Hybrid Soft-Rigid Active Prosthetics Laboratory Exercise for Hands-On Biomechanical and Biomedical Engineering Education
Bibliographic record
Abstract
This paper introduces a hands-on laboratory exercise focused on assembling and testing a hybrid soft-rigid active finger prosthetic for biomechanical and biomedical engineering (BME) education. This hands-on laboratory activity focuses on the design of a myoelectric finger prosthesis, integrating mechanical, electrical, sensor (i.e., inertial measurement units (IMUs), electromyography (EMG)), pneumatics, and embedded software concepts. We expose students to a hybrid soft-rigid robotic system, offering a flexible, modifiable lab activity that can be tailored to instructors' needs and curriculum requirements. All necessary files are made available in an open-access format for implementation. Off-the-shelf components are all purchasable through global vendors (e.g., DigiKey Electronics, McMaster-Carr, Amazon), costing approximately USD 100 per kit, largely with reusable elements. We piloted this lab with 40 undergraduate engineering students in a neural and rehabilitation engineering upper year elective course, receiving excellent positive feedback. Rooted in real-world applications, the lab is an engaging pedagogical platform, as students are eager to learn about systems with tangible impacts. Extensions to the lab, such as follow-up clinical (e.g., prosthetist) and/or technical (e.g., user-device interface design) discussion, are a natural means to deepen and promote interdisciplinary hands-on learning experiences. In conclusion, the lab session provides an engaging journey through the lifecycle of the prosthetic finger research and design process, spanning conceptualization and creation to the final assembly and testing phases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".