A Radar-Based Opioid Overdose Detection Device for Public Restrooms: Design, Development, and Evaluation Study
Notice bibliographique
Résumé
BACKGROUND: The opioid epidemic is a growing crisis worldwide. While many interventions have been put in place to try to protect people from opioid overdoses, they typically rely on the person to take initiative in protecting themselves, requiring forethought, preparation, and action. Respiratory depression or arrest is the mechanism by which opioid overdoses become fatal, but it can be reversed with the timely administration of naloxone. OBJECTIVE: In this study, we described the development and validation of an opioid overdose detection radar (ODR), specifically designed for use in public restroom stalls. In-laboratory testing was conducted to validate the noncontact, privacy-preserving device against a respiration belt and to determine the accuracy and reliability of the device. METHODS: We used an ODR system with a high-frequency pulsed coherent radar sensor and a Raspberry Pi (Raspberry Pi Ltd), combining advanced technology with a compact and cost-effective setup to monitor respiration and detect opioid overdoses. To determine the optimal position for the ODR within the confined space of a restroom stall, iterative testing was conducted, considering the radar's bounded capture area and the limitations imposed by the stall's dimensions and layout. By adjusting the orientation of the ODR, we were able to identify the most effective placement where the device reliably tracked respiration in a number of expected positions. Experiments used a mock restroom stall setup that adhered to building code regulations, creating a controlled environment while maintaining the authenticity of a public restroom stall. By simulating different body positions commonly associated with opioid overdoses, the ODR's ability to accurately track respiration in various scenarios was assessed. To determine the accuracy of the ODR, testing was performed using a respiration belt as a reference. The radar measurements were compared with those obtained from the belt in experiments where participants were seated upright and slumped over. RESULTS: The results demonstrated favorable agreement between the radar and belt measurements, with an overall mean error in respiration cycle duration of 0.0072 (SD 0.54) seconds for all recorded respiration cycles (N=204). During the simulated overdose experiments where participants were slumped over, the ODR successfully tracked respiration with a mean period difference of 0.0091 (SD 0.62) seconds compared with the reference data. CONCLUSIONS: The findings suggest that the ODR has the potential to detect significant deviations in respiration patterns that may indicate an opioid overdose event. The success of the ODR in these experiments indicates the device should be further developed and implemented to enhance safety and emergency response measures in public restrooms. However, additional validation is required for unhealthy opioid-influenced respiratory patterns to guarantee the ODR's effectiveness in real-world overdose situations.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».