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Objective: To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking., Design: Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users., Setting: Rehabilitation hospital training apartment., Participants: Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18)., Interventions: Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials., Main Outcome Measure: Percent correct classification., Results: With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly., Conclusions: We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies., (Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

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.

Objective: To characterize the compensatory movements of the trunk during functional reaching tasks performed by upper limb prosthesis users., Design: Survey., Setting: Clinical laboratory at a national rehabilitation hospital., Participants: Transhumeral and transradial prosthesis users (n=10) and uninjured control subjects (n=10)., Interventions: Not applicable., Main Outcome Measures: Three-dimensional motion analysis data were collected during simulated reaching tasks, such as donning a cap, placing a nut, and sorting clothes. The metrics were range of motion of the trunk in the 3 anatomical directions and elbow and shoulder path distance., Results: Prosthesis users had significantly larger truncal movements than controls during all 3 reaching tasks in all 3 directions (P≤.03). Shoulder path distance in persons with amputation was larger than in controls in all 3 tasks (P<.01). Elbow path distance in persons with amputation was larger than in controls in the nut and clothes tasks (P≤.02). The subgroup of transradial prosthesis users displayed these abnormal movements despite the presence of an intact elbow., Conclusions: The altered physiologic structure of the arm caused the individuals to develop a different motor control strategy than an intact arm. Functional limitations, such as the loss of distal degrees of freedom, required persons with amputation to use trunk displacement in place of arm/hand movement. These compensatory movements during reaching tasks may be a cause of prosthesis rejection and, in some cases, may be resolved with proper rehabilitative training. Analysis of compensatory trunk movements may also provide a useful endpoint for evaluating new prosthesis designs., (Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Objectives: To examine the impact of a new prosthesis on an experienced and highly motivated prosthetic limb user, to evaluate the effects of training and the ability of clinical measures to detect change, and to gain insight into the mechanisms by which improvement occurs., Design: A single-case study., Setting: An outpatient clinic., Participant: A bilateral high-arm amputee (right shoulder disarticulation, left above elbow)., Interventions: Provision of new prosthesis and occupational therapy., Main Outcome Measures: Action Research Arm Test, box and block test of manual dexterity, Jebsen-Taylor Hand Function Test, and speed and accuracy of reaching movements with and without visual guidance., Results: In this experienced prosthesis user, provision of a new prosthesis led to an immediate worsening in functional limitation. With training, the subject recovered his baseline status and then exceeded it in both proximal and distal function. All study clinical measures detected change, but the change detected varied as much as 300-fold depending on the measure chosen. The clinical improvements were associated with modest improvements in the speed of reaching but not the accuracy of reaching under visual guidance. Improvements in reaching accuracy without visual guidance were seen after 10 trials, suggesting that some motor learning had occurred., Conclusions: Provision of a new prosthesis can cause functional decline even in an experienced user; this decline can be reversed with training. There is wide variability in sensitivity to change among functional limitation measures. Although some training-related improvements may have been due to increased speed and accuracy of reaching without visual guidance, skill in prosthesis use also plays a role.