ArticleSleep & breathing = Schlaf & Atmung2026
GA-SPARF: Genetic algorithm-tuned random forest for sleep position monitoring using accelerometer.
Article in Sleep & breathing = Schlaf & Atmung, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveThis study presents GA-SPARF, a wearable Internet of Things (IoT) system for real-time sleep posture monitoring aimed at supporting Gastroesophageal Reflux Disease (GERD) management. Unlike prior studies that typically classify a limited number of sleep postures (e.g., 4–5 classes), GA-SPARF enables real-time classification across 12 distinct sleep positions with high accuracy and low power consumption, thereby enhancing its clinical applicability and scalability for long-term home-based monitoring.
methodsThe system integrates a single ADXL345 accelerometer positioned on the abdomen, an ESP8266 microcontroller, and Wi-Fi connectivity for real-time data transmission. Data were collected from 20 participants in a controlled environment across 12 labeled postures. Preprocessing involved noise filtering, missing value imputation, and extraction of statistical and kinematic features. A Random Forest (RF) model was optimized using a Genetic Algorithm (GA) and benchmarked against Decision Tree (DT), Gradient-Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) classifiers using 5-fold cross-validation.
resultsThe GA-optimized RF model achieved an average accuracy and F1-score of 99.79 ± 0.02%, outperforming all baseline models. Inference time was 3 ms per prediction, with minimal power consumption. Clinically significant postures—supine (D), right lateral (R), and left lateral (DL)—were classified with 100% sensitivity, positive predictive value (PPV), and negative predictive value (NPV), underscoring the system’s clinical relevance. Despite minor confusion between adjacent postures, the overall classification performance remained consistently high.
conclusionGA-SPARF demonstrates state-of-the-art performance in sleep posture classification using a compact and low-cost setup. Unlike previous studies, it supports a broader set of postures and enables real-time deployment on constrained hardware. This work offers a practical tool for GERD care and lays the groundwork for future innovations in wearable health monitoring.
Indexed as
Identifiers
41784887What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.