A Vision-based Deep Learning Platform for Human Motor Activity Recognition
Bibliographic record
Abstract
To track the body movement of patients with movement disorders, sensors such as Kinect cameras are not easily accessible. Recently-developed deep learning models, as a subset of Artificial Intelligence (AI), can analyze patients’ behavior from RGB images of smartphones. The Stacked Hourglass model is a novel pose estimation deep learning model which can accurately determine the location of body joints and a long short-term memory network (LSTM) can determine the corresponding action by analyzing the kinematic behavior of the body joints. This study develops a deep learning model that uses RGB images from the UT-Kinect dataset as input and determines the action performed with 84.14 % accuracy. Specifically, our contributions are: (i) developed the preprocessing pipeline to use stack hourglass model on the UT-kinect dataset (ii) finetuning of the model to handle 20 joints (iii) Added a human action recognition component to accurately classify the actions performed. Our method can be an efficient replacement for the hardly-accessible Kinect cameras and can be used to analyze various diseases with movement disorders.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".