{"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\/da\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/","title":{"rendered":"Forskere har udviklet en AI-model til at opdage cannabisbrug"},"content":{"rendered":"

Forskerne brugte data fra smartphones og Fitbit wearables til at bestemme, hvorn\u00e5r folk var h\u00f8je med 85%-n\u00f8jagtighed. <\/strong><\/p>\n

Cannabisbrug bestemmes traditionelt via urin-, spyt- og h\u00e5rstr\u00e5tests. Men disse metoder kr\u00e6ver laboratorieanalyser, og et umiddelbart cannabisforbrug er m\u00e5ske ikke umiddelbart synligt i nogle tests.\u00a0<\/span><\/p>\n

Sang Won Bae og hendes team fra Stevens Institute of Technology i New Jersey havde til form\u00e5l at fremskynde processen med at bestemme farlige forgiftningsniveauer.<\/span><\/p>\n

I deres <\/span>unders\u00f8gelse<\/span><\/a>33 hyppige cannabisbrugere rapporterede deres daglige forbrug over en periode p\u00e5 30 dage.<\/span><\/p>\n

Under hele forl\u00f8bet bar deltagerne aktivitetsm\u00e5lere - nemlig Fibits - som registrerede m\u00e5linger som puls, antal skridt og s\u00f8vnm\u00f8nstre. <\/span><\/p>\n

I mellemtiden overv\u00e5gede telefonsensorer deres mikrobev\u00e6gelser, s\u00e5som telefonens h\u00e5ndteringsm\u00f8nstre, for at evaluere koordination og stabilitet.<\/span><\/p>\n

Efter at have indsamlet forel\u00f8bige deltagerdata tr\u00e6nede teamet maskinl\u00e6ringsmodellen til at genkende potentielle tegn p\u00e5 cannabisbrug. <\/span><\/p>\n

Da den blev testet p\u00e5 det resterende datas\u00e6t, opn\u00e5ede den tr\u00e6nede model en n\u00f8jagtighed p\u00e5 85% i forhold til at identificere personer, der havde indtaget cannabis inden for de foreg\u00e5ende 5 minutter.<\/span><\/p>\n

Wearables som Fitbits indsamler store m\u00e6ngder data fra deres brugere, men at udnytte dem til at forudsige, om nogen er sk\u00e6ve, ville v\u00e6re enormt kontroversielt.\u00a0<\/span><\/p>\n

Med hensyn til AI'ens n\u00f8jagtighed n\u00e6vner Chung, at den er afh\u00e6ngig af deltagernes selvrapportering, hvilket h\u00e6mmer dens anvendelse i den virkelige verden.\u00a0<\/span><\/p>\n

S\u00e5dan fungerede unders\u00f8gelsen<\/span><\/h2>\n
    \n
  1. M\u00e5ls\u00e6tning:<\/b> Forskerne satte sig for at finde ud af, om en kombination af smartphonesensorer og en Fitbit pr\u00e6cist kunne registrere episoder af akut marihuanaforgiftning i realtid.<\/span><\/li>\n
  2. Metodologi:<\/b> I l\u00f8bet af 30 dage rapporterede 33 unge voksne deres forbrug af marihuana og tilh\u00f8rende f\u00f8lelser af beruselse. De registrerede deres subjektive f\u00f8lelser inden for 15 minutter efter brug af marihuana og for tre semi-tilf\u00e6ldige daglige prompter.<\/span><\/li>\n
  3. M\u00e5lte variabler:<\/b> Deltagerne vurderede deres beruselse p\u00e5 en skala: \"ikke beruset\" (score = 0), \"lav beruselse\" (score = 1-3) og \"moderat til h\u00f8j beruselse\" (score = 4-10). Smartphone-sensorer og Fitbit m\u00e5lte faktorer som puls, mikrobev\u00e6gelser og st\u00f8rre bev\u00e6gelser bestemt af GPS (kaldet makrobev\u00e6gelse eller radius of gyration) og st\u00f8jenerginiveauer i deres umiddelbare omgivelser.<\/span><\/li>\n
  4. Resultater:<\/b> Ved at modellere dataene med EXtreme Gradient Boosting Machine-klassifikatoren (XGBoost) kunne forskerne registrere, om folk havde indtaget cannabis inden for de sidste fem minutter med en n\u00f8jagtighed p\u00e5 85%.<\/span><\/li>\n<\/ol>\n

    Overv\u00e5gning af mennesker via deres wearables for at bestemme potentielt cannabisforbrug - Det er en m\u00e5de at<\/span> \u00f8ge deres paranoia-niveau.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskerne brugte data fra smartphones og Fitbit-armb\u00e5nd til at afg\u00f8re, hvorn\u00e5r folk var h\u00f8je med 85% n\u00f8jagtighed. Cannabisbrug bestemmes traditionelt via urin-, spyt- og h\u00e5rstr\u00e5test. Men disse metoder kr\u00e6ver laboratorieanalyse, og \u00f8jeblikkeligt cannabisforbrug er m\u00e5ske ikke umiddelbart synligt i nogle tests. Sang Won Bae og hendes team fra Stevens Institute of Technology, New Jersey, havde til form\u00e5l at fremskynde processen med at bestemme farlige forgiftningsniveauer. I deres unders\u00f8gelse rapporterede 33 hyppige cannabisbrugere deres daglige forbrug over en periode p\u00e5 30 dage. I hele denne periode bar deltagerne aktivitetsm\u00e5lere - nemlig Fibits - som registrerede<\/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\/da\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/\" \/>\n<meta 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