![]() View full-textĪ sample of 111 runners was given a survey to illustrate their experience in using sports monitoring devices. Consequently, covered distance and EE should not be monitored with the presented Wearables, in sport specific conditions. However, the covered distance, as well as the EE could not be assessed validly with the investigated Wearables. The measurement of EE was acceptable for the Garmin, Fitbit and Withings Wearables (small to moderate MAPE), while Bodymedia Sensewear, Polar Loop, and Beurer AS80 showed a high MAPE up to 56% for all test conditions.Ĭonclusion: In our study, most Wearables provide an acceptable level of validity for step counts at different constant and intermittent running velocities reflecting sports conditions. For covered distance, all Wearables showed a very low ICC (<0.1) and high MAPE (up to 50%), revealing no good validity. Results: All Wearables, except Bodymedia Sensewear, Polar Loop, and Beurer AS80, revealed good validity (small MAPE, good ICC) for all constant and varying velocities for monitoring step count. ![]() ![]() Step count, covered distance, and EE were evaluated by comparing each Wearable with a criterion method (Optogait system and manual counting for step count, treadmill for covered distance and indirect calorimetry for EE). h⁻¹) while wearing eleven different Wearables (Bodymedia Sensewear, Beurer AS 80, Polar Loop, Garmin Vivofit, Garmin Vivosmart, Garmin Vivoactive, Garmin Forerunner 920XT, Fitbit Charge, Fitbit Charge HR, Xaomi MiBand, Withings Pulse Ox).h⁻¹), a 5 min period of intermittent velocity, and a 2.4 km outdoor run (10.1 km.Methods: Twenty healthy sport students (10 men, 10 women) performed a running protocol consisting of four 5 min stages of different constant velocities (4.3 7.2 10.1 13.0 km Therefore, the aim was to evaluate the validity of eleven Wearables for monitoring step count, covered distance and energy expenditure (EE) under laboratory conditions with different constant and varying velocities. For (elite) athletes, however, scientific trustworthiness needs to be given for a broad spectrum of velocities or even fast changes in velocities reflecting the demands of the. For recreational people, testing of these devices under walking or light jogging conditions might be sufficient. The experimental results show that our algorithm obtains the ball locations for above 96% frames in a sufficient accuracy for summarization.īackground: In the past years, there was an increasing development of physical activity tracker (Wearables). This algorithm is able to obtain ball locations for most frames in a BTV, making use of four cues, namely, (1) an antimodel method to produce ball candidates from each frame, (2) a trajectory-based scheme to generate, identify and extend the ball trajectories from a set of candidates, (3) a method to infer the ball locations according to players' locations and the points of hitting, (4) a method to estimate missing ball locations from known ball locations. Instead it decides whether a candidate trajectory is a ball trajectory. Unlike the object-based algorithm, it does not decide whether an object is the ball. This paper presents a trajectory-based algorithm to detect and track the ball in BTV. But so far no algorithm is able to obtain satisfactory result in locating the ball in broadcast tennis video (BTV). Keywords Event detection, multimodal data mining, integrated data mining in multimedia information systems, frameworks for multimedia data mining.īall locations over frames facilitate tennis video analysis to a great extent. The results are promising and can provide a good basis for analyzing the high-level structure of video content. This framework has been tested using soccer videos with different styles as produced by different broadcasters. The proposed framework fully exploits the rich semantic information contained in visual and audio features for soccer video data, and incorporates the data mining process for effective detection of soccer goal events. ![]() Then the data pre-filtering step is performed on raw video features with aid of domain knowledge, and the pre-filtered data are used as the input data in the data mining process using classification rules. The proposed multimedia data mining framework first analyzes the soccer videos by using joint multimedia features (visual and audio features). The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. In this paper, we present an effective data mining framework for automatic extraction of goal events in soccer videos. As digital video data becomes more and more pervasive, the issue of mining information from video data becomes increasingly important.
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