Eliciting Force and Slippage in Upper Limb Amputees Through Transcutaneous Electrical Nerve Stimulation (TENS)
Bibliographic record
Abstract
Upper limb amputation severely affects the quality of life of individuals. Therefore, developing closed-loop upper-limb prostheses would enhance the sensory-motor capabilities of the prosthetic user. Considering design priorities based on user needs, the restoration of sensory feedback is one of the most desired features. This study focuses on employing Transcutaneous Electrical Nerve Stimulation (TENS) as a non-invasive somatotopic stimulation technique for restoring somatic sensations in upper-limb amputees. The aim of this study is to propose two encoding strategies to elicit force and slippage sensations in transradial amputees. The former aims at restoring three different levels of force through a Linear Pulse Amplitude Modulation (LPAM); the latter is devoted to elicit slippage sensations through Apparent Moving Sensation (AMS) by means of three different algorithms, i.e. the Pulse Amplitude Variation (PAV), the Pulse Width Variation (PWV) and Inter-Stimulus Delay Modulation (ISDM). Amputees had to characterize perceived sensations and to perform force and slippage recognition tasks. Results demonstrates that amputees were able to correctly identify low, medium and high levels of force, with an accuracy above the 80% and similarly, to also discriminate the slippage moving direction with a high accuracy above 90%, also highlighting that ISDM would be the most suitable method, among the three AMS strategies to deliver slippage sensations. It was demonstrated for the first time that the developed encoding strategies are effective methods to somatotopically reintroduce in the amputees, by means of TENS, force and slippage sensations.
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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 | 0.001 |
| 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) | 0.001 | 0.000 |
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".