The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Consumer design priorities for upper limb prosthetics
Abstract
PURPOSE: To measure consumer satisfaction with upper limb prosthetics and provide an enumerated list of design priorities for future developments. METHODS: A self-administered, anonymous survey collected information on participant demographics, history of and goals for prosthesis use, satisfaction, and design priorities. The questionnaire was available online and in paper format and was distributed through healthcare providers, community support groups, and one prosthesis manufacturer; 242 participants of all ages and levels of upper limb absence completed the survey. RESULTS: Rates of rejection for myoelectric hands, passive hands, and body-powered hooks were 39%, 53%, and 50%, respectively. Prosthesis wearers were generally satisfied with their devices while prosthesis rejecters were dissatisfied. Reduced prosthesis weight emerged as the highest priority design concern of consumers. Lower cost ranked within the top five design priorities for adult wearers of all device types. Life-like appearance is a priority for passive/cosmetic prostheses, while improved harness comfort, wrist movement, grip control and strength are required for body-powered devices. Glove durability, lack of sensory feedback, and poor dexterity were also identified as design priorities for electric devices. CONCLUSIONS: Design priorities reflect consumer goals for prosthesis use and vary depending on the type of prosthesis used and age. Future design efforts should focus on the development of more light-weight, comfortable prostheses.
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“Particularly for those who have lost upper limbs, natural sensory feedback through the prostheses is especially desired (Biddiss et al., 2007; Pylatiuk et al., 2007; Wijk and Carlsson, 2015).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Biddiss et al., 2007
In the citing paper
“Fifty percent of users do still prefer body-powered prostheses, possibly for improved feedback or referred sensation (Biddiss et al., 2007).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Biddiss et al., 2007
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Metadata
- Authors
- Biddiss, E., Beaton, D., & Chau, T.
- Affiliations
- University of Toronto (CA)
- Institute for Work & Health (CA)
- Corresponding author
- Chau, Tom
- Venue
- Disability and Rehabilitation: Assistive Technology
- Publisher
- Informa UK Limited
- Published
- 2007-01
- Volume
- 2
- Issue
- 6
- Pages
- 346-357
- ISSN
- 1748-3107 (ISSN-L)
- 1748-3115
- OpenAlex
- W2051230789
- Cited by
- 519
- References
- 48
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 22 · 2023: 41 · 2022: 47 · 2021: 50 · 2020: 35 · 2019: 55 · 2018: 32 · 2017: 36 · 2016: 30 · 2015: 24 · 2014: 27 · 2013: 23 · 2012: 21
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Disability and Rehabilitation Assistive Technology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- upper limb prosthesis, customer satisfaction, design priorities, myoelectric hand, prosthesis rejection, prosthesis weight, prosthesis cost, grip control, sensory feedback, glove durability, dexterity, age differences
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Artificial Limbs, Child, Equipment Design, Female, Humans, Male, Middle Aged, Surveys and Questionnaires, Patient Satisfaction, Upper Extremity, Qualitative Research, Young Adult
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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