Compact and Low-Power Neural Spike Compression Using Undercomplete Autoencoders
Implantable microsystems that collect and transmit neural data are becoming very useful entities in the field of neuroscience. Limited by high data rates, on-chip compression is often required to transmit the recorded data without causing power dissipation at levels that would damage sensitive brain tissue.
Design Optimization in Lower Limb Prostheses: A Review
This paper aims to develop a knowledge base and identify the promising research pathways toward designing lower limb prostheses for optimal biomechanical and clinical outcomes.
Effects of Vibrotactile Biofeedback Coding Schemes on Gait Symmetry Training of Individuals With Stroke
Variations in biofeedback coding schemes for postural control, in recent research, have shown significant differences in performance outcomes due to variations in coding schemes.
Subject-Exoskeleton Contact Model Calibration Leads to Accurate Interaction Force Predictions
Knowledge of human–exoskeleton interaction forces is crucial to assess user comfort and effectiveness of the interaction. The subject-exoskeleton collaborative movement and its interaction forces can be predicted in silico using computational modeling techniques.
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are persistent neurophysiological markers of past concussions.
Bilevel Optimization for Cost Function Determination in Dynamic Simulation of Human Gait
Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying human gait. Predictive musculoskeletal simulation may be used for a variety of applications from designing assistive devices to testing theories of motor control. However, the underlying cost function for the predictive optimization is unknown and is generally assumed a priori.