTS: The analysis of the 64 selected publications confirmed the putative improvement in motor skills suggested by the results of the previous review. The reliability and safety of both Microsoft Kinect and Wii Balance Board in the proposed scenarios was further confirmed by several recent studies. Clinical trials present better (n = 5) or similar (n = 3) results than control groups (traditional rehabilitation or regular exercise) in motor (TUG, BBS) and cognitive (attention, alertness, working memory, executive function), thus emphasizing the potential of exergames in PD. Pilot studies (n = 11) stated the safety and feasibility of both Microsoft Kinect and Wii Balance Board, potentially in home scenarios as well. Technical papers (n = 30) stated the reliability of balance and gait data captured by both devices. Related meta-analyses and systematic reviews (n = 15) further support these statements, generally citing the need for adaptation to patient's skills and new input devices and sensors as identified gaps. CONCLUSION: Recent evidence indicates exergame-based therapy has been widely proven to be feasible, safe, and at least as effective as traditional PD rehabilitation. Further insight into new sensors, best practices and different cognitive stadiums of PD (such as PD with Mild Cognitive Impairment), as well as task specificity, are required. Also, studies linking game parameters and results with traditional assessment methods, such as UPDRS scores, are required. Outcomes for randomized controlled trials (RCTs) should be standardized, and follow-up studies are required, particularly for motor outcomes.