Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU

Quantitative measurement of the golf swing is critical for evaluating technique and enabling individualized feedback. However, existing methods are impractical to use on the golf course: optical motion capture is laboratory-bound, camera-based methods require impractical camera placement, and multi-sensor inertial measurement unit (IMU) systems require multi-segment setup and calibration. We thus propose a single wrist-worn IMU approach for estimating full-body joint angles during golf swings. The proposed Wrist-IMU Temporal Kinematic Network (WIT-KinNet) leverages modality-specific IMU embeddings and temporal kinematic encoding to learn wrist-to-body motion dependencies and estimate full-body joint angles during golf swings. Thirty-six golfers spanning beginner and skilled players, performed full, half, and quarter swings using seven club types: driver, 3-wood, 5-hybrid, 5-iron, 7-iron, 9-iron, and sand wedge. The proposed WIT-KinNet was evaluated under subject-wise cross-validation using synchronized smartwatch IMU data and ground-truth kinematics derived from an optical motion capture system. The proposed approach achieved a mean absolute error of 8.11 $\pm$ 1.84$^\circ$ across full-body joint angles. High temporal correlation was observed for pelvic rotation and upper torso rotation (r = 0.98 and 0.97, respectively), with X-factor and S-factor also showing strong correlation (r = 0.96 and 0.96). Linear mixed-effects models of the error revealed that swing amplitude, skill level, and club type all significantly affected measurement differences (p $<$ 0.05). The results establish the first single wrist-worn IMU approach for estimating full-body golf swing kinematics, enabling practical swing analysis during real gameplay.

Mixtac: A Novel Bio-Inspired Hybrid Tactile Sensor with Synergistic Event-Frame Perception

Vision based and event based tactile sensors are important in robotic manipulation research. However, they suffer from a fundamental tradeoff: vision based sensors have low sampling rates, while event based sensors are prone to drift during long term static force estimation. To solve this challenge and achieve human level tactile perception, the novel hybrid event frame tactile sensor (Mixtac) is proposed in this paper by emulating the synergistic function of biological mechanoreceptors, which achieves normal force estimation. The prototype leverages events for high frequency force tracking and frames for long term accuracy. The Frame Guided Event Recurrent Network (FGER-Net) was proposed to fuse the two data streams. Frames were used by the net to correct event drift during training and guide high frequency predictions during inference. Experiments demonstrated an MAE of 0.04 N. This paper could bridge the sampling rate gap from 0 to 500 Hz in current vision based tactile sensors and pave the way for human level robotic manipulation.