About

José L. Pons, PhD, is a world-renowned scientist leading a translational research team at Shirley Ryan AbilityLab, focusing on developing advanced methods for measuring and restoring lower-limb function in diverse patient populations. He is a creative engineer with a long-standing history of collaboration with physicians in physical medicine and rehabilitation, and he is best known for his work in wearable robotics and neuroprosthetics as applied to patients with spinal cord injury, stroke, and Parkinsonism. Dr. Pons has authored more than 150 peer-reviewed articles and has developed methods for studying balance and tremor, creating robotic manipulators and mobility devices for children with cerebral palsy, modifying computer cursors for patients with limited mobility, and developing movement sensors for amputees. His expertise in physics enables him to perform fundamental analyses of devices and movement patterns applicable to various movement disorders. Prior to joining Shirley Ryan AbilityLab in 2019, he built his academic career at the Spanish National Research Council (CSIC) in Madrid, where he was director of the Neural Rehabilitation Group in the Department of Translational Neuroscience. Dr. Pons received his BS in mechanical engineering from the University of Navarra and his PhD in physics from Complutense University of Madrid. He is a Fellow of the American Institute of Medical and Biological Engineering (AIMBE) and serves as an associate editor for several scientific journals.

Research topics

• Computer Science
• Artificial Intelligence
• Human–computer interaction
• Risk analysis (engineering)
• Embedded system
• Simulation
• Data science
• Engineering

Selected publications

• Leveraging neural drive to assess hand motor function in individuals with chronic stroke

  Journal of NeuroEngineering and Rehabilitation · 2026-01-08 · 1 citations

  articleOpen access

  BACKGROUND: Stroke is a leading cause of disability, with up to 80% of survivors experiencing motor impairments. These impairments are attributed to various factors, including reduced neural drive and altered motor unit firing patterns. Rehabilitation aims to restore motor function by enhancing motor unit recruitment and synchronization. High-density electromyography (HD-EMG) is a valuable tool for evaluating these changes in motor unit activity. METHODS: We tested a wearable HD-EMG forearm sleeve to investigate the relationship between motor function and motor unit properties including firing rate, motor unit module activation, and coherence. Seven individuals with chronic stroke and seven able-bodied individuals attempted 12 cued hand and wrist movements while EMG was recorded. Motor units were decomposed across all movements using convolutive blind source separation. RESULTS: Fewer motor units were detectable in individuals with stroke compared to able-bodied participants. There was a significant reduction in motor unit firing rate during specific movements such as wrist flexion and hand open. Motor unit coupling and activation were altered following stroke, with reduced module activation in 8 of the 12 attempted movements. Furthermore, a reduction in coherence for gross movements and an increase in coherence for more dexterous thumb movements suggest altered neural drive to motor units after stroke that is differentially tuned to the complexity of movement. A combined neural control signature, consisting of multiple motor unit features, demonstrated strong correlation ([Formula: see text]) with clinical motor function scores. CONCLUSIONS: This study demonstrates that HD-EMG can capture detailed motor unit activity and neural control characteristics across multiple forearm muscles in individuals with chronic stroke. By integrating multiple HD-EMG features, this approach provides new insights into neuromuscular alterations linked to hand motor function after stroke. These findings support the use of HD-EMG for monitoring recovery, predicting outcomes, and guiding more targeted rehabilitation, thus advancing both stroke research and patient care.

  PublisherOA PDFDOI

• Haptic interaction with a human partner for ankle training in chronic stroke: a pilot study

  Research Square · 2025-08-20

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Somatosensory burst peripheral nerve stimulation focally upregulates corticospinal and spinal excitability in the upper limb

