Computer vision based object tracking has been used to annotate and augment sports video. For automatically and systematically competition data collection and tactical analysis. The proposed project also includes research of data visualization, connected training auxiliary devices, and data warehouse. Deep learning techniques will be used to develop video-based real-time microscopic competition data collection based on broadcast competition video. Machine learning techniques will be used to develop tactical analysis. In addition, training auxiliary devices including smart badminton rackets and connected serving machines will be developed based on the IoT technology to further utilize competition data and tactical data and boost training efficiency. Especially, the connected serving machines will be developed to perform specified tactics and to interact with players in their training.

Contactless exercise monitoring is a new trend that makes people feel more comfortable and unconstrained. However, the decrease in accuracy of the pulse rate measurement caused by large motion artifacts is an urgent problem to be solved. In this paper, we proposed a novel approach to monitor step count and improve the accuracy of remote pulse rate measurement based on the image detection method. We designed a chrominance-based adaptive filter and normalization (CADN) method and a domain selection scheme (DSS) to enhance the accuracy of contactless pulse rate measurement during exercise. Various exercises such as biking, stepping, and treadmill running were conducted to evaluate motion robustness of the proposed CADN + DSS and the accuracy of step counts. The results reveal that the detection rates of the proposed step count method are 99.52% and 99.77% for stepping and treadmill exercise, respectively. The pulse rate accuracy is compared with two state-of-the-art algorithms-chrominance and chrominance-based adaptive filter. The results show the proposed CADN+DSS method provides a lower discrepancy between the detected pulse rate and a ground-truth device (Polar H7) for all activities. We expand the scope of contactless measurement for physical activity detection and develop an unfettered step count and pulse rate measurement method in exercise. Therefore, step count and pulse rate can be measured synchronously without relying on any contact sensors.

This paper proposes a mobile patient monitoring system, which integrates current personal digital assistant (PDA) technology and wireless local area network (WLAN) technology. At the patient's location, a wireless PDA-based monitor is used to acquire continuously the patient's vital signs, including heart rate, three-lead electrocardiography, and SpO2. Through the WLAN, the patient's biosignals can be transmitted in real-time to a remote central management unit, and authorized medical staffs can access the data and the case history of the patient, either by the central management unit or the wireless devices. A prototype of this system has been developed and implemented. The system has been evaluated by technical verification, clinical test, and user survey. The evaluation of performance yields a high degree of satisfaction (mean = 4.64, standard deviation--SD = 0.53 in a five-point Likert scale) of users who used the PDA-based system for intrahospital transport. The results also show that the wireless PDA model is superior to the currently used monitors both in mobility and in usability, and is, therefore, better suited to patient transport.