Multi-Body Modeling of Human Musculoskeletal System for an Exercise Therapy Method and its Verification
Bibliographic record
Abstract
The objective of this paper is to establish a concise structural model of the human musculoskeletal system (HMS) that can be applied to an exercise therapy that treats malfunctions or distortions of the human body. There exist a number of traditional exercise therapy methods in Japan and China, but any systematic approaches for learning, coaching or training are not found to the best of the author’s knowledge. Among such approaches, we deal with an exercise therapy called Somatic Balance Restoring Therapy (SBRT) in which a patient executes a series of non-invasive and painless motions in face-up/down laid posture. Although thousands of results have been piled up in a fixed-format data base, justification for the SBRT has not been provided in bio/mechanical engineering sense. The purpose of modeling is a first step for this holistic approach. For such reasons, the model must be useful and uncomplicated for therapists to identify the problematic areas of the human body with adequate visualization while maintaining a theoretical thoroughness in mechanics or dynamics. To bridge multi-body dynamics and the SBRT, we have utilized a human body model with a collection of joint connected 15 rigid bodies in a topological tree configuration as used for humanoid robot with 80 Degrees-of-Freedom (DOF). In order to achieve the purpose stated above, we have developed a static force/torque balance equation for each body element. In addition, we will describe modeling processes, derivation of static equations, and estimation of parameters/states and verification based on the analysis of the FPS experimental data, and contact forces are parameterized with quantitative values to be given by the Force Plate System (FPS), installed at CARIS at the University of British Columbia (UBC).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".