Assessing and improving human movements using sensitivity analysis and digital human simulation Enhancing the performance of technical movements aims both at improving operational results and at reducing biomechanical demands. Advances in human biomechanics and modeling tools allow to evaluate human performance with more and more details. Finding the right modifications to improve the performance is, however, still addressed with extensive time consuming trialanderror processes. This paper presents a framework for easily assessing human movements and automatically providing recommendations to improve their performances. An optimizationbased wholebody controller is used to dynamically replay human movements from motion capture data, to evaluate existing movements. Automatic digital human simulations are then run to estimate performance indicators when the movement is performed in many different ways. Sensitivity indices are thereby computed to quantify the influence of postural parameters on the performance. Based on the results of the sensitivity analysis, recommendations for posture improvement are provided. The method is successfully validated on a drilling activity.

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.