Automatically Segmenting Physical Performance Test Items for Older Adults Using a Doppler Radar: A Proof of Concept Study
Bibliographic record
Abstract
Assessing the performance of physical activities through the modified physical performance test (mPPT) is a known approach for predicting the frailty level in older adults. This study proposes a system comprising of a continuous-wave (CW) radar for data acquisition and deep neural network (DNN) models (convolutional neural network (CNN) and convolutional recurrent neural network (CRNN)) as classifiers to automatically segment the mPPT items. These two DNN models were trained and evaluated in a leave-one- participant-out (LOPO) cross-validation procedure with a transfer learning method. To segment the mPPT items during recording by the radar, an additional flag activity was employed, which involves having the volunteer wave their hands at the start of each activity. Compared to the CNN, the CRNN achieved better classification performance, with the f1-score ranging from 0.3445 (lifting a book) to 0.9509 (standing balance). The recognition result was then used to segment the time series data and predict each item’s duration. The average absolute duration prediction error ranged from 0.78 s (standing balance) to 2.78 s (climbing stairs). The result implied that the system has the potential to segment mPPT items automatically. Future works will be focused on accomplish all the evaluation criteria automatically, e.g., the steadiness and continuity of steps while turning 360°, and improve the low classification result of some mPPT items like lifting a book.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".