{"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\/nb\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/","title":{"rendered":"Forskere har utviklet en AI-modell for \u00e5 oppdage cannabisbruk"},"content":{"rendered":"

Forskerne brukte data fra smarttelefoner og Fitbit-armb\u00e5nd for \u00e5 finne ut n\u00e5r folk var h\u00f8ye med 85%-n\u00f8yaktighet. <\/strong><\/p>\n

Cannabisbruk bestemmes tradisjonelt ved hjelp av urin-, spytt- og h\u00e5rstr\u00e5tester. Disse metodene krever imidlertid laboratorieanalyser, og det er ikke sikkert at et umiddelbart cannabisforbruk er umiddelbart synlig i noen tester.\u00a0<\/span><\/p>\n

Sang Won Bae og hennes team fra Stevens Institute of Technology i New Jersey hadde som m\u00e5l \u00e5 fremskynde prosessen med \u00e5 bestemme farlige forgiftningsniv\u00e5er.<\/span><\/p>\n

I deres <\/span>studie<\/span><\/a>rapporterte 33 hyppige cannabisbrukere sitt daglige forbruk over en periode p\u00e5 30 dager.<\/span><\/p>\n

Gjennom hele perioden hadde deltakerne p\u00e5 seg aktivitetsm\u00e5lere - nemlig Fibits - som registrerte blant annet hjertefrekvens, antall skritt og s\u00f8vnm\u00f8nster. <\/span><\/p>\n

I mellomtiden overv\u00e5ket telefonsensorene mikrobevegelsene deres, for eksempel hvordan de h\u00e5ndterte telefonen, for \u00e5 evaluere koordinasjon og stabilitet.<\/span><\/p>\n

Etter \u00e5 ha samlet inn forel\u00f8pige deltakerdata, trente teamet opp maskinl\u00e6ringsmodellen til \u00e5 gjenkjenne potensielle tegn p\u00e5 cannabisbruk. <\/span><\/p>\n

Da den trente modellen ble testet p\u00e5 det gjenv\u00e6rende datasettet, oppn\u00e5dde den en treffsikkerhet p\u00e5 85% n\u00e5r det gjaldt \u00e5 identifisere personer som hadde inntatt cannabis i l\u00f8pet av de foreg\u00e5ende fem minuttene.<\/span><\/p>\n

Wearables som Fitbits samler inn enorme mengder data fra brukerne sine, men \u00e5 bruke dem til \u00e5 forutsi om noen er ruset, ville v\u00e6rt enormt kontroversielt.\u00a0<\/span><\/p>\n

N\u00e5r det gjelder AI-ens n\u00f8yaktighet, nevner Chung at den er avhengig av deltakernes egenrapportering, noe som hindrer bruk i den virkelige verden.\u00a0<\/span><\/p>\n

Hvordan studien fungerte<\/span><\/h2>\n
    \n
  1. M\u00e5lsetting:<\/b> Forskerne satte seg fore \u00e5 finne ut om en kombinasjon av smarttelefonsensorer og en Fitbit kunne oppdage episoder med akutt marihuanaforgiftning i sanntid.<\/span><\/li>\n
  2. Metodikk:<\/b> I l\u00f8pet av 30 dager rapporterte 33 unge voksne sitt marihuanakonsum og tilh\u00f8rende f\u00f8lelser av rus. De registrerte sine subjektive f\u00f8lelser i l\u00f8pet av 15 minutter etter bruk av marihuana og for tre semi-tilfeldige daglige instruksjoner.<\/span><\/li>\n
  3. Variabler som m\u00e5les:<\/b> Deltakerne vurderte sin beruselse p\u00e5 en skala: \"ikke beruset\" (poengsum = 0), \"lav beruselse\" (poengsum = 1-3) og \"moderat til h\u00f8y beruselse\" (poengsum = 4-10). Smarttelefonsensorer og Fitbit m\u00e5lte faktorer som hjertefrekvens, mikrobevegelser og st\u00f8rre bevegelser bestemt av GPS (kalt makrobevegelser eller gyrasjonsradius) og st\u00f8yniv\u00e5et i n\u00e6rmilj\u00f8et.<\/span><\/li>\n
  4. Resultater:<\/b> Ved \u00e5 modellere dataene med EXtreme Gradient Boosting Machine-klassifiseringsmaskinen (XGBoost) kunne forskerne oppdage om folk hadde konsumert cannabis de siste fem minuttene med en n\u00f8yaktighet p\u00e5 85%.<\/span><\/li>\n<\/ol>\n

    Overv\u00e5king av mennesker gjennom deres wearables for \u00e5 fastsl\u00e5 potensielt cannabisbruk - det er en m\u00e5te \u00e5<\/span> \u00f8ke paranoiaen deres.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskerne brukte data fra smarttelefoner og Fitbit-armb\u00e5nd for \u00e5 avgj\u00f8re n\u00e5r folk var ruset med 85% n\u00f8yaktighet. Cannabisbruk bestemmes tradisjonelt via urin-, spytt- og h\u00e5rstr\u00e5tester. Disse metodene krever imidlertid laboratorieanalyse, og det er ikke sikkert at et umiddelbart cannabisforbruk er umiddelbart synlig i noen tester. Sang Won Bae og hennes team fra Stevens Institute of Technology i New Jersey hadde som m\u00e5l \u00e5 fremskynde prosessen med \u00e5 bestemme farlige rusniv\u00e5er. I studien deres rapporterte 33 hyppige cannabisbrukere sitt daglige forbruk over en periode p\u00e5 30 dager. I l\u00f8pet av denne perioden hadde deltakerne p\u00e5 seg aktivitetsm\u00e5lere - nemlig Fibits - som registrerte<\/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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