Torque Estimation Using Neural Drive for a Concentric Contraction.

The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. A challenge across these research areas is improving user-interface control. One established approach is using neural control interfaces derived from surface electromyography (sEMG).

Determining day-to-day human variation in indirect calorimetry using Bayesian decision theory.

New Findings: What is the central question of this study? We sought to understand the day-to-day variability of human indirect calorimetry during rest and exercise. Previous work has been unable to separate human day-to-day variability from measurement error and within-trial human variability. We developed models accounting for different levels of human- and machine-level variance and compared the probability density functions using total variation distance.

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP).