From on-body sensors to in-body data for health monitoring and medical robotics: A survey
Bibliographic record
Abstract
The estimation of musculoskeletal data such as muscle forces and joint torques could have a significant impact on patient care monitoring and medical robotics as well as on reducing healthcare and industrial costs by improving the treatment in the field of rehabilitation. Direct measurement of these data is now non-invasive, as they are computed from dedicated wireless on-body sensors, which can synchronously measure segment positions, muscle activation, external forces and allow to estimate muscle force and joint torques using musculoskeletal models. This paper presents a state-of-the-art survey reviewing both the most commonly used on-body sensors, over the last thirty years, to compute in-body data and the most popular optokinetic cameras. The results are presented and classified into tables which show the evolution of on-body sensors since the 1980's, but also the challenges that lie ahead, as very accurate sensors only accentuate the faults of an inaccurate musculoskeletal model. The survey results show that there is a lack of studies validating the different musculoskeletal models. In addition, current interfaces between hardware and software could be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".