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Updated: Apr 25, 2026

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
Published on: October 27, 2023
Internal models of upper limb prosthesis users when grasping and lifting a fragile object with their prosthetic limb
Peter S Lum1, Iian Black, Rahsaan J Holley
1Biomedical Engineering, The Catholic University of America, Pangborn Hall, room 131, 620 Michigan Ave NE, Washington, DC, 20064, USA, lum@cua.edu.
Abstract:
Internal models allow unimpaired individuals to appropriately scale grip force when grasping and lifting familiar objects. In prosthesis users, the internal model must adapt to the characteristics of the prosthetic devices and reduced sensory feedback. We studied the internal models of 11 amputees and eight unimpaired controls when grasping and lifting a fragile object. When the object was modified from a rigid to fragile state, both subject groups adapted appropriately by significantly reducing grasp force on the first trial with the fragile object compared to the rigid object (p < 0.020). There was a wide range of performance skill illustrated by amputee subjects when lifting the fragile object in 10 repeated trials. One subject, using a voluntary close device, never broke the object, four subjects broke the fragile device on every attempt and seven others failed on their initial attempts, but improved over the repeated trials. Amputees decreased their grip forces 51 ± 7 % from the first to the last trial (p < 0.001), indicating a practice effect. However, amputees used much higher levels of force than controls throughout the testing (p < 0.015). Amputees with better performance on the Box and Blocks test used lower grip force levels (p = 0.006) and had more successful lifts of the fragile object (p = 0.002). In summary, amputees do employ internal models when picking up objects; however, the accuracy of these models is poor and grip force modulation is significantly impaired. Further studies could examine the alternative sensory modalities and training parameters that best promote internal model formation.

