The purpose of this study is to develop a real-time controller for exoskeletons using neural information embedded in human musculature. This controller will consist of an online interface that anticipates human movement based on high-density electromyography (HD-EMG) recordings, and then translates it into functional assistance. This study will be carried out in both healthy participants and participants post-stroke.

The researchers will develop an online algorithm (decoder) in currently existing exoskeletons that can extract hundreds of motor unit (MU) spiking activity out of HD-EMG recordings. The MU spiking activity is a train of action potentials coded by its timing of occurrence that gives access to a representative part of the neural code of human movement. The researchers will also develop a command encoder that can anticipate human intent (multi-joint position and force commands) from MU spiking activity to translate the neural information to movement. The researchers will integrate the decoder with the command encoder to showcase the real-time control of multiple joint lower-limb exoskeletons.

The researchers will record muscle activity in healthy participants and participants post-stroke from up to eight lower limb muscles (soleus, gastrocnemius, tibialis anterior, rectus femoris, vastus lateralis, and hamstring) during functional tasks (e.g., single-joint movement, gait, squatting, cycling). These measurements will provide the physiological dataset of lower limb movement and locomotion for the neural decoder. Then, the researchers will apply online deep learning methods for MU spiking activity decomposition from over eight muscles, and develop a real-time neural decoder. This will provide real-time decomposition of hundreds of MUs concurrently active during natural lower limb human behavior. The researchers will validate this approach by comparing our results with a gold standard, the blind source separation method. Blind source separation algorithms can separate or decompose the HD-EMG signals, a convolutive mix of MU action potentials, into the times at which individual MUs discharge their action potentials. With the decomposed MU spiking data, the researchers will develop methods to translate MU spiking activity in position, force, and hybrid commands for exoskeletons that will become a command encoder implemented into currently existing research exoskeletons that can anticipate human intent (multi-joint position and force commands) to estimate the level of assistance required by the task, (e.g., add knee torque during the stance phase).

The researchers will combine the MU spiking activity decoder with the subspace projection methods into a neural real-time interface between individuals and a currently existing research lower extremity exoskeleton for locomotion augmentation. This will become an integrated high-resolution human-machine interface that can be used for real-time control of exoskeletons so that commands will be delivered at a rate higher than the muscles' electromechanical delay, i.e., the elapsed time between neural command and muscle force generation of movement.

For Experiment A, the investigators will recruit healthy volunteers (n = 20) and participants post-stroke (n = 20) and complete single-joint movement and locomotor tasks to collect muscle activity data via HD-EMG.

For Experiment B, the investigators will showcase the generalization of our approach recruiting and interfacing healthy volunteers (n = 20) and participants post-stroke (n = 20) with the assistive exoskeleton. Subjects will perform single-joint and locomotor tasks to calibrate the decoder, and then repeat single-joint and locomotor tasks with the decoder providing real-time assistance. Participants post-stroke will repeat up to 10 sessions to evaluate the stability of the ability of the decoder to extract motor units.

Inclusion Criteria for Healthy Participants:

• Age from 18 to 80 years
• No history of a brain and/or skull lesion
• Normal hearing and vision, both can be corrected
• Able to understand and give informed consent
• No neurological disorders
• Absence of pathology that could cause abnormal movements of extremities (e.g.,
• epilepsy, stroke, marked arthritis, chronic pain, musculoskeletal injuries)
• Able to understand and speak English
• Height between 3 foot 6 inches (1.1 meters) and 6 foot 9 inches (2.1 meters)

Inclusion Criteria for Participants Post-stroke:

• Age from 18 to 80 years
• History of unilateral, supratentorial, ischemic or hemorrhage stroke greater than 6 months
• Ability to walk independently on level ground, allowed to use assistive device or bracing
• as needed
• Medically stable
• No planned surgeries, medical treatments or outpatient therapy during the study period
• Normal hearing and vision, both can be corrected
• Able to understand and give informed consent
• Able to understand and speak English
• Height between 3 foot 6 inches (1.1 meters) and 6 foot 9 inches (2.1 meters)

Exclusion Criteria for Healthy Participants:

• Weight over 220 lbs
• Pregnancy (ruled out by pregnancy questionnaire)
• Any neurological diagnoses or medications influencing brain function
• History of significant head trauma (i.e., extended loss of consciousness, neurological
• sequelae)
• Known structural brain lesion
• Significant other disease (heart disease, malignant tumors, mental disorders)
• Non prescribed drug use (as reported by subject)
• History of current substance abuse (exception: current nicotine use is allowed)
• Recreational marijuana
• Dementia; severe depression; or prior neurosurgical procedures
• Failure to perform the behavioral or locomotor tasks
• Prisoners

Exclusion Criteria for Participants Post-Stroke:

• Weight over 220 lbs
• Pregnancy (ruled out by pregnancy questionnaire)
• Botox (botulinum toxin) injection to lower limbs within the prior 3 months, or planned
• injection during study period.
• History of current substance abuse (exception: current nicotine use is allowed)
• Reduced cognitive function
• Severe aphasia
• Prisoners
• Co-existence of other neurological diseases (ex: (Parkinson's disease, traumatic brain
• injury, multiple sclerosis, etc.)
• Mixed or complex tremors
• Severe hip, or knee arthritis
• Osteoporosis (as reported by subject)
• Medical (cardiac, renal, hepatic, oncological) or psychiatric disease that would
• interfere with study procedures for HD-EMG