Smart coaching platforms are emerging which combine Body-Sensor-Networks with AI-based 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 work-related injuries. This paper presents a solution to non-invasively 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 under-performing. 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 real-world 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 (t-test, $\mathrm{p}\lt 0.0001)$ 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.
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Inertial-based Motion Capturing and Smart Training System
Semantic Scholar · Engineering · 2019
Abstract
Smart coaching platforms are emerging which combine Body-Sensor-Networks with AI-based 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 work-related injuries. This paper presents a solution to non-invasively 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 under-performing. 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 real-world 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 (t-test, $\mathrm{p}\lt 0.0001)$ 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.