REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION

Overall latency is the elapsed time from input of human motion to the immediate response of the input in the display. Apparently, latency is one of the most frequently cited shortcomings of current Virtual Reality (VR) applications. To compensate latency, previous prediction mechanisms insert a complex mathematical algorithm, which may not be appropriate for complex virtual training applications. More complex VR simulations most likely will impose greater computation burdens and resulted in the increase of latencies. In order to overcome latency problem, this research is an attempt to suggest a new prediction algorithm based on heuristic that could be used to develop a more effective and general system for virtual training applications. The heuristic-based predictor provides a platform to utilize the heuristic power of human along with the algorithmic power, geometry accuracy of motion-planning programs and biomechanical laws of human. Heuristic algorithm is an important module widely used for humanoid robots and avatars in VR systems. However, to the best of the researcher's knowledge, the heuristic approach has not been used as a single prediction algorithm for compensating latency in virtual training systems. In order to find out whether the new prediction algorithm is acceptable and possibly could reduce latency, a fast synchronization squash-game simulation was selected as a study source. This research analyzed the latencies of all subcomponents of this system and designed prediction algorithm that allows high-speed interaction. In measuring the performance on various prediction methods, this research also makes a comparison in real tasks among 1) the heuristic-based prediction, 2) the Grey system prediction and 3) the one without prediction using different sample rates. Findings indicated that heuristic-based algorithm is an accurate prediction method to compensate latency in virtual training. Apparently, heuristic-based prediction and Grey system prediction are significantly better than the one without prediction. When heuristic-based prediction and Grey system prediction were compared, heuristic-based prediction was in fact a better predictor. Overall findings indicated that heuristicbased prediction is efficient, robust and easier to implement

Reducing latency when using Virtual Reality for teaching in sport

Latency is a frequently cited shortcoming of Virtual Reality (VR) applications. To compensate for excessive latency, prediction mechanisms may use sophisticated mathematical algorithms, which may not be appropriate for complex virtual teaching applications. This paper suggests that heuristic prediction algorithms could be used to develop more effective and general systems for VR educational applications. A fast synchronization squash simulation illustrates where heuristic prediction can be used to deal with latency problems

Pedagogical Feedback for Computer-based Sport Training

Feedback in Computer-based Sport Training (CBST) may be synthetically designed to allow athletes to practise in a more effective way and enhance their skill acquisition. Little research has integrated pedagogic theory and instructional design with the design of feedback in CBST. To bridge this gap, the paper presents the design of pedagogically-informed feedback for the implementation of a CBST system. The heart of the design is to generate feedback based on the athletes’ achievement of their intended training outcome. The pedagogical feedback system measures athletes’ performance and compares it with the given training outcomes. The system then identifies the performance’s gap and generates feedback to reinforce better performance. A Counterbalanced experiment asked student rowers (N = 8) to explore the differences between the pedagogical feedback system and their current feedback system (Sean-Analysis). Pedagogical feedback was at least as good as Sean-Analysis with respect to the level of satisfaction of the athlete. Overall, it can be concluded that the pedagogical feedback appears to be a good model for generating feedback in CBST

REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION

Overall latency is the elapsed time from input of human motion to the immediate response of the input in the display. Apparently, latency is one of the most frequently cited shortcomings of current Virtual Reality (VR) applications. To compensate latency, previous prediction mechanisms insert a complex mathematical algorithm, which may not be appropriate for complex virtual training applications. More complex VR simulations most likely will impose greater computation burdens and resulted in the increase of latencies. In order to overcome latency problem, this research is an attempt to suggest a new prediction algorithm based on heuristic that could be used to develop a more effective and general system for virtual training applications. The heuristic-based predictor provides a platform to utilize the heuristic power of human along with the algorithmic power, geometry accuracy of motion-planning programs and biomechanical laws of human. Heuristic algorithm is an important module widely used for humanoid robots and avatars in VR systems. However, to the best of the researcher's knowledge, the heuristic approach has not been used as a single prediction algorithm for compensating latency in virtual training systems. In order to find out whether the new prediction algorithm is acceptable and possibly could reduce latency, a fast synchronization squash-game simulation was selected as a study source. This research analyzed the latencies of all subcomponents of this system and designed prediction algorithm that allows high-speed interaction. In measuring the performance on various prediction methods, this research also makes a comparison in real tasks among 1) the heuristic-based prediction, 2) the Grey system prediction and 3) the one without prediction using different sample rates. Findings indicated that heuristic-based algorithm is an accurate prediction method to compensate latency in virtual training. Apparently, heuristic-based prediction and Grey system prediction are significantly better than the one without prediction. When heuristic-based prediction and Grey system prediction were compared, heuristic-based prediction was in fact a better predictor. Overall findings indicated that heuristicbased prediction is efficient, robust and easier to implement

Pedagogical feedback in the motor skill domain for computer-based sports training

With the rapid development of Computer-based Sport Training (CBST), feedback plays an important role in both coaching and learning. A good CBST system includes not only good training strategies but also effective feedback design. Feedback in the motor skill domain via CBST may be synthetically designed to allow athletes to practice in a more effective way, and enhance their skill acquisition. Little research has been undertaken on the integration of pedagogic theory and instructional design with the design of feedback in CBST. To bridge this gap, this thesis's purpose was to explore the design of pedagogically-informed feedback in the motor skill domain via CBST, in order to support athletes' achievement of their intended training outcomes.This thesis presents a framework of pedagogical feedback in the motor skill domain. It draws a picture of how principles from learning transactions, competency, cybernetics, and behaviourism, can work together to build sound pedagogical feedback for the implementation of a CBST system. The key principle of the framework is to generate feedback based on the athletes' achievement of their intended training outcome. The training outcome is conceptualised as comprising two components: a statement of capability, and a statement of the subject matter to which the capability applies. The pedagogical feedback system measures athletes' performance and compares it against the intended training outcomes. The system then identifies any performance gap and generates feedback to reinforce better performance.Four counterbalanced experiments asked student rowers to explore the differences between the pedagogical feedback system and their current feedback system (Sean-Analysis). Pedagogical feedback was at least as good as Sean-Analysis with respect to the level of satisfaction of the athlete. In addition, pedagogical feedback seemed able to generate feedback that was consistent with the athlete's intended training outcome, support the athlete's positioning within their level of achieved performance, and support the athlete's self-assessment. Overall, it can be concluded that the pedagogical feedback based on the proposed framework appears to be a good model for generating feedback in CBST

