Human Activity Classification in Underwater using Sonar and Deep Learning
Bibliographic record
Abstract
In this paper, we study the classification of human activity on the surface of a body of water using sonar. In particular, we investigate the classification of three different swimming styles; freestyle, butterfly, and backstroke. Experiments are conducted in a swimming pool to capture acoustic micro-Doppler signatures produced by the different swimming styles. Two acoustic hydrophones are used underwater; one to transmit a single tone signal in the direction of a swimmer and the other to receive the reflected waveform from the swimmer's body. We apply joint time-frequency analysis on the received acoustic signal to extract the micro-Doppler signatures present in the spectrogram. Each of these swimming style activities presents their own unique micro-Doppler signatures. To classify the acoustic micro-Doppler signatures, we explore a deep convolution neural network (DCNN) algorithm. Spectrogram can be considered as an image in which case applying DCNN can serve well for feature recognition purposes. We show that using the spectrogram images the DCNN algorithm can classify different swimming styles performed on the surface of the water with fairly high accuracy. Using the collected data set, we performed experiments where we used 80% of the data for training and the remaining 20% for validation purposes. The DCNN algorithm averaged 93.7% accuracy during training while it had a 90.8% average validation accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".