From the Delsys Blog

Dr. William Gerin shares expert guidance on choosing the right collaborators, crafting a compelling Specific Aims page, and preparing pilot data that convinces reviewers your research is feasible and worth funding.

In the final installment of the NIH Grant Writing Series, Dr. William Gerin shares expert advice on building a realistic budget, navigating new NIH policies, and writing clearly and engagingly so reviewers understand and support your research.

Myoelectric-controlled lower limb prostheses use EMG signals to actuate movement, but assumptions about neural activation between residual and intact limbs may need revision. New research reveals that amputation alters motor unit properties in ways that directly impact prosthetic control models.

EMG pattern recognition translates muscle signals into prosthetic movements, enabling more natural limb function for patients with upper-limb deficiencies. Two recent studies demonstrate how input data quality and type significantly improve actuation accuracy in myoelectric prosthetics.

Electromyography-driven musculoskeletal models can track muscle fatigue in real-time, with applications in sports training, rehabilitation, and manual handling. This post explores a novel time-varying model (t-pEMS) that uses EMG data to estimate fatigue progression with person-specific accuracy.

EMG-guided Human Robot Collaboration (HRC) enables automated optimization of worker posture and joint torque in industrial settings, reducing muscle effort and improving ergonomics during high-effort tasks.

Delsys EMG systems help sports performance and rehabilitation professionals measure asymmetry, fatigue, compensation, and muscle inhibition in athletes — translating precise data into actionable insights for return-to-play decisions.

Cardiff University researchers combine TMS and Delsys EMG during dynamic walking tasks to reveal how the brain and spinal cord coordinate muscle activity in real-world movement — with direct applications in fall prevention and rehabilitation.

By combining EMG and ultrasound imaging, neuromechanics research reveals how muscles activate and adapt mechanically and neurally — opening the door to personalized rehabilitation and deeper insights into individual movement signatures.

Human-in-the-loop methods using EMG biosignals and machine learning are transforming wearable robotics, enabling more personalized assistive devices that reduce muscle effort and device abandonment.