STPT: Spatio-Temporal Polychromatic Trajectory Based Elderly Exercise Evaluation System
Bibliographic record
Abstract
This paper introduces an elderly exercise evaluation system. To determine the quality of a performed exercise, the authors propose a novel system to generate and use Spatio-Temporal Polychromatic Trajectory (STPT) images. Usually, the elder people need to perform some exercises or take physiotherapy in order to stay healthy both physically and mentally. It becomes difficult to evaluate the quality of their exercise routine without the aid of a trained physiotherapist. The system aims to overcome this problem by allowing elders to record their exercise videos using an easy-to-use Graphical User Interface and evaluate the results. A dataset of 109 subjects performing four types of shoulder exercises several times was created. The videos are labelled as correct or incorrect and an STPT image is generated from each video. Using our newly introduced method, the movement of the elder person is projected into an image which can be input to a Convolutional Neural Network (CNN). The dataset is further augmented to increase accuracy. Using our proposed method, the best model achieved an F1 Score over 90% in three of the four exercises. The CNN is trained based on these clips and the models are added to the backend of the interface. The proposed system requires only an ordinary camera and a computer with an entry level GPU allowing it to be deployed at a large scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".