Investigation into the Potential to Create a Force Myography-based Smart-home Controller for Aging Populations
Bibliographic record
Abstract
Force Myography (FMG) quantifies the volumetric changes in a limb occurring with muscle contraction and can potentially be used to design convenient, low-cost interfaces to assist in activities of daily living (ADL). The aim of this study is to evaluate whether elders can effectively use an FMG-based wrist band to interact with their environment. In this regard, an FMG band consisted of an array of force-sensing resistors (FSRs) was designed. Ten participants were grouped in two classes, namely “senior” and “non-senior”, and were instructed to perform control gestures and unconstrained ADL tasks while wearing the designed wrist band. To evaluate the usability of the band, correct identification of hand gestures and reaction times were noted. Results showed that seniors were capable of successfully performing a control gesture within 1.4 s of displaying the instruction during online testing. The individually-trained gesture identification algorithm achieved an accuracy of 76.5% in this case. Non-seniors had a reaction time of 0.9 s with an overall classification accuracy of 91.2%. This preliminary study demonstrates the potential and feasibility of utilizing FMG-based technology to provide elders with assistance during activities of daily living.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".