Advancements of Upper Limb Prostheses can Improve Patient Quality of Life: A Technology Review
Bibliographic record
Abstract
Introduction: Based on the evolution of hand prosthetics, many original shortcomings have been addressed and further rectified. Some common past limitations include limited degrees of freedom, strained motion control/fine movements, the weight of the design, and lack of ability to do thumb-index pinch. Henceforth, this review highlights and assesses the effects of hand- prosthetics advancements on the quality of life of patients with/requiring joint replacements. Methods: Relevant literature between 2013 to 2023 was obtained using Web of Science and PubMed. Search terms were “Upper limb prostheses” OR “Body replacement technologies” AND “technology” as well as “Upper limb prostheses” AND “technology” AND “Quality of life” OR “Improvement”. Literature was selected based on applicability to key aspects of the research topic after assessing the results and abstracts. Results: Studies support the theory that over the decade, the ability to accomplish fine motion control with hand-prosthetics has aided in improving patients' quality of life. Overall, advancements to prosthetic design, prosthetic sensors, and waterproofing designs via myoelectricity, insulated and biopotential sensors, as well as waterproofing technologies, were reported to contribute greatly to patients’ quality of life. Discussion: As technological advances aid in improved dexterity and motility, recently advanced prosthetics help promote independence and confidence, and in some cases, decrease the cost of living for some patients. Myoelectric prostheses, flexible insulated sensors, capacitive biopotential sensors, and waterproofing technologies, have shown trends in increased dexterity, patient comfort and flexibility, as well as increased degrees of freedom of movement. The most notable limitations to these advancements were limited accessibility, comfort challenges, and lack of large-scale patient assessment. Hence, future advancements in further research and patient testing of these technologies were suggested. Conclusion: This review will be deemed as supporting material for healthcare providers and policy makers while making decisions on the allocation of resources to ensure that patients from all demographics can acquire accessible technologies, improving their qualities of life. In addition, the advancements of hand prosthetic technologies from mechanics and robotics research can provide implications for the next-generation technologies with the determination to improve patients’ life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".