Smart Phone Based Human Activity Recognition
Bibliographic record
Abstract
Human Activity Recognition (HAR) is a field that uses collected data to classify different human actions. One simple and general approach to HAR is to use the sensor data from a mobile device to recognize different patterns behind complex motions. Early studies show promising results on simple activities using manually selected features from accelerometer readings. As newer publicly available datasets include more complex data and activities, manual feature selection have become cumbersome, impractical and face limitations in finding the optimal feature sets for HAR. In this paper, we present an empirical approach to defining models of 3D tensor data structures from 2D time series data obtained from multiple sensors on a smart phone, and a new Convolutional Neural Network (CNN) model, which uses the tensor data and performs automatic feature extraction and classification for HAR. We use the public benchmark dataset, MobiAct v2.0, to train and validate our model, which achieved an overall better performance in classifying 11 Activities of Daily Living (ADL) than the state-of-the-art approaches. Compared to the approach presented by Chatzaki et al. which has a very high rate of misclassifications for car-step out (CSO), car-step in (CSI), sit to stand (CHU), and stand to sit (SCH) classes, our proposed approach has 15% higher sensitivity for each of these activities with the optimal number of training epochs being only 25.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.006 |
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; both teacher heads agree on what is shown here.
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".