{"title":"Using Virtual Reality and Head-Mounted Displays to Increase Performance in Rowing Workouts","authors":"S. Arndt, A. Perkis, Jan-Niklas Voigt-Antons","doi":"10.1145/3265845.3265848","DOIUrl":"https://doi.org/10.1145/3265845.3265848","url":null,"abstract":"Technology is advancing rapidly in the domain of virtual reality, as well as in using sensors to gather feedback from our body and the environment we are interacting in. Combining these two technologies gives us the opportunity to create personalized and reactive immersive environments. These environments can be used for training of dangerous situations (e.g. fire, crashes, etc) or to improve skills with less distraction than regular natural environments would offer. The pilot study described in this paper, puts an athlete rowing on a stationary rowing machine into a virtual environment. The virtual reality receives movement data from several sensors of the rowing machine and displays those in the head-mounted display. In addition, metrics on technique are derived from the sensor data as well as physiological data. All this is used to investigate if and to which extend VR improves the technical skills of an athlete, during the complex sport of rowing. Furthermore, athletes are given subjective feedback about their performance comparing the standard rowing workout with the workout using VR. First results indicate improved performance of the workout and an enhanced experience for the athlete in the VR condition.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115617550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Zecha, C. Eggert, Moritz Einfalt, Stephan Brehm, R. Lienhart
{"title":"A Convolutional Sequence to Sequence Model for Multimodal Dynamics Prediction in Ski Jumps","authors":"D. Zecha, C. Eggert, Moritz Einfalt, Stephan Brehm, R. Lienhart","doi":"10.1145/3265845.3265855","DOIUrl":"https://doi.org/10.1145/3265845.3265855","url":null,"abstract":"A convolutional sequence to sequence model for predicting the jump forces of ski jumpers directly from pose estimates is presented. We collect the footage of multiple, unregistered cameras together with the output of force measurement plates and present a spatiotemporal calibration procedure for all modalities which is merely based on the athlete's pose estimates. The synchronized data is used to train a fully convolutional sequence to sequence network for predicting jump forces directly from the human pose. We demonstrate that the best performing networks produce a mean squared error of 0.062 on normalized force time series while being able to identify the moment of maximal force occurrence in the original video at 55% recall within +- 2 frames around the ground truth.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122376029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Session 2","authors":"D. Zecha","doi":"10.1145/3287619","DOIUrl":"https://doi.org/10.1145/3287619","url":null,"abstract":"","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124512728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kentaro Yagi, Kunihiro Hasegawa, Yuta Sugiura, H. Saito
{"title":"Estimation of Runners' Number of Steps, Stride Length and Speed Transition from Video of a 100-Meter Race","authors":"Kentaro Yagi, Kunihiro Hasegawa, Yuta Sugiura, H. Saito","doi":"10.1145/3265845.3265850","DOIUrl":"https://doi.org/10.1145/3265845.3265850","url":null,"abstract":"The purpose of this study is sensing movements of 100-m runners from video that is publicly available, for example, Internet broadcasts. Normally, information that can be obtained from a video is limited to the number of steps and average stride length. However, our proposed method makes it possible to measure not only this information, but also time-scale information like every stride length and speed transition from the same input. Our proposed method can be divided into three steps. First, we generate a panoramic image of the 100-m track. By doing this, we can estimate where the runners are running in a frame at the 100-meter scale. Second, we detect whether the runner steps in the frame. For this process, we utilize the detected track lines and leg joint positions of runners. Finally, we project every steps to the overview image of the 100-m track to estimate the stride length at the 100-m scale. In the experiment part, we apply our method to various race videos. We evaluate the accuracy of our method via comparison with the data measured using typical methods. In addition, we evaluate the accuracy of estimation of the number of steps and show visualized runners' steps and speed transitions.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127074245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Practical Sports Video Analysis at Qoncept: A Few Case Studies","authors":"Tuukka Karvonen","doi":"10.1145/3265845.3282352","DOIUrl":"https://doi.org/10.1145/3265845.3282352","url":null,"abstract":"Analyzing sports scenarios through video processing is an enticing approach to sports analytics, especially due to the lack of need to attach sensors to the measured target. However, it has many challenges inherent to inverse problems such as multidimensionality of the data and the noise in the measurements. There are also many practical challenges to providing a working computer vision -based solution such as equipment installation constraints and including these in the system design, and dealing with outside factors such as weather changes. We take a look at these various challenges through a few case studies. We go through both the algorithmic and practical issues faced when working on these problems, and as a result, hope to identify common problems when working in the field and through them construct a useful framework for building computer vision -based solutions to sports analysis problems.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114464606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tse-Yu Pan, Chen-Yuan Chang, Wan-Lun Tsai, Min-Chun Hu
