Research Publications

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Has files: YesSubject: search.filters.subject.Mental Health

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Now showing 1 - 6 of 6
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    PublicationOpen Access
    Kalmora: A Voice-Based Journaling App for Real-Time Emotion Detection and Sustainable Mental Well-Being
    (SLIIT City UNI, 2025-07-08) Kavundhya, K; Dampallessa, D.R.C.G.K.
    The current tools for journaling depend on personal self-reporting which fails to match accurately with how people genuinely feel, and emotional states affect sustainable societal development. This research introduces Kalmora, which stands as a mobile voice-journaling application which utilizes Wav2Vec2 speech emotion recognition model that identifies seven basic emotions (happiness, sadness, anger, fear, disgust, neutral, surprise) in real time. Kalmora's secure dual frontend backend framework consisting of Flutter and Flask and Firebase elements performs time-based emotion assessment and individual wellness guidance. The model evaluated using controlled TESS data reached 99.8% accuracy which surpassed CNN-LSTM benchmark models at 94.1% accuracy. User testing involved observing real users interacting with the app to evaluate the ease of voice journaling, accuracy of emotion detection, and overall user experience, leading to improvements based on their feedback. Through its combination of objective emotional knowledge and practical tips Kalmora brings new possibilities to digital mental healthcare that enable sustainable emotional self-care practices.
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    PublicationOpen Access
    A STUDY ON THE PHYSICAL AND MENTAL HEALTH ISSUES TO THE NEIGHBOURING RESIDENCES DUE TO THE CONSTRUCTION PROJECTS IN SRI LANKA
    (Ceylon Institute of Builders - Sri Lanka, 2023-07-21) Arjuna, M.P.; Edirisinghe, V.; Manoharan, K.; Herath, S.S.
    This study investigates the physical and mental health issues experienced by neighbouring residences as a result of construction projects in Sri Lanka. Specifically, it examines the impact of these projects on respiratory distress, hearing impairments, traffic congestion, lack of landscape, and flooding conditions. Additionally, the study explores the psychological effects on residents and emphasises the importance of health and safety measures in project management. Data collection involved conducting interviews with project managers, site safety officers, and a male nurse from three selected construction sites, followed by a questionnaire survey administered to 30 neighbouring residents. The study provides recommendations to mitigate adverse impacts, raise community awareness, and promote environmentally friendly practices in the construction industry. The findings enhance understanding of the health challenges faced by neighbouring residents and offer insights to policymakers and project managers to improve the well-being of affected communities.
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    PublicationEmbargo
    Perceptions of People with Mental Disorders about Their Mental Health Condition; An Exploratory Study
    (Faculty of Humanities and Sciences, SLIIT, 2022-09-15) Wimaladhamma, K
    This exploratory study was conducted to explore how people who have been diagnosed with mental disorders perceive their mental health condition, and their descriptions of lived experience of it. It also explored how their mental health condition might influence how they perceive themselves, their relationships, and how they engage the world around them through their activities. Sixteen participants (16) with a diagnosis of a mental disorder were the sample. The participants of the study were taking medication for the diagnosed mental disorder from a period of a minimum of six (06) months up to fortyseven (47) years. The data were collected at the Psychiatric Clinic, the General Hospital (Teaching), Kandy. The data were interpreted using Interpretative Phenomenological Analysis (IPA). It was identified that all the participants defined and understood mental health and mental illness through their lived experiences of mental illness. It was also identified that most of the participants experience some element of unhappiness or dissatisfaction because of their mental illness. However, the participants' positive self-esteem has played an important role in adjusting to the identity with the mental illness. The participants also valued the physical and psychological support they receive from their family members and others when they deal with their mental illness. Emotional support was highly valued by almost all the participants. It is expected that the generated knowledge of this research would help mental health professionals to work with families to construct an enabling view of the person with the illness and move away from treating them as helpless and irresponsible.
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    PublicationEmbargo
    Mobile Application for Mental Health Using Machine Learning
    (IEEE, 2022-12-09) Mendis, E.S; Kasthuriarachchi, L.W; Samarasinha, H.P.K.L; Kasthuriarachchi, S; Rajapaksa, S
    In present era, mental health has become one of the most neglected, yet critically important, factors of our overall well-being. A large number of people are affected by various types of mental illnesses and mental health disorders. Stress, anxiety, and depression are the most common disorders among children and adolescents in Sri Lanka, and their prevalence has increased over the years, likely to require immediate medical attention. In today’s world, mobile phones and applications play an important role in everyone’s life. With the rapid growth of mental illness, mental health-focused apps and websites have gradually increased globally in recent years. This study aims to develop a mobile application that will primarily serve Sri Lankans with mental health problems, helping them identify their levels of stress, anxiety, and depression (ADS) and receiving advice on how to deal with them. This app’s main objective is to support those who are dealing with mental illnesses and raise awareness of them locally using machine learning and image processing techniques. It does this by serving as a constant reminder of how crucial mental health is and how much of an impact it has on daily life. The GSE Scale, DASS 21 scale has been used to find the users’ mental health illness and the severity of each mental health illness such and Anxiety depression and stress. These methods are put to our mobile application using machine learning techniques such as Decision tree and Random Forest classifiers and uses image processing technologies, CNN machine learning algorithm to offer a variety of activities for reliving stress, depression, and anxiety,
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    PublicationEmbargo
    Better you: Automated tool that evaluates mental health and provides guidance for university students
    (Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Eeswar, S. S; Samaratunga, J. S; Nivethika, G; Anjana, W.W.M.; Jayasingha, T.B.; Pandithakoralage, S; Kasthurirathna, D
    This research paper proposes a system that evaluates mental health through text-based, voice-based and facial emotion recognition. After predicting the user's overall emotional state, activity suggestions and close contact interactions will be suggested to improve their mental health.
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    PublicationEmbargo
    UNWIND – A Mobile Application that Provides Emotional Support for Working Women
    (IEEE, 2022-10-11) Kugapriya, P; Manohara, M; Ranganathan, K; Kanapathy, D; Gamage, A
    Depression is a common phenomenon affecting more than 264 million people worldwide. It is one of the leading causes of disability and a major contributor to the overall global burden of disease. Around twice as many women are affected by mental illness compared to men. This situation has worsened during the pandemic. The need to balance both work life and personal life has put them under immense pressure. Even though the diagnosis of mental illness almost exclusively depends on doctor-patient communication, it has its own set of disadvantages such as patient denial, recall bias, subjective biases, time-consuming and inaccuracy and it is a long-term health problem that needs to be continuously monitored and managed. Considering this social problem, we have planned to develop an Emotional Support Mobile application UNWIND – using modern technological concepts of machine learning and artificial intelligence. Which focuses especially on working women and would include several functionalities: a Chabot to detect mental health status in real-time and to provide counseling, an internal activities tracker to find the correlation between changes in lifestyle and mental health, an improvement tracker of the user’s current mental state using facial recognition and also Recommendation system with the support group, which recommends the most suitable professional counselors to the user as per their preferences and enabling into the support group to provide with necessary treatments and consultation at greater accuracy.