{"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\/sv\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/","title":{"rendered":"Forskare har utvecklat en AI-modell f\u00f6r att uppt\u00e4cka cannabisanv\u00e4ndning"},"content":{"rendered":"

Forskarna anv\u00e4nde data fr\u00e5n smartphones och Fitbit-armband f\u00f6r att avg\u00f6ra n\u00e4r m\u00e4nniskor var h\u00f6ga med 85% noggrannhet. <\/strong><\/p>\n

Cannabisanv\u00e4ndning fastst\u00e4lls traditionellt via urin-, saliv- och h\u00e5rstr\u00e5tester. Dessa metoder kr\u00e4ver dock laboratorieanalys, och omedelbar cannabisanv\u00e4ndning kanske inte syns omedelbart i vissa tester.\u00a0<\/span><\/p>\n

Sang Won Bae och hennes team fr\u00e5n Stevens Institute of Technology, New Jersey, hade som m\u00e5l att p\u00e5skynda processen f\u00f6r att fastst\u00e4lla farliga f\u00f6rgiftningsniv\u00e5er.<\/span><\/p>\n

I sina <\/span>studie<\/span><\/a>rapporterade 33 frekventa cannabisanv\u00e4ndare sin dagliga konsumtion under en period p\u00e5 30 dagar.<\/span><\/p>\n

Under hela tiden bar deltagarna aktivitetsm\u00e4tare - n\u00e4mligen Fibits - som registrerade m\u00e4tv\u00e4rden som hj\u00e4rtfrekvens, antal steg och s\u00f6mnm\u00f6nster. <\/span><\/p>\n

Samtidigt \u00f6vervakade telefonsensorer deras mikror\u00f6relser, till exempel hur de hanterade telefonen, f\u00f6r att utv\u00e4rdera koordination och stabilitet.<\/span><\/p>\n

Efter att ha samlat in prelimin\u00e4ra deltagardata tr\u00e4nade teamet maskininl\u00e4rningsmodellen f\u00f6r att k\u00e4nna igen potentiella tecken p\u00e5 cannabisanv\u00e4ndning. <\/span><\/p>\n

N\u00e4r den tr\u00e4nade modellen testades p\u00e5 det \u00e5terst\u00e5ende datasetet uppn\u00e5dde den en tr\u00e4ffs\u00e4kerhet p\u00e5 85% n\u00e4r det g\u00e4llde att identifiera personer som hade konsumerat cannabis under de f\u00f6reg\u00e5ende 5 minuterna.<\/span><\/p>\n

B\u00e4rbara produkter som Fitbits samlar in enorma m\u00e4ngder data fr\u00e5n sina anv\u00e4ndare, men att anv\u00e4nda dem f\u00f6r att f\u00f6ruts\u00e4ga om n\u00e5gon \u00e4r h\u00f6g skulle vara oerh\u00f6rt kontroversiellt.\u00a0<\/span><\/p>\n

N\u00e4r det g\u00e4ller AI:ns noggrannhet n\u00e4mner Chung att den \u00e4r beroende av deltagarnas sj\u00e4lvrapportering, vilket hindrar dess verkliga anv\u00e4ndningsomr\u00e5den.\u00a0<\/span><\/p>\n

Hur studien fungerade<\/span><\/h2>\n
    \n
  1. M\u00e5ls\u00e4ttning:<\/b> Forskarna ville ta reda p\u00e5 om en kombination av smartphonesensorer och en Fitbit exakt kunde uppt\u00e4cka episoder av akut marijuanaf\u00f6rgiftning i realtid.<\/span><\/li>\n
  2. Metodik:<\/b> Under 30 dagar rapporterade 33 unga vuxna sin marijuanakonsumtion och tillh\u00f6rande k\u00e4nslor av berusning. De registrerade sina subjektiva k\u00e4nslor inom 15 minuter efter att ha anv\u00e4nt marijuana och f\u00f6r tre semi-slumpm\u00e4ssiga dagliga uppmaningar.<\/span><\/li>\n
  3. Variabler som m\u00e4ts:<\/b> Deltagarna bed\u00f6mde sin berusning p\u00e5 en skala: \"inte berusad\" (po\u00e4ng = 0), \"l\u00e5g berusning\" (po\u00e4ng = 1-3) och \"m\u00e5ttlig till h\u00f6g berusning\" (po\u00e4ng = 4-10). Sensorer i smartphones och Fitbit m\u00e4tte faktorer som hj\u00e4rtfrekvens, mikror\u00f6relser och st\u00f6rre r\u00f6relser som best\u00e4mdes av GPS (s\u00e5 kallade makror\u00f6relser eller gyrationsradie) samt energiniv\u00e5er f\u00f6r buller i den omedelbara omgivningen.<\/span><\/li>\n
  4. Resultat:<\/b> Genom att modellera data med klassificeringsverktyget EXtreme Gradient Boosting Machine (XGBoost) kunde forskarna uppt\u00e4cka om personer hade konsumerat cannabis under de senaste fem minuterna med en noggrannhet p\u00e5 85%.<\/span><\/li>\n<\/ol>\n

    \u00d6vervakning av m\u00e4nniskor genom deras wearables f\u00f6r att avg\u00f6ra potentiellt cannabisbruk - det \u00e4r ett s\u00e4tt att<\/span> \u00f6ka deras paranoia niv\u00e5er.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskare anv\u00e4nde data fr\u00e5n smartphones och Fitbit-b\u00e4rbara enheter f\u00f6r att avg\u00f6ra n\u00e4r m\u00e4nniskor var h\u00f6ga med 85%-noggrannhet. Cannabisanv\u00e4ndning best\u00e4ms traditionellt via urin-, saliv- och h\u00e5rstr\u00e4ngstester. Dessa metoder kr\u00e4ver dock laboratorieanalys, och omedelbar cannabiskonsumtion kanske inte \u00e4r omedelbart uppenbar i vissa tester. Sang Won Bae och hennes team fr\u00e5n Stevens Institute of Technology, New Jersey, hade som m\u00e5l att p\u00e5skynda processen f\u00f6r att fastst\u00e4lla farliga berusningsniv\u00e5er. I deras studie rapporterade 33 frekventa cannabisanv\u00e4ndare sin dagliga konsumtion under en period av 30 dagar. Under hela denna period bar deltagarna aktivitetssp\u00e5rare - n\u00e4mligen Fibits - som registrerade<\/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\" href=\"https:\/\/dailyai.com\/sv\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/\" 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