Prosthetic Use in Adult Upper Limb Amputees: A Comparison of the Body Powered and Electrically Powered Prostheses

Three hundred and fourteen adult upper limb amputees were reviewed retrospectively at the Ontario Workers' Compensation Board. A questionnaire was used to evaluate the use of body and electrically powered prostheses. Follow-up ranged from 1 to 49 years with a mean of 15 years. Results indicated that complete or useful acceptance of an electrically powered prosthesis was reported by 69 of 83 amputees (83%); 199 of 291 amputees (68%) used the cable operated hook, 57 of 291 (20%) used the cable operated hand and 40 of 83 (48%) used the cosmetic prosthesis. The majority of amputees used more than one prosthesis for their functional needs and should be fitted with more than one type of prosthesis. Acceptance of an upper limb prosthesis by 89% (196/220) of below-elbow, 76% (56/74) of above-elbow and 60% (12/20) of high level amputees indicates that for most upper limb amputees, their prostheses are well used and essential to their personal and employment activities.

Download Full-text

Upper Limb Prosthesis Control for High-Level Amputees via Myoelectric Recognition of Leg Gestures

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2018.2807360 ◽

2018 ◽

Vol 26 (5) ◽

pp. 1056-1066 ◽

Cited By ~ 10

Author(s):

Kenneth R. Lyons ◽

Sanjay S. Joshi

Keyword(s):

Upper Limb ◽

Upper Limb Prosthesis ◽

Prosthesis Control ◽

High Level ◽

Limb Prosthesis

Download Full-text

Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use

PLoS ONE ◽

10.1371/journal.pone.0246795 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246795

Author(s):

Sophie L. Wang ◽

Conor Bloomer ◽

Gene Civillico ◽

Kimberly Kontson

Keyword(s):

Machine Learning ◽

Outcome Measures ◽

Upper Limb ◽

Degrees Of Freedom ◽

Clustering Algorithm ◽

The Body ◽

Movement Quality ◽

Upper Limb Prosthesis ◽

Norm Group ◽

Limb Prosthesis

To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of “normal movement” and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared to healthy controls. 24 participants with no UL disability or impairment were recruited for this study and trained on the use of a body-powered bypass (n = 6) or the DEKA limb bypass (n = 6) prosthetic devices or included as normative controls. 3D motion capture data were collected from all participants as they performed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test (tBBT). Range of Motion, peak angle, angular path length, mean angle, peak angular velocity, and number of zero crossings were calculated from joint angle data for the right/left elbows, right/left shoulders, torso, and neck and fed into a K-means clustering algorithm. Results show right shoulder and torso DOFs to be most informative in distinguishing between bypass user and norm group movement. The JHFT page turning task and the seated tBBT elicit movements from bypass users that are most distinctive from the norm group. Results can be used to inform the development of movement quality scoring methodology for UL performance-based outcome measures. Identifying tasks across two different devices with known variations in movement can inform the best tasks to perform in a rehabilitation setting that challenge the prosthesis user’s ability to achieve normative movement.

Download Full-text