  medRxiv · 2025-05-29

  preprintOpen accessSenior authorCorresponding

  ABSTRACT Peripheral nerve stimulation (PNS) is commonly used in research and clinical settings for pain management and for augmenting somatosensory input for motor recovery. Its functional effects are dependent on stimulation parameters such as frequency, intensity, and duration of stimulation. Recently, interest in temporally modulated PNS (burst PNS), in which high-frequency carrier pulses are demodulated to low-frequency bursts, has increased. Burst PNS applied below the motor threshold (sensory) has been used to suppress pain and tremor. However, the effects of burst sensory PNS (sPNS) on corticospinal and spinal excitability are unknown, limiting its application. We evaluated the impact of a session of burst sPNS on corticospinal excitability through motor-evoked potentials (MEPs) and spinal excitability through F-wave and H-reflex assessments targeting the first dorsal interosseous (FDI) and flexor carpi radialis (FCR) muscles. Ten healthy participants underwent a randomized crossover study with two experimental visits, in which corticospinal and spinal excitability were evaluated before and after a session (40 min) of burst sPNS at the wrist or no stimulation (control). Compared with the control condition, burst sPNS resulted in a focal increase in MEP amplitudes (p < 0.001) in the FDI muscle, but not in the FCR muscle (p = 0.26). Similarly, only the F-wave amplitude increased following burst sPNS (p = 0.008) for the FDI muscle compared to the control condition, but no differences were observed in the H-reflex amplitude (p = 0.33) in the FCR muscle between the burst sPNS and the control condition. Our findings suggest that burst sPNS modulates spinal and corticospinal excitability in the short term (5–10 min in this study). However, the relative changes in cortical and spinal levels due to burst sPNS are unknown, and the timeline for these continued aftereffects requires further investigation. Trial registration NCT04501133

  PublisherOA PDFDOI

• Evaluation of lipid levels and lipid-lowering therapy by age and sex in dyslipidaemic Mexican population

  European Journal of Preventive Cardiology · 2025-07-12

  articleOpen access
  PublisherOA PDFDOI

• Closed-Loop Sensory Peripheral Nerve Stimulation Provides Tremor Suppression by Decreasing Tremorgenic Drive to the Wrist Extensors and Flexors in Essential Tremor

  Biosystems & biorobotics · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Machine-Learning Based Intuitive Control of Lower-Limb Assistive Exoskeletons

  Biosystems & biorobotics · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Development of a Customizable Assistance Algorithm for Lower-Limb Exoskeleton

  Biosystems & biorobotics · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Deep-Learning Control of Lower-Limb Exoskeletons via Simplified Therapist Input

  2025-05-12 · 3 citations

  articleSenior author

  Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of "normal" walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations of hierarchical control in exoskeletons, this work proposes a three-step, data-driven approach: (1) using recent sensor data to probabilistically infer locomotion states (landing step length, landing step height, walking velocity, step clearance, gait phase), (2) allowing therapists to modify these features via a user interface, and (3) using the adjusted locomotion features to predict the desired joint posture and model stiffness in a spring-damper system based on prediction uncertainty. We evaluated the proposed approach with two healthy participants performing treadmill walking and stair ascent/descent at varying speeds, with and without external modification of the gait features through a user interface. Results showed a variation in kinematics according to the gait characteristics and a negative interaction power ($-2.1 \pm 1.6 \mathrm{W}$ for the hip and $-0.6 \pm 1.4 \mathrm{W}$ for the knee joints) suggesting exoskeleton assistance across the different conditions.

  PublisherDOI

• Exoskeleton-Mediated Physical Teacher-Student Interaction for Gait Training: A Pilot Study

  Biosystems & biorobotics · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Impact of Sensory Burst Nerve Stimulation on Corticospinal Excitability in Forearm Muscles

  Biosystems & biorobotics · 2025-01-01

  book-chapterSenior author
  PublisherDOI

Recent grants

• NRI: INT: Co-Robot Controllers for Human-Like Physical Interaction and Improved Motor Learning

  NSF · $1.5M · 2020–2025

Frequent coauthors

• Juan C. Moreno

  278 shared

• Diego Torricelli

  184 shared

• Eduardo Rocón

  157 shared

• Filipe Oliveira Barroso

  150 shared

• Yue Wen

  Beijing Institute of Technology

  116 shared

• Dario Farina

  103 shared

• Antonio J. del‐Ama

  Universidad Rey Juan Carlos

  84 shared

• Sangjoon J. Kim

  University of California, Irvine

  82 shared

Awards & honors

• Fellow of the American Institute of Medical and Biological E…