Human Activity Recognition Algorithm Based on One-Dimensional Convolutional Neural Network
Bibliographic record
Abstract
Human activity recognition (HAR) is widely used in healthcare, personal fitness, physical training and military, etc.How to distinguish various human activities accurately (such as running, walking, walking upstairs and downstairs, jumping and standing) has become an important problem in human-computer interaction.The computer vision method requires a large amount of computing resources, and it is not highly accuracy and can be easily disturbed by other objects in the background.The sensor-based method can achieve high accuracy, and it requires few computing resources, and is not disturbed by the background.This paper proposes a method based on the one-dimensional convolutional neural network (1D-CNN) to classify the sensor signals of some different activities.For comparison, this paper applies some widely used methods to accomplish the recognition task with the same dataset.Then, it tests the proposed 1D-CNN model with different datasets, for the purpose of testing its generality across users.The experimental results show that the proposed model achieves an accuracy of 95.12% with the said datasets, which is higher than those of the other methods by about 8% on average.This indicates that the proposed method has good performance in terms of generality across users, and at the same time provides a higher accuracy.The obtained results can improve the accuracy of current technologies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".