It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).

Recent advances in wearable technology have led to the development of various methods for stress sensing in both controlled laboratory and real-life environments. However, existing methods often rely on specialized or expensive sensors that may not be easily accessible to the general population. In this study, we investigate the feasibility of using off-the-shelf smartwatches for stress detection in real-life scenarios. To achieve this, we propose SOSW, a comprehensive methodology for robust sensor data processing by considering both physiological and contextual data. SOSW employs a two-layer machine learning (ML) architecture. The first-layer ML model is trained and validated using carefully collected data under controlled laboratory conditions. The second-layer ML model is trained and validated using data collected in real-life settings. We conducted evaluations with 26 and 18 participants in controlled laboratory and real-life conditions, respectively. The results indicate that our methodology can successfully detect stressful events with an F-1 score of up to 0.84 in laboratory conditions and 0.71 in real-life scenarios using off-the-shelf smartwatches. The results are comparable to those achieved by the state-of-the-art methods that rely on dedicated wearables.

Depression is a significant mental health issue that profoundly impacts people’s lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring active user involvement. This study contributes to the detection of depression severity and depressive symptoms using mobile devices. Our proposed approach aims to distinguish between different patterns of depression and improve prediction accuracy. We conducted an experiment involving 381 participants over a period of at least three months, during which we collected comprehensive passive sensor data and Patient Health Questionnaire (PHQ-9) self-reports. To enhance the accuracy of predicting depression severity levels (classified as none/mild, moderate, or severe), we introduce a novel approach called symptom profiling. The symptom profile vector represents nine depressive symptoms and indicates both the probability of each symptom being present and its significance for an individual. We evaluated the effectiveness of the symptom-profiling method by comparing the $F_1$ score achieved using sensor data features as inputs to machine learning models with the $F_1$ score obtained using the symptom profile vectors as inputs. Our findings demonstrate that symptom profiling improves the $F_1$ score by up to 0.09, with an average improvement of 0.05, resulting in a depression severity prediction with an $F_1$ score as high as 0.86.

This paper proposes StressBal, a mobile system that offers personalized just-in-time stress intervention with mobile and wearable sensing. The system runs entirely on-device, detecting stress in real-time and providing respiratory exercise-based interventions. It also updates the stress detection algorithm continuously, based on user feedback. We envision an open mobile platform that helps researchers to design and evaluate automatic, seamless, and unobtrusive stress interventions during everyday activities.

Traditional in-person counseling encounters limitations in terms of accessibility, flexibility, and social stigma. Additionally, low mental health literacy and embarrassment among individuals hinder help-seeking behavior. Meanwhile, the introduction of more sophisticated sensors embedded in ubiquitous devices such as smartphones and smartwatches, and the release of a powerful large language model, i.e., chatGPT, create new opportunities to address the existing limitations of traditional counseling services. In that regard, we propose WMGPT, a system that offers round-the-clock mental health counseling services. By leveraging continuous analysis of user context and digital phenotype, WMGPT delivers personalized counseling support. Through 24/7 passive monitoring, it continuously assesses individuals’ mental state, initiates conversations on their behalf, and potentially triggers counseling services. These specialized counseling services are facilitated by a fine-tuned chatGPT model. WMGPT presents a promising solution to overcome the limitations of traditional counseling by providing accessible, personalized, and timely mental health support, paving the way for a convenient and effective service for improving well-being.

We present WatchPPG, an open-source toolkit that enables raw photoplethysmography (PPG) data collection and stress detection using off-the-shelf smartwatches.