Fusion of Dual Sensor Features for Fall Risk Assessment with Improved Attention Mechanism
Bibliographic record
Abstract
Nowadays, falls are one of the important causes of accidental death in older people.Assessment of fall risk can help to protect older adults in a timely manner.Current studies tend to use a single type of sensor, which always suffers from insufficient robustness, and the accuracy of the risk assessment model is low.In this study, we proposed a Convolutional Neural Network (CNN)-Bi Long Short-Term Memory (LSTM) fall risk assessment model based on the fusion of multi-sensor information with improved efficient channel attention (ISA-ECA-CNN-BiLSTM). Firstly, we construct a hybrid network consisting of a LSTM network and a CNN, which can capture the features hidden in asynchronous gait data sequences very well.An improved efficient Channel Attention Mechanism was also incorporated to make the model more attentive to the global features of the gait.Since the features extracted from the plantar pressure distribution signal and the IMU signal do not contribute to the fall risk assessment to the same extent, an adaptive weighted feature fusion method was introduced to enhance the influence of important features on the assessment results while weakening the influence of unimportant features on the assessment results.The improved method has higher sensitivity, specificity, and accuracy compared to the direct cascade method.The experimental results show that the accuracy, precision, sensitivity, and F1-score of the ISA-ECA-CNN-BiLSTM model proposed in this study were 98.4%, 99.1%, 98.8%, and 98.9%, respectively, which are higher than other classification models and can effectively extract gait features, thus improving the accuracy of fall risk recognition.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".