# Investigating Fall Mitigation Strategies when Walking with an Exoskeleton for Users with Lower-Limb Paralysis > **NIH VA IK1** · LOUIS STOKES CLEVELAND VA MEDICAL CENTER · 2024 · — ## Abstract Paralysis as a result of a spinal cord injury can severely impact activities of daily living, limit independence, and negatively impact overall health of Veterans. Powered exoskeletons are an evolving technology aiming to improve mobility and quality of life for individuals with paralysis. Clinical studies of users walking with exoskeletons report a reduction in pain and spasticity and improvements to bone density and muscle tone. Currently, commercial exoskeletons have FDA 510(k) clearances that require continuous contact guarding or stand-by-assistance by a spotter. During instances of falling, these devices are only designed to enter joint-locking configurations and require the user to balance themselves via crutches, a walker, or intervention from a caregiver to prevent impact with the ground. The rationale of this CDA-1 is that the absence of safety features that prevent falling while wearing an exoskeleton may be restricting the technology to monitored environments with trained caregivers and be preventing their widespread use within the community. The purpose of this award is to provide Dr. Hnat with the required training to become a successful researcher within the VA system and to lead her towards achieving her long term goal of designing an exoskeleton that detects destabilizations and prevents falls from occuring. The current proposal addresses the candidate’s short term goals regarding the issue of falling with powered exoskeletons by exploring configurations to reduce the impact with the ground and the potential of injury should a fall occur. The proposed work is immediately translatable into a hybrid neuroprosthesis resulting from the primary mentor’s funded research (Dr. Ronald Triolo) and will serve as preliminary data for subsequent CDA-2 and VA Merit Award proposals. The project goals will be achieved through two Specific Aims. Specific Aim 1 includes a series of able- bodied walking experiments while wearing an exoskeleton while experiencing a slip or trip on an instrumented treadmill. A Fall Classification Algorithm (FCA) will predict the occurrence and classify the type of a fall using center of mass kinematics measured from the experiments. Specific Aim 2 focuses on identifying the exoskeletal configurations that minimize the magnitudes and locations of impact forces on the user and applying this information to design an Impact Mitigation Controller (IMC) that reconfigures the exoskeleton’s joint angles into these safer configurations. Optimizations of a 3D musculoskeletal model developed in the OpenSim Modeling Suite will minimize the impact forces at the head, joints (neck, shoulder, elbow, wrist, hip, knee, and ankle), and long bones. These experiments will be repeated on an instrumented crash-test dummy and the potential for injury of the reconfigured positions will be statistically compared to the baselines. The trained FCA and IMC will be validated in real-time in a repeated set of experiments with able-bodied partici... ## Key facts - **NIH application ID:** 10862056 - **Project number:** 1IK1RX004265-01A2 - **Recipient organization:** LOUIS STOKES CLEVELAND VA MEDICAL CENTER - **Principal Investigator:** Sandra Hnat - **Activity code:** IK1 (R01, R21, SBIR, etc.) - **Funding institute:** VA - **Fiscal year:** 2024 - **Award amount:** — - **Award type:** 1 - **Project period:** 2024-03-01 → 2026-02-28 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10862056 ## Citation > US National Institutes of Health, RePORTER application 10862056, Investigating Fall Mitigation Strategies when Walking with an Exoskeleton for Users with Lower-Limb Paralysis (1IK1RX004265-01A2). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10862056. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*