{"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\/pt\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/","title":{"rendered":"Os investigadores desenvolveram um modelo de IA para detetar o consumo de can\u00e1bis"},"content":{"rendered":"

Os investigadores utilizaram dados de smartphones e de wearables Fitbit para determinar quando as pessoas estavam pedradas com uma precis\u00e3o de 85%. <\/strong><\/p>\n

O consumo de can\u00e1bis \u00e9 tradicionalmente determinado atrav\u00e9s de testes de urina, saliva e fios de cabelo. No entanto, estes m\u00e9todos exigem an\u00e1lises laboratoriais e o consumo imediato de can\u00e1bis pode n\u00e3o ser imediatamente vis\u00edvel em alguns testes.\u00a0<\/span><\/p>\n

Sang Won Bae e a sua equipa do Stevens Institute of Technology, em Nova J\u00e9rsia, pretendiam acelerar o processo de determina\u00e7\u00e3o dos n\u00edveis de intoxica\u00e7\u00e3o perigosos.<\/span><\/p>\n

Na sua <\/span>estudo<\/span><\/a>Em uma pesquisa, 33 consumidores frequentes de cannabis relataram o seu consumo di\u00e1rio durante 30 dias.<\/span><\/p>\n

Durante todo este tempo, os participantes usaram rastreadores de atividade - nomeadamente os Fibits - que registaram m\u00e9tricas como o ritmo card\u00edaco, a contagem de passos e os padr\u00f5es de sono. <\/span><\/p>\n

Entretanto, os sensores do telefone monitorizavam os seus micromovimentos, como os padr\u00f5es de manuseamento do telefone, para avaliar a coordena\u00e7\u00e3o e a estabilidade.<\/span><\/p>\n

Depois de recolher dados preliminares dos participantes, a equipa treinou o modelo de aprendizagem autom\u00e1tica para reconhecer potenciais sinais de consumo de can\u00e1bis. <\/span><\/p>\n

Quando testado no restante conjunto de dados, o modelo treinado atingiu uma taxa de precis\u00e3o de 85% na identifica\u00e7\u00e3o de indiv\u00edduos que tinham consumido can\u00e1bis nos 5 minutos anteriores.<\/span><\/p>\n

Os wearables como os Fitbits recolhem grandes quantidades de dados dos seus utilizadores, mas utiliz\u00e1-los para prever se algu\u00e9m est\u00e1 pedrado seria extremamente controverso.\u00a0<\/span><\/p>\n

Relativamente \u00e0 precis\u00e3o da IA, Chung refere que esta depende da autoavalia\u00e7\u00e3o dos participantes, o que dificulta a sua utiliza\u00e7\u00e3o no mundo real.\u00a0<\/span><\/p>\n

Como funcionou o estudo<\/span><\/h2>\n
    \n
  1. Objetivo:<\/b> Os investigadores tentaram descobrir se a combina\u00e7\u00e3o de sensores de smartphone e um Fitbit poderia detetar com precis\u00e3o epis\u00f3dios em tempo real de intoxica\u00e7\u00e3o aguda por marijuana.<\/span><\/li>\n
  2. Metodologia:<\/b> Durante 30 dias, 33 jovens adultos relataram o seu consumo de marijuana e as sensa\u00e7\u00f5es de intoxica\u00e7\u00e3o que lhe estavam associadas. Registaram as suas sensa\u00e7\u00f5es subjectivas nos 15 minutos que se seguiram ao consumo de marijuana e em tr\u00eas momentos di\u00e1rios semi-aleat\u00f3rios.<\/span><\/li>\n
  3. Vari\u00e1veis medidas:<\/b> Os participantes classificaram a sua intoxica\u00e7\u00e3o numa escala: \"n\u00e3o intoxicado\" (pontua\u00e7\u00e3o = 0), \"intoxica\u00e7\u00e3o baixa\" (pontua\u00e7\u00e3o = 1-3) e \"intoxica\u00e7\u00e3o moderada a alta\" (pontua\u00e7\u00e3o = 4-10). Os sensores do smartphone e o Fitbit mediram factores como a frequ\u00eancia card\u00edaca, os micromovimentos e os movimentos maiores determinados pelo GPS (designados por macromovimentos ou raio de gira\u00e7\u00e3o) e os n\u00edveis de energia sonora no ambiente imediato.<\/span><\/li>\n
  4. Resultados:<\/b> Ao modelar os dados com o classificador EXtreme Gradient Boosting Machine (XGBoost), os investigadores conseguiram detetar se as pessoas tinham consumido can\u00e1bis nos \u00faltimos cinco minutos com uma precis\u00e3o de 85%.<\/span><\/li>\n<\/ol>\n

    Monitorizar as pessoas atrav\u00e9s dos seus dispositivos port\u00e1teis para determinar o potencial consumo de can\u00e1bis - essa \u00e9 uma maneira de<\/span> aumentam os seus n\u00edveis de paranoia.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Os investigadores utilizaram dados de smartphones e de dispositivos port\u00e1teis Fitbit para determinar quando as pessoas estavam pedradas com uma precis\u00e3o de 85%. Tradicionalmente, o consumo de can\u00e1bis \u00e9 determinado atrav\u00e9s de testes de urina, saliva e fios de cabelo. No entanto, estes m\u00e9todos exigem an\u00e1lises laboratoriais e o consumo imediato de can\u00e1bis pode n\u00e3o ser imediatamente vis\u00edvel em alguns testes. Sang Won Bae e a sua equipa do Stevens Institute of Technology, em Nova J\u00e9rsia, pretendiam acelerar o processo de determina\u00e7\u00e3o dos n\u00edveis de intoxica\u00e7\u00e3o perigosos. No seu estudo, 33 consumidores frequentes de cannabis comunicaram o seu consumo di\u00e1rio durante 30 dias. Ao longo deste per\u00edodo, os participantes usaram rastreadores de atividade - nomeadamente Fibits - que registaram<\/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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