Solid-State Electro-Mechanical Transducers for Sensing and Actuation Haptic Enhanced Prosthetics
Bibliographic record
Abstract
Upper limb amputations severely impact individuals' quality of life, affecting physical functioning, mobility, and proprioception. Traditional and affordable prosthetic devices often lack sensory feedback, leading to difficulties in use and prosthesis rejection. This article explores the development of a power-autonomous, solid-state haptic feedback prosthetic system based on dielectric elastomers (DEs). The soft devices detect mechanical pressure and convert the signal into actuator vibration, delivered to existing, innervated anatomical parts that relay the signal to the user's brain. Integrating capacitive DE-based pressure sensors with DEA-based vibrotactile armbands offers a potential solution to restore sensory feedback, enabling users to perceive touch and regain enhanced perception of the environment. The proposed system addresses challenges in energy consumption and device autonomy. Detailed fabrication methods are provided for the actuators, sensors and the power autonomous integration system that enables joint operation. Characterization studies demonstrate the system's effectiveness, and a user study confirms its potential for frequency discrimination and strength perception. This innovative approach using DE-based technology presents a promising avenue for enhancing upper limb prosthetics and improving the quality of life for amputees.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".