Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition
Bibliographic record
Abstract
The world population is aging in the last few years and this trend is expected to increase in the future. The number of persons requiring assistance in their everyday life is also expected to rise. Luckily, smart homes are becoming a more and more compelling alternative to direct human supervision. Smart homes are equipped with sensors that, coupled with Artificial Intelligence (AI), can support their occupants whenever needed. At the heart of the problem is the recognition of activities. Human activity recognition is a complex problem due to the variety of sensors available, their impact on privacy, the high number of possible activities, and the several ways even a simple activity can be performed. This paper proposes a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. As regards activity recognition, experiments were conducted in a real and furnished apartment where 15 different activities were performed by 10 participants. Results showed an improvement of 18.63% over previous work on the same dataset with 65.59% in Top-1 accuracy using Leave-One-Subject-Out cross validation. Furthermore, the experiments that address movement recognition were conducted under the same conditions where a single participant was asked to perform four distinct arm movements with the three UWB radars positioned at two different heights. With an overall accuracy of 73% in Top-1, the detailed analysis of the results obtained showed that the proposed model was capable of recognizing accurately large and fine-grained movements. However, the medium-sized movements demonstrated a significant impact on the movement recognition due to an insufficient degree of variation between the four proposed movements.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".