FSR-Based Smart System for Detection of Wheelchair Sitting Postures Using Machine Learning Algorithms and Techniques
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an intelligent system containing FSR-based posture detection using machine learning algorithms. This paper is aimed at detecting the sitting posture of a wheelchair user. Individuals using wheelchairs are at increased risk of pressure ulcers when they hold an incorrect position for too long because the blood supply desists at some points of their skin due to increased pressure. The main objective of this research is to find a better configuration combined with the best machine learning algorithm for the detection of posture to prevent pressure ulcers. In the proposed monitoring system, two configurations consisting of a <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mn>3</a:mn> <a:mo>×</a:mo> <a:mn>3</a:mn> </a:math> matrix configuration (9 sensors) and a crossconfiguration (5 sensors) of FSR sensors are embedded on a wheelchair seat to get pressure data generated and collected in a real-time processing unit and then compared. The posture recognition is performed for five sitting positions: ideal, backward-leaning, forward-leaning, right-leaning, and left-leaning based on five machine learning algorithms: <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>K</c:mi> </c:math> -nearest neighbors ( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>K</e:mi> </e:math> -NN), logistic regression (LR), decision tree (DT), support vector machines (SVM), and LightGBM. The research study provides a system to detect a real-time pressure sitting posture on a processing unit (laptop) wirelessly using the ESP32 module. Consequently, a posture classification accuracy of up to 95.41% is accomplished using a <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mn>3</g:mn> <g:mo>×</g:mo> <g:mn>3</g:mn> </g:math> matrix configuration. The proposed system helps prevent pressure ulcers and is valuable in risk assessment related to pressure ulcers. This system describes the relationship between accuracy, different sensor configurations, and performance of the multiple machine learning algorithms.
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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 | 0.001 | 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 | 0.000 |
| 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 it