{"id":5442,"date":"2025-05-14T14:00:03","date_gmt":"2025-05-14T14:00:03","guid":{"rendered":"https:\/\/ukdw.ac.id\/en\/?p=5442"},"modified":"2025-05-14T14:00:03","modified_gmt":"2025-05-14T14:00:03","slug":"ukdw-researchers-develop-moodtracker-app-with-facial-mood-detection-and-mental-health-screening-features","status":"publish","type":"post","link":"https:\/\/ukdw.ac.id\/en\/2025\/05\/14\/ukdw-researchers-develop-moodtracker-app-with-facial-mood-detection-and-mental-health-screening-features\/","title":{"rendered":"UKDW Researchers Develop MoodTracker App with Facial Mood Detection and Mental Health Screening Features"},"content":{"rendered":"

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Amid growing global concern over mental health, researchers at Univeraitas Kristen Duta Wacana (UKDW) in Yogyakarta, Indonesia, have developed an innovative digital tool aimed at supporting emotional well-being. The university\u2019s Faculty of Information Technology has been working on MoodTracker<\/em>, a mobile application designed to help users monitor mood fluctuations and assess their mental health status.<\/p>\n

The project, initiated by Agata Filiana, S.Kom., M.Sc., and supported by faculty members and students from the Faculty of Information Technology, focused on adapting the app for Android devices and enhancing its functionality. In the next phase of development, faculty members Maria Nila Anggia Rini, ST., MTI, Antonius Rachmat C., S.Kom., M.Cs., and Andhika Galuh Prabawati, S.Kom., M.Kom., collaborated with students to build out the app\u2019s capabilities. Recent developments introduced advanced features that include facial mood recognition and a mental health self-assessment tool. These features are designed to give users a more comprehensive picture of their psychological condition.<\/p>\n

The facial mood detection feature analyzes users’ expressions through a machine learning model trained to identify emotional states. At the same time, the mental health screening module presents users with a series of questions aimed at identifying potential symptoms of psychological distress. The app then provides tailored recommendations, including referrals to mental health professionals based on the assessment results.<\/p>\n

Maria Nila noted that this initiative aligns with UKDW\u2019s Research Master Plan, specifically its focus on information systems and public health technologies.<\/p>\n

\u201cWe held a focus group discussion with 10 clinical psychologists from the Yogyakarta branch of the Indonesian Clinical Psychologists Association (IPK),\u201d she said. \u201cTheir insights helped shape the app\u2019s mental health screening tool, which uses the SRQ-20 for early detection of mental health conditions and provides follow-up advice based on test results,\u201d she explained.<\/p>\n

In addition to these features, the app includes a location-based service that helps users find nearby psychologists or mental health clinics, allowing them to access follow-up support if needed. This ensures that the application not only helps identify mental health concerns but also connects users with professional help.<\/p>\n

The machine learning model used for facial mood recognition combines convolutional neural networks (CNNs), data augmentation techniques, and ethical design considerations to ensure accuracy and reliability. The system was trained on a dataset of 327 facial images of individuals aged 18 to 50, representing a diverse range of racial and gender backgrounds. The current functionality allows users to add mood entries manually or use the app\u2019s mood scan feature. During a scan, the application captures an image after a three-second countdown and classifies the mood into one of four categories: angry, happy, neutral, or sad. This facial classification tool was developed as part of an undergraduate thesis by Edwin Mahendra, a student in the Faculty of Information Technology.<\/p>\n

Looking ahead, the development team plans to improve the app\u2019s facial detection accuracy and test its performance through user evaluations. Future enhancements under consideration include better pose recognition and face detection technology, integration of electroencephalography (EEG) for mood sensing, development of more adaptive AI-based detection algorithms, and the addition of virtual psychological counseling or personal coaching features.<\/p>\n

By blending artificial intelligence, mental health expertise, and user-centered design, UKDW\u2019s MoodTracker<\/em> represents a promising step forward in the development of accessible and intelligent tools to support mental wellness in a digital age. [humasukdw\/trans.drr<\/em>]<\/p>\n

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                Amid growing global concern over mental health, researchers at Univeraitas Kristen Duta Wacana (UKDW) in Yogyakarta, Indonesia, have developed an innovative digital tool aimed at supporting emotional well-being. The university\u2019s Faculty of … <\/p>\n","protected":false},"author":44,"featured_media":5443,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5],"tags":[],"class_list":["post-5442","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-berita"],"acf":[],"_links":{"self":[{"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/posts\/5442","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/comments?post=5442"}],"version-history":[{"count":0,"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/posts\/5442\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/media\/5443"}],"wp:attachment":[{"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/media?parent=5442"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/categories?post=5442"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ukdw.ac.id\/en\/wp-json\/wp\/v2\/tags?post=5442"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}