{"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\/de\/2023\/10\/researchers-have-developed-an-ai-model-for-detecting-cannabis-use\/","title":{"rendered":"Forscher haben ein KI-Modell zur Erkennung von Cannabiskonsum entwickelt"},"content":{"rendered":"

Die Forscher verwendeten Daten von Smartphones und Fitbit-Wearables, um festzustellen, wann Menschen mit 85%-Genauigkeit high waren. <\/strong><\/p>\n

Der Cannabiskonsum wird traditionell durch Urin-, Speichel- und Haarstrangtests festgestellt. Diese Methoden erfordern jedoch eine Laboranalyse, und der unmittelbare Cannabiskonsum ist bei einigen Tests m\u00f6glicherweise nicht sofort erkennbar.\u00a0<\/span><\/p>\n

Sang Won Bae und ihr Team vom Stevens Institute of Technology, New Jersey, wollten den Prozess der Bestimmung gef\u00e4hrlicher Vergiftungsgrade beschleunigen.<\/span><\/p>\n

In ihrem <\/span>Studie<\/span><\/a>gaben 33 h\u00e4ufige Cannabiskonsumenten ihren t\u00e4glichen Konsum \u00fcber einen Zeitraum von 30 Tagen an.<\/span><\/p>\n

W\u00e4hrend dieser Zeit trugen die Teilnehmer Aktivit\u00e4ts-Tracker - namentlich Fibits -, die Messwerte wie Herzfrequenz, Schrittzahl und Schlafverhalten aufzeichneten. <\/span><\/p>\n

Gleichzeitig \u00fcberwachten Telefonsensoren ihre Mikrobewegungen, z. B. das Verhalten beim Telefonieren, um Koordination und Stabilit\u00e4t zu bewerten.<\/span><\/p>\n

Nachdem das Team erste Teilnehmerdaten gesammelt hatte, trainierte es das maschinelle Lernmodell, um potenzielle Anzeichen f\u00fcr Cannabiskonsum zu erkennen. <\/span><\/p>\n

Beim Test des verbleibenden Datensatzes erreichte das trainierte Modell eine Trefferquote von 85% bei der Identifizierung von Personen, die in den vorangegangenen 5 Minuten Cannabis konsumiert hatten.<\/span><\/p>\n

Wearables wie Fitbits sammeln riesige Datenmengen von ihren Nutzern, aber sie zur Vorhersage, ob jemand high ist, heranzuziehen, w\u00e4re \u00e4u\u00dferst umstritten.\u00a0<\/span><\/p>\n

Hinsichtlich der Genauigkeit der KI erw\u00e4hnt Chung, dass sie auf den Selbstausk\u00fcnften der Teilnehmer beruht, was ihre Anwendung in der realen Welt erschwert.\u00a0<\/span><\/p>\n

Wie die Studie funktionierte<\/span><\/h2>\n
    \n
  1. Zielsetzung:<\/b> Die Forscher wollten herausfinden, ob durch die Kombination von Smartphone-Sensoren und einem Fitbit Episoden einer akuten Marihuana-Intoxikation in Echtzeit erkannt werden k\u00f6nnen.<\/span><\/li>\n
  2. Methodik:<\/b> \u00dcber 30 Tage hinweg berichteten 33 junge Erwachsene \u00fcber ihren Marihuanakonsum und die damit verbundenen Rauschgef\u00fchle. Sie hielten ihre subjektiven Gef\u00fchle innerhalb von 15 Minuten nach dem Konsum von Marihuana und bei drei halb zuf\u00e4lligen t\u00e4glichen Aufforderungen fest.<\/span><\/li>\n
  3. Gemessene Variablen:<\/b> Die Teilnehmer bewerteten ihren Rauschzustand auf einer Skala: \"nicht berauscht\" (Punktzahl = 0), \"geringe Berauschung\" (Punktzahl = 1-3) und \"m\u00e4\u00dfige bis starke Berauschung\" (Punktzahl = 4-10). Mit Smartphone-Sensoren und dem Fitbit wurden Faktoren wie Herzfrequenz, Mikrobewegungen und gr\u00f6\u00dfere, per GPS ermittelte Bewegungen (Makrobewegung oder Kreiselradius genannt) sowie der Ger\u00e4uschpegel in der unmittelbaren Umgebung gemessen.<\/span><\/li>\n
  4. Ergebnisse:<\/b> Durch die Modellierung der Daten mit dem EXtreme Gradient Boosting Machine Klassifikator (XGBoost) konnten die Forscher mit einer Genauigkeit von 85% erkennen, ob die Personen in den letzten f\u00fcnf Minuten Cannabis konsumiert hatten.<\/span><\/li>\n<\/ol>\n

    \u00dcberwachung von Personen \u00fcber ihre Wearables zur Ermittlung des potenziellen Cannabiskonsums - Das ist ein Weg, um<\/span> ihren Verfolgungswahn zu verst\u00e4rken.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Die Forscher nutzten Daten von Smartphones und Fitbit-Wearables, um mit einer Genauigkeit von 85% festzustellen, wann Menschen high waren. Der Cannabiskonsum wird traditionell durch Urin-, Speichel- und Haarstrangtests ermittelt. Diese Methoden erfordern jedoch eine Laboranalyse, und der unmittelbare Cannabiskonsum ist bei einigen Tests m\u00f6glicherweise nicht sofort erkennbar. Sang Won Bae und ihr Team vom Stevens Institute of Technology, New Jersey, wollten den Prozess der Bestimmung gef\u00e4hrlicher Rauschzust\u00e4nde beschleunigen. In ihrer Studie berichteten 33 h\u00e4ufige Cannabiskonsumenten \u00fcber ihren t\u00e4glichen Konsum \u00fcber einen Zeitraum von 30 Tagen. W\u00e4hrend dieser Zeit trugen die Teilnehmer Aktivit\u00e4ts-Tracker - n\u00e4mlich Fibits - die Folgendes aufzeichneten<\/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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