Comparative Study of Time Series-based Human Activity Recognition using Convolutional Neural Networks
Bibliographic record
Abstract
Human Activity Recognition (HAR) is an emerging active research area which applies both digital signal processing methods and machine learning algorithms to predict motion activity based on ubiquitously available sensor measurements. Inertial sensors are commonly used in HAR systems. Deep learning (DL) techniques, especially using Convolutional Neural Network (CNN), have been widely used due to their structure flexibility and high classification accuracy. In this paper, we study different CNN structures and test their generalization ability under various conditions. The aim is to find the best CNN model for HAR systems in obtaining higher prediction rates using short time segment of inertial measurements data (1 s) compared to longer time segments (2.5 s) that are used in current literature. The results from this study show that using a simple 1-D shallow CNN fused with standard global statistical features of the input time signal provide the highest precision and recall. The study also shows that enhancing the architecture to multimodal CNN improves the recognition rate of some activities. The system was tested on two publicly available datasets, namely UCI HAR and m-Health. The proposed architecture achieved recognition accuracy of 97.12% using subject-based cross validation of UCI HAR dataset. Moreover, the generalization ability of the UCI HAR based system was verified by testing on m-Health dataset. This cross-dataset evaluation showed an overall accuracy of 93.09%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".