Inertialbased Motion Capturing and Smart Training System Smart coaching platforms are emerging which combine BodySensorNetworks with AIbased training software to monitor and analyze body motions of athletes, workers, or medical patients. This allows for new opportunities to explore algorithms to interpret body sensor data and provide analytical feedback for learning a physical task, refining body motions, or to protect from workrelated injuries. This paper presents a solution to noninvasively equip a person with sensors of a Smart Training System (STS) to improve training efficiency during sport activities. Our system calculates the significance of each body part during physical activities and provides targeted feedback on which body locations are underperforming. In experiments, the system collected data from 13 inertial sensors attached to the entire body of inexperienced golf learners. Using an indoor golf training net with a central target with 3 concentric zones, 1,080 realworld golf swings of 11 participants were analyzed. During the first 30 swings of each participant, the system learned distributions of motions from each sensor, conditioned on swing performance reported by users from their hitting location on the target. In the later 70 swings, feedback was provided to a subgroup of 8 participants, by computing, for an optimal set of features determined during training, the largest discrepancy. The remaining 3 (control) participants received no feedback. From only 100 golf swings for each participant, our system led to significantly improved scores by on average 3.7x (ttest, mathrmplt 0.0001) over the latter 70 swings. Our results suggest that the combination of motion sensors and processing developed here was able to yield significantly improved golf swing training.

IART: Inertial Assistant Referee and Trainer for Race Walking This paper presents IART, a novel inertial wearable system for automatic detection of infringements and analysis of sports performance in race walking. IART algorithms are developed from raw inertial measurements collected by a single sensor located at the bottom of the vertebral column (L5xe2x80x93S1). Two novel parameters are developed to estimate infringements: loss of ground contact time and loss of ground contact step classification; three classic parameters are indeed used to estimate performance: step length ratio, step cadence, and smoothness. From these parameters, five biomechanical indices customized for elite athletes are derived. The experimental protocol consists of four repetitions of a straight path of 300 m on a longpaved road, performed by nine elite athletes. Over a total of 1620 steps (54 sequences of 30 steps each), the average accuracy of correct detection of loss of ground contact events is equal to 99%, whereas the correct classification of the infringement is equal to 87% for each step sequence, with a 92% of acceptable classifications. A great emphasis is dedicated on the usercentered development of IART: an intuitive radar chart representation is indeed developed to provide practical usability and interpretation of IART indices from the athletes, coaches, and referees perspectives. The results of IART, in terms of accuracy of its indices and usability from endusers, are encouraging for its usage as tool to support athletes and coaches in training and referees in real competitions.