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Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population
A data driven approach that allows physical activity intensity states to emerge from the data, without relying on parameters derived from external populations, and offers a new perspective on this problem and potentially improved results.
Modelling and Analysis of Magnetic Fields from Skeletal Muscle for Valuable Physiological Measurements
Here, upon briefly describing the principles of voltage distribution inside skeletal muscles due to the electrical stimulation, we provide a protocol to determine the effects of the magnetic field generated from a time-changing action potential propagating in a group of skeletal muscle cells.
Classification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning
We set out to address a longstanding challenge in using dictionary learning for classification; that is to simultaneously maximise the discriminability and sparse-representability power of the learned dictionaries.
Grasp Type Estimation for Myoelectric Prostheses using Point Cloud Feature Learning
Prosthetic hands can help people with limb difference to return to their life routines.
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
Further, we reformulate the problem of robotic grasping by replacing conventional grasp rectangles with grasp belief maps, which hold more precise location information than a rectangle and account for the uncertainty inherent to the task.
