Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
Bibliographic record
Abstract
The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. A paralyzed person with poor sitting posture is highly likely to slip out of their wheelchair. To prevent this from happening and consistently maintain paralyzed individuals under observation, a new model, the Emperor Penguin Optimized Sensor-Infused Wheelchair (EPIC), has been designed to monitor the position and health of the individual in the wheelchair in real-time. A Force Sensitive Resistor (FSR) sensor and an ultrasonic sensor continuously transmit information to the Arduino UNO R4 Wi-Fi board. The Emperor Penguin Optimizer Algorithm (EPOA) was used to select the features sent from the Arduino board to the ESP8266-Wi-Fi module. A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. A mobile application for Android collects data from the ESP32 module and estimates posture to inform the caretaker about the user's current posture and health status. Evaluation metrics such as precision, accuracy, sensitivity, and specificity have been used to determine the efficiency in the EPIC framework, which improves overall accuracy by 10.1%, 7.73%, and 2.84% for better posture recognition.
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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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".