An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices

• URL: http://arxiv.org/abs/2505.03039v1
• Date: Mon, 05 May 2025 21:41:05 GMT
• Title: An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices
• Authors: Yuezhou Zhang, Amos A. Folarin, Callum Stewart, Heet Sankesara, Yatharth Ranjan, Pauline Conde, Akash Roy Choudhury, Shaoxiong Sun, Zulqarnain Rashid, Richard J. B. Dobson,
• Abstract summary: Continuous monitoring of behavior and physiology via wearable devices offers a novel, objective method for the early detection of worsening depression and anxiety.<n>We present an explainable anomaly detection framework that identifies clinically meaningful increases in symptom severity using consumer-grade wearable data.
• Score: 2.217204868812473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continuous monitoring of behavior and physiology via wearable devices offers a novel, objective method for the early detection of worsening depression and anxiety. In this study, we present an explainable anomaly detection framework that identifies clinically meaningful increases in symptom severity using consumer-grade wearable data. Leveraging data from 2,023 participants with defined healthy baselines, our LSTM autoencoder model learned normal health patterns of sleep duration, step count, and resting heart rate. Anomalies were flagged when self-reported depression or anxiety scores increased by >=5 points (a threshold considered clinically significant). The model achieved an adjusted F1-score of 0.80 (precision = 0.73, recall = 0.88) in detecting 393 symptom-worsening episodes across 341 participants, with higher performance observed for episodes involving concurrent depression and anxiety escalation (F1 = 0.84) and for more pronounced symptom changes (>=10-point increases, F1 = 0.85). Model interpretability was supported by SHAP-based analysis, which identified resting heart rate as the most influential feature in 71.4 percentage of detected anomalies, followed by physical activity and sleep. Together, our findings highlight the potential of explainable anomaly detection to enable personalized, scalable, and proactive mental health monitoring in real-world settings.

Related papers

• Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements [17.985259248390943]
  Depression and suicidal ideation, in particular, lack objective biomarkers, with screening and diagnosis primarily relying on self-reports and clinical interviews.<n>We investigated eye tracking as a potential marker modality for screening purposes.<n>We recorded eye-tracking sequences from 126 young adults as they read and responded to affective sentences, and subsequently developed a deep learning framework to predict their clinical status.
  arXiv  Detail & Related papers  (2025-04-29T17:11:13Z)
• LLM Assistance for Pediatric Depression [2.1398676192061683]
  This work investigates the feasibility of state-of-the-art LLMs for depressive symptom extraction in pediatric settings (ages 6-24).<n>Our findings show that all LLMs are 60% more efficient than word match, with Flan leading in precision (average F1: 0.65, precision: 0.78), excelling in the extraction of more rare symptoms like "sleep problems" (F1: 0.92) and "self-loathing" (F1: 0.8)<n>Llama 3, with the highest recall (0.90), overgeneralizes symptoms, making it less suitable for this type of analysis.
  arXiv  Detail & Related papers  (2025-01-29T09:27:27Z)
• Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
  We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
  arXiv  Detail & Related papers  (2024-09-24T16:05:17Z)
• Investigating the Generalizability of Physiological Characteristics of Anxiety [3.4036712573981607]
  We evaluate the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.
  arXiv  Detail & Related papers  (2024-01-23T16:49:54Z)
• Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content [3.6195994708545016]
  We propose a novel semantic feature preprocessing technique with a three-folded structure. With enhanced semantic features, we train a machine learning model to predict and classify mental disorders. Our methods, when compared to seven benchmark models, demonstrate significant performance improvements.
  arXiv  Detail & Related papers  (2023-11-09T00:15:06Z)
