Automatic cough detection via a multi-sensor smart garment using machine learning
Notice bibliographique
Résumé
Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect quality of life in those affected. In this context, coughing quantification is often important, but routinely performed via questionnaires. This approach is dependent on patient compliance or recall, which may affect validity and be especially difficult for nocturnal coughs. Manual review of audio recordings is potentially more accurate, but raises privacy concerns due to the collection and review of sensitive audio-data by a human annotator. Today, machine learning approaches are increasingly used to quantify coughs; however, algorithms often rely on microphone recordings, resulting in the same privacy issues, especially if data are sent to a remote server for analysis. The aims of this study are to determine if (1) a suite of sensors, excluding microphone recordings, can accurately detect coughs unobtrusively and (2) what the relative importance of each sensor-type on model performance may be. Data from 44 healthy young adult participants performing on-demand coughs and other tasks (breathing, talking, throat clearing, laughing, sniffing) in supine and sitting conditions were collected for this observational, cross-sectional study using a multi-sensor smart-garment device. Synchronized video was used to annotate tasks. Three-dimension acceleration, respiration (inductance plethysmography), and electrical activity (electrocardiography) signals were extracted into 1 s strips and binarized into coughs and non-coughs. Data were split into train and test sets using an inter-subject 80:20 split, ensuring that data from a particular participant are found in a single set. This procedure was repeated 10 times with different random inter-subject splits to assess the variability of results. Statistical and frequency-based features were computed and used as inputs to a Random Forest Classifier to predict classes (cough vs not-cough). Model hyperparameters were tuned to maximize F1-score using five-fold cross validation of the training set. Final model performance was assessed using F1-score, precision, and recall (sensitivity) on the test sets with mean (standard deviation) reported. Single sensor models based on acceleration, respiration, or electrocardiography revealed F1 scores of 92.6 (1.2)%, 88.9 (3.2)%, and 77.5 (3.4)%, respectively. Overall, the dual (acceleration, respiration) sensor model achieved the highest performance (F1-score 93.0 (1.1)%, precision 84.2 (4.2)%, and recall 95.5 (1.6)%). The multi-modal wearable device was able to distinguish coughs from other respiratory maneuvers, with acceleration and respiration sensors providing the most valuable information. Future studies could implement this approach for remote monitoring of coughs in patients suffering from coughing symptoms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».