Intelligent System for the Reduction of Injuries in Archery Archery is one of these sports in which the athletes repeat the same body postures over and over again. This means that tiny wrong habits could cause serious longterm health injuries. Consequently, learning a correct shooting technique is very important for both beginner archers and elite athletes. In this work, we present a system that uses machine learning to automatically detect anomalous postures and return to the archer a shooting score, that works by giving the archer a feedback on his own body configuration. We use a neural network to analyze images of archers during the firing and return the place of their different body joints. With this information, the system can detect wrong postures which might lead to injuries. This feedback is very important to the archer when learning the shooting technique. In addition, the system is not intrusive for the archer, so shehe can fire arrows freely. Preliminary results show the usefulness of the system, which is able to detect 4 spine misalignment and 4 raised elbow analyzing only 9 shots.

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.