Papers for

sports equipment manufacturers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Batteryless motion capture sensors on fabric enable 3d shape tracking

Toward Motion Capture Textiles: A Batteryless IMU Array Powered and Networked Through a Conductive-Fabric Bus

Abstract: Motion capture is increasingly important in healthcare, rehabilitation, and sports applications. However, conventional vision-based systems require camera infrastructure and line-of-sight conditions, limiting their use in daily environments. This paper proposes a location-independent platform for motion capture, based on an array of IMU (inertial measurement unit) sensor nodes without individual wiring or onboard batteries, enabled by two-dimensional communication and power delivery over conductive fabric. Pipeline processing in each sensor node, combined with a token-passing protocol, enables collision-free and stable data acquisition at 50 Hz. Real-time three-dimensional reconstruction of flexible objects is further demonstrated by applying Hermite spline interpolation to orientation data from grid-aligned IMUs. Against a cylinder of known radius, the reconstructed surface deviates by 2.60 mm RMS over the 225 mm span of the array. The reconstruction is formulated for the quasi-static regime, and this letter empirically validates it in that regime. The hardware platform and communication protocol implemented in this work enable further investigation toward dynamic motion capture textiles.

Wed 14 OctNetworking and Internet Architecture
The gist
Motion capture often needs cameras and clear views, which makes it hard to use every day. The authors created small sensors that can be put on fabric to track movement without needing batteries or wires for each sensor. These sensors get power and send data through special conductive fabric, working together to measure and rebuild shapes in 3D. They tested this on a curved surface and found the system to be quite accurate. This approach could lead to motion capture clothes that work anytime and anywhere.
Open → 2610.19969v1

Deep learning model analyzes table tennis player skills from smart racket data

TTNet: Multi-Task Deep Learning for Table Tennis Player Analysis with Smart Racket

Abstract: The AI CUP 2025 Precise Analysis of Table Tennis Smart Racket Data Competition introduced smart table tennis rackets that collect extensive player swing data, enabling research on table tennis big data. These data support in-depth analysis of players' return techniques and swing-force consistency, improving the accuracy of player skill assessment. This study focuses on six-axis sensor data collected by smart table tennis rackets and proposes TTNet, a novel deep learning model with multitask learning capabilities, to advance table tennis data analysis and related applications. TTNet combines convolutional neural networks (CNNs), residual networks (ResNet), and self-attention mechanisms to simultaneously predict four player attributes: gender, playing hand, years of experience, and skill level. We adopt a two-stage training strategy that incorporates data augmentation and task-specific loss functions to improve generalization on imbalanced data. Our approach achieved second place on the official competition leaderboard.

Tue 6 OctMachine Learning
The gist
Analyzing how people play table tennis can be complicated, but new smart rackets collect detailed swing data to help. The authors created a deep learning model called TTNet that can look at this data and guess things about the player, like their gender, which hand they use, how experienced they are, and their skill level. This helps make better and more precise assessments of player skills. Their smart method performed well in a competition, showing promise for using AI to study sports moves.
Open → 2610.07823v1