The detection of depression through non-verbal cues has gained significant attention. Previous research predominantly centred on identifying depression within the confines of controlled laboratory environments, often with the supervision of psychologists or counsellors. Unfortunately, datasets generated in such controlled settings may struggle to account for individual behaviours in real-life situations. In response to this limitation, we present the Extended D-vlog dataset, encompassing a collection of 1, 261 YouTube vlogs. Additionally, the emergence of large language models (LLMs) like GPT3.5, and GPT4 has sparked interest in their potential they can act like mental health professionals. Yet, the readiness of these LLM models to be used in real-life settings is still a concern as they can give wrong responses that can harm the users. We introduce a virtual agent serving as an initial contact for mental health patients, offering Cognitive Behavioral Therapy (CBT)-based responses. It comprises two core functions: 1. Identifying depression in individuals, and 2. Delivering CBT-based therapeutic responses. Our Mistral model achieved impressive scores of 70.1% and 30.9% for distortion assessment and classification, along with a Bert score of 88.7%. Moreover, utilizing the TVLT model on our Multimodal Extended D-vlog Dataset yielded outstanding results, with an impressive F1-score of 67.8%

Depression may live in the brain, but scientists have developed a new smartphone app to detect the disorder by looking for clues on your face. MoodCapture uses AI to assess micro-changes to a person's face - such as their gaze, eye movement, and how the person tilted their head - to determine whether they were depressed. The app, which was funded by the National Institute of Health, takes pictures with the front-facing camera and sends an alert if it identified a trend in facial expressions by looking at the position of the participants' lips, eyes and the depression lines in their face. According to the study, MoodCapture was correct in identifying people with depression 75 percent of the time. MoodCapture identified if participant's had depressive symptoms based on their facial features, lighting, and background objects About eight percent of U.S. adults are diagnosed with depression each year, amounting to roughly 21 million Americans More research still needs to be conducted, but researchers said MoodCapture could be made available to the public as early as within five years.

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.

TextBlob is more of a natural language processing library, but it comes with a rule-based sentiment analysis library. Polarity is a float that lies in the range of [-1,1] where 1 means a positive statement and -1 means a negative statement. Subjective sentences generally refer to personal opinion, emotion, or judgment whereas objective refers to factual information. VADER (Valence Aware Dictionary and Sentiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. The compound score is the sum of positive, negative & neutral scores which is then normalized between -1(most extreme negative) and 1 (most extreme positive).

Scientists have revealed they're planning to create a smartphone app that detects if someone's depressed based on changes in their voice. Speech coordination changes when a person becomes depressed, according to the researchers, at the University of Maryland. Depressed people cannot think as fast, and their speaking rate is slowed with more and longer pauses than if they are not depressed, they say. Therefore, a voice detection app using deep learning – a type of machine learning based on artificial neural networks – could help detect such traits, which can often be subtle. The app could be recommended by mental health therapists to their patients, who would submit video and audio updates on their mood at home, which the technology would then assess.

Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study. "The majority of kids under eight are undiagnosed."

Depression is a mental health condition of multiple faces. Those who are depressed may exhibit their symptoms in different ways. They could become apathetic, sad, or agitated. They could also experience distorted sleeping and eating patterns. These are only a few of the ways that this condition can be exhibited.

A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics. Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study.

MIT (Massachusetts Institute of Technology) has been working on the ability to identify depression or anxiety within somebody's speech patterns using deep learning. They are referring to it as "talk diagnosis", and are hoping that it could help to diagnose mental health issues, allowing these situations to be treated quickly. By doing this, it may be less necessary for patients to have to visit a doctor and undergo the series of questions doctors use to draw conclusions on various illnesses. Instead, your own computer might be able to draw this conclusion based on your everyday conversations instead. The study is made possible thanks to a computer farm packed full of NVIDIA (Titan X) GPUs, which work together to analyze given data and develop a model that can be used for detecting the various changes in a person's conversation.

The mental image of AI has always been that of a non-sentient being conversing meaningfully with us, starting as an assistant, and potentially taking over control from humans. In reality, AI is on our phone already, not just as Siri or Allo, but in map navigation, image correction, face recognition, deciding which ads we see and what products are recommended to us. We also live in a world where one in four people suffer from mental disorders, making it one of the leading causes of disability and ill-health. Technology has become an addiction, and it often blamed as the cause of rising mental health issues. Could Artificial intelligence help it become the cure instead?