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.

In recent years, the variety of textile electrodes developed for electrophysiological signal detection has increased rapidly. Among the applications that could benefit from this advancement, those based on surface electromyography (sEMG) are particularly relevant in rehabilitation, training, and muscle function assessment. In this work, we validate the performance of polymer-based screen-printed textile electrodes for sEMG signal detection.

Spinal cord stimulation (SCS) has enabled motor recovery in paraplegics with motor complete spinal cord injury (SCI). However, the physiological mechanisms underlying this recovery are unknown. This paper analyzes muscle synergies in two motor complete SCI patients under SCS during standing and compares them with muscle synergies in healthy subjects, in order to help elucidate the mechanisms that enable motor control through SCS.

Overactive bladder (OAB) patients suffer from a frequent urge to urinate, which can lead to a poor quality of life. Current neurostimulation therapy uses open-loop electrical stimulation to alleviate symptoms. Continuous stimulation facilitates habituation of neural pathways and consumes battery power. Sensory feedback-based closed-loop stimulation may offer greater clinical benefit by driving bladder relaxation only when bladder contractions are detected, leading to increased bladder capacity.

The aim of the current study was to examine the role of environment, whether virtual or physical, on robot-assisted reaching movements in chronic stroke and healthy individuals, within a single session. Twenty-three subjects participated in the current study divided into three groups: nine chronic stroke individuals able to perform a reaching task with no need for the robot assistance, nine chronic stroke individuals who needed robot assistance to complete the reaching task, and five healthy individuals.

A general principle of human movement is that our nervous system is able to learn optimal coordination strategies. However, how our nervous system performs this optimization is not well understood. Here we design, build, and test a mechatronic system to probe the algorithms underlying optimization of energetic cost in walking.