{"title":"ORSNet","authors":"Tse-Yu Pan, Chen-Yuan Chang, Wan-Lun Tsai, Min-Chun Hu","doi":"10.1145/3265845.3265849","DOIUrl":"https://doi.org/10.1145/3265845.3265849","url":null,"abstract":"In this work, we propose a novel sports referee training system based on wearable sensors and a real-time Official Referee Signal (ORS) segmentation/recognition method which can recognize 65 kinds basketball ORSs with the accuracy of 95.3%. A hybrid neural network named ORSNet is designed for recognizing gestures based on IMU signals. The proposed ORSNet involves convolution layers and recurrent layers to learn more representative features and correlations in temporal domain, respectively. A novel loss function and a weight sharing strategy are proposed to learn a more robust ORS recognition model. Moreover, we investigate the influence of applying a semi-supervised network in the proposed ORSNet.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121867595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Understanding and Shaping the Athlete's Brain Using Body-Mind Reading and Feedback","authors":"M. Kashino","doi":"10.1145/3265845.3282351","DOIUrl":"https://doi.org/10.1145/3265845.3282351","url":null,"abstract":"In sports where athletes play against an opponent, for example, ballgames and martial arts, a variety of cognitive functions hold the key to winning, such as grasping the situation, strategizing against one's opponent, making appropriate decisions instantaneously, and keeping an ideal mental state under intense pressure. Most of these functions, however, are \"implicit\" brain functions that the athlete himself/herself is not even aware of and cannot control at will. This is thought to be one of the factors behind the difficulty of acquiring, exhibiting, and coaching a specific skill. The NTT Sports Brain Science project was established in January 2017 to conduct research with the aim of understanding superior implicit brain functions in top athletes, identifying the factors in winning, and improving the performance of athletes based on research findings. The technical core of the project is \"body-mind reading and feedback\", which is constituted by various technologies such as wearable sensing, computer vision, biometric analyses, virtual reality, and sonification. The basic concepts and some research topics of the project will be introduced, with a focus on baseball and softball.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121058289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kei Saito, Katsutoshi Masai, Yuta Sugiura, Toshitaka Kimura, M. Sugimoto
{"title":"Development of a Virtual Environment for Motion Analysis of Tennis Service Returns","authors":"Kei Saito, Katsutoshi Masai, Yuta Sugiura, Toshitaka Kimura, M. Sugimoto","doi":"10.1145/3265845.3265854","DOIUrl":"https://doi.org/10.1145/3265845.3265854","url":null,"abstract":"In sports performance analysis, it is important to understand differences between experts and novices in order to train novices in an efficient manner. To understand these differences within the game of tennis, we developed a virtual environment to analyze the responses of experts and novices to services. By capturing actual service motions of an expert, it is possible to reproduce virtualized services in the environment. We did experiments on types and courses of services. As a result, we found differences between experts and novices in preparation, leg movement, take-back returns, and degree of spine twist.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130390001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","authors":"","doi":"10.1145/3265845","DOIUrl":"https://doi.org/10.1145/3265845","url":null,"abstract":"","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116312858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Lin, Ping-Hsuan Han, Kuan-Yin Lu, Chia Hung Sun, P. Lee, Y. Jan, Amy Ming Sui Lee, Wei-Zen Sun, Yi-Ping Hung
{"title":"Stillness Moves: Exploring Body Weight-Transfer Learning in Physical Training for Tai-Chi Exercise","authors":"H. Lin, Ping-Hsuan Han, Kuan-Yin Lu, Chia Hung Sun, P. Lee, Y. Jan, Amy Ming Sui Lee, Wei-Zen Sun, Yi-Ping Hung","doi":"10.1145/3265845.3265856","DOIUrl":"https://doi.org/10.1145/3265845.3265856","url":null,"abstract":"Body weight-transfer plays an important role in many exercises. The correlation of the body posture, movement, and weight-transfer will mutually affect the trainee to do well in performances such as Tai-Chi exercise. According to the traditional way of learning Tai-Chi, we proposed Stillness Moves, a physical training system for Tai-Chi, which captures and records users' skeleton movement and weight-transfer information for offering real-time and summary visual feedback. Based on above, we provide a gradual learning program in physical training, which combines body movement and weight-transfer learning. We evaluated our system and compared the performance without and with weight-transfer guidance in the user study. The result demonstrated that weight-transfer guidance is beneficial for trainee learning the Tai-Chi moves. For difficult moves, the trainee should learn the weight-transfer first, then, learning the body movement.","PeriodicalId":174845,"journal":{"name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116521067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}