{"id":6191,"date":"2023-10-06T18:02:58","date_gmt":"2023-10-06T18:02:58","guid":{"rendered":"https:\/\/dailyai.com\/?p=6191"},"modified":"2023-10-06T18:12:33","modified_gmt":"2023-10-06T18:12:33","slug":"researchers-have-developed-an-ai-model-for-detecting-cannabis-use","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/","title":{"rendered":"I ricercatori hanno sviluppato un modello di intelligenza artificiale per rilevare il consumo di cannabis"},"content":{"rendered":"

I ricercatori hanno utilizzato i dati provenienti dagli smartphone e dagli indossabili Fitbit per determinare quando le persone erano alte con una precisione di 85%. <\/strong><\/p>\n

Il consumo di cannabis viene tradizionalmente determinato tramite test delle urine, della saliva e dei capelli. Tuttavia, questi metodi richiedono analisi di laboratorio e il consumo immediato di cannabis potrebbe non essere immediatamente evidente in alcuni test.\u00a0<\/span><\/p>\n

Sang Won Bae e il suo team dello Stevens Institute of Technology, nel New Jersey, hanno cercato di accelerare il processo di determinazione dei livelli di intossicazione pericolosi.<\/span><\/p>\n

Nel loro <\/span>studio<\/span><\/a>33 consumatori abituali di cannabis hanno riferito il loro consumo giornaliero nell'arco di 30 giorni.<\/span><\/p>\n

Per tutta questa durata, i partecipanti hanno indossato degli activity tracker, in particolare i Fibit, che hanno registrato parametri come la frequenza cardiaca, il conteggio dei passi e il ritmo del sonno. <\/span><\/p>\n

Nel frattempo, i sensori del telefono hanno monitorato i loro micromovimenti, come i modelli di manipolazione del telefono, per valutare la coordinazione e la stabilit\u00e0.<\/span><\/p>\n

Dopo aver raccolto i dati preliminari dei partecipanti, il team ha addestrato il modello di apprendimento automatico a riconoscere i potenziali segni del consumo di cannabis. <\/span><\/p>\n

Quando \u00e8 stato testato sul restante set di dati, il modello addestrato ha raggiunto un tasso di accuratezza di 85% nell'identificare gli individui che avevano consumato cannabis nei 5 minuti precedenti.<\/span><\/p>\n

I dispositivi indossabili come i Fitbit raccolgono grandi quantit\u00e0 di dati dai loro utenti, ma attingere a questi dati per prevedere se qualcuno \u00e8 fatto o meno sarebbe estremamente controverso.\u00a0<\/span><\/p>\n

Per quanto riguarda l'accuratezza dell'intelligenza artificiale, Chung afferma che essa si basa sull'autodichiarazione dei partecipanti, il che ne ostacola l'utilizzo nel mondo reale.\u00a0<\/span><\/p>\n

Come ha funzionato lo studio<\/span><\/h2>\n
    \n
  1. Obiettivo:<\/b> I ricercatori hanno cercato di capire se la combinazione di sensori per smartphone e Fitbit potesse rilevare con precisione gli episodi di intossicazione acuta da marijuana in tempo reale.<\/span><\/li>\n
  2. Metodologia:<\/b> Nell'arco di 30 giorni, 33 giovani adulti hanno riferito il loro consumo di marijuana e le sensazioni di intossicazione associate. Hanno registrato le loro sensazioni soggettive entro 15 minuti dal consumo di marijuana e per tre volte al giorno in modo semicasuale.<\/span><\/li>\n
  3. Variabili misurate:<\/b> I partecipanti hanno valutato la loro intossicazione su una scala: \"non intossicato\" (punteggio = 0), \"intossicazione bassa\" (punteggio = 1-3) e \"intossicazione da moderata a elevata\" (punteggio = 4-10). I sensori dello smartphone e il Fitbit hanno misurato fattori quali la frequenza cardiaca, i micromovimenti e i movimenti pi\u00f9 ampi determinati dal GPS (definiti macromovimenti o raggio di rotazione) e i livelli di energia sonora nell'ambiente circostante.<\/span><\/li>\n
  4. Risultati:<\/b> Modellando i dati con il classificatore EXtreme Gradient Boosting Machine (XGBoost), i ricercatori hanno potuto rilevare se le persone avevano consumato cannabis negli ultimi cinque minuti con una precisione di 85%.<\/span><\/li>\n<\/ol>\n

    Monitoraggio delle persone attraverso i loro dispositivi indossabili per determinare il potenziale uso di cannabis - questo \u00e8 un modo per<\/span> aumentare i livelli di paranoia.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    I ricercatori hanno usato dati provenienti da smartphone e indossabili Fitbit per determinare quando le persone erano fatte con una precisione di 85%. Il consumo di cannabis viene tradizionalmente determinato attraverso test su urina, saliva e capelli. Tuttavia, questi metodi richiedono analisi di laboratorio e il consumo immediato di cannabis potrebbe non essere immediatamente evidente in alcuni test. Sang Won Bae e il suo team dello Stevens Institute of Technology, nel New Jersey, hanno cercato di accelerare il processo di determinazione dei livelli di intossicazione pericolosi. Nel loro studio, 33 consumatori abituali di cannabis hanno riferito il loro consumo quotidiano per un periodo di 30 giorni. Per tutta questa durata, i partecipanti hanno indossato degli activity tracker - i Fibit - che registravano<\/p>","protected":false},"author":2,"featured_media":6192,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[105,421],"class_list":["post-6191","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-machine-learning","tag-wearables"],"yoast_head":"\nResearchers have developed an AI model for detecting cannabis use | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers used data from smartphones and Fitbit wearables to determine when people were high with 85% accuracy.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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