A Framework for Pedagogical Feedback in the Motor Skill Domain

With increasingly rapid development in Computer-based Sport Training (CBST), feedback plays an important role in both coaching and learning. A good CBST system includes not only good training strategies but also effective feedback design. Feedback in the motor skill domain via CBST may be synthetically designed to allow athletes to practise in a more effective way, and enhance their skill acquisition. Existing designs lack pedagogical elements. To bridge the gap, we propose a framework for the design of pedagogically-informed feedback based on learning transactions, competence, cybernetics, and behaviourism

The Design of a Conceptual Model of Learning Outcomes in the Computer-based Sport training (CBST).

Instructional design usually begins with the specification of behavioral objectives or intended learning outcomes. The field of educational psychology has long been sensitive to the desirability of establishing learning objective for instruction

Pedagogical feedback in the motor skill domain for computer-based sport training

With the rapid development of Computer-based Sport Training (CBST), feedback plays an important role in both coaching and learning. A good CBST system includes not only good training strategies but also effective feedback design. Feedback in the motor skill domain via CBST may be synthetically designed to allow athletes to practice in a more effective way, and enhance their skill acquisition. Little research has been undertaken on the integration of pedagogic theory and instructional design with the design of feedback in CBST. To bridge this gap, this thesis‟s purpose was to explore the design of pedagogically-informed feedback in the motor skill domain via CBST, in order to support athletes‟ achievement of their intended training outcomes. This thesis presents a framework of pedagogical feedback in the motor skill domain. It draws a picture of how principles from learning transactions, competency, cybernetics, and behaviourism, can work together to build sound pedagogical feedback for the implementation of a CBST system. The key principle of the framework is to generate feedback based on the athletes‟ achievement of their intended training outcome. The training outcome is conceptualised as comprising two components: a statement of capability, and a statement of the subject matter to which the capability applies. The pedagogical feedback system measures athletes‟ performance and compares it against the intended training outcomes. The system then identifies any performance gap and generates feedback to reinforce better performance. Four counterbalanced experiments asked student rowers to explore the differences between the pedagogical feedback system and their current feedback system (Sean-Analysis). Pedagogical feedback was at least as good as Sean-Analysis with respect to the level of satisfaction of the athlete. In addition, pedagogical feedback seemed able to generate feedback that was consistent with the athlete‟s intended training outcome, support the athlete‟s positioning within their level of achieved performance, and support the athlete‟s self-assessment. Overall, it can be concluded that the pedagogical feedback based on the proposed framework appears to be a good model for generating feedback in CBST.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Pedagogical feedback in the motor skill domain for computer-based sport training

With the rapid development of Computer-based Sport Training (CBST), feedback plays an important role in both coaching and learning. A good CBST system includes not only good training strategies but also effective feedback design. Feedback in the motor skill domain via CBST may be synthetically designed to allow athletes to practice in a more effective way, and enhance their skill acquisition. Little research has been undertaken on the integration of pedagogic theory and instructional design with the design of feedback in CBST. To bridge this gap, this thesis‟s purpose was to explore the design of pedagogically-informed feedback in the motor skill domain via CBST, in order to support athletes‟ achievement of their intended training outcomes. This thesis presents a framework of pedagogical feedback in the motor skill domain. It draws a picture of how principles from learning transactions, competency, cybernetics, and behaviourism, can work together to build sound pedagogical feedback for the implementation of a CBST system. The key principle of the framework is to generate feedback based on the athletes‟ achievement of their intended training outcome. The training outcome is conceptualised as comprising two components: a statement of capability, and a statement of the subject matter to which the capability applies. The pedagogical feedback system measures athletes‟ performance and compares it against the intended training outcomes. The system then identifies any performance gap and generates feedback to reinforce better performance. Four counterbalanced experiments asked student rowers to explore the differences between the pedagogical feedback system and their current feedback system (Sean-Analysis). Pedagogical feedback was at least as good as Sean-Analysis with respect to the level of satisfaction of the athlete. In addition, pedagogical feedback seemed able to generate feedback that was consistent with the athlete‟s intended training outcome, support the athlete‟s positioning within their level of achieved performance, and support the athlete‟s self-assessment. Overall, it can be concluded that the pedagogical feedback based on the proposed framework appears to be a good model for generating feedback in CBST.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Pedagogy in computer-based sport training

The central question addressed in this paper is the appropriate formal representation of learning outcomes in the motor skill domain so they can be interpreted and manipulated by computers as well as humans for the implementation of Computer-based Sport Training (CBST). Instructional design usually begins with the specification of behavioural objectives or intended learning outcomes. The field of educational psychology has long been sensitive to the desirability of establishing learning objectives for instruction. Computerprocessable learning outeomes in the motor skill domain, however, seem to have remained the silent partner of learning outcomes in both the cognitive and affective domains. This paper presents a conceptual model of learning outcomes in the motor skill domain for the implementation ofCBST. The heart of this model is to treat athlete's skill as a contextualized space of capability either actual or potential. Rowing is the sport chosen as the study domain