arXiv:2606.28145v1 Announce Type: new Abstract: Wearable devices produce large, high dimensional training logs for everyday runners, and interpretation rather than data collection is now the limiting step. This paper evaluates five dimensionality reduction models, three autoencoder variants, PCA, and a Variational Autoencoder, on their ability to compress nine sensor runner profiles into a single scalar performance indicator, the latent score. Because the setting is fully unsupervised, model quality is assessed along two complementary axes: reconstruction error (Mean Squared Error) and latent
The proliferation of wearable devices and advancements in AI, specifically autoencoders for dimensionality reduction, are enabling more sophisticated analysis of biometric data.
This development allows for the conversion of high-dimensional wearable sensor data into actionable, interpretable performance metrics, moving beyond mere data collection to intelligent interpretation.
The ability to automatically generate scalar performance indicators from complex wearable data could democratize advanced athletic analysis and personalized training protocols.
- · Wearable device manufacturers
- · Sports analytics companies
- · Professional sports organizations
- · Individual athletes
- · Traditional sports coaching (if not adopting tech)
- · Generic training programs
- · Manual data analysis services
Improved, hyper-personalized training regimes for athletes and everyday users become widely accessible.
Increased consumer demand for wearable devices offering advanced analytical capabilities and AI-driven insights.
The application of similar autoencoder architectures and unsupervised learning to other areas of human performance and health monitoring, such as early disease detection from continuous biometrics.
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Read at arXiv cs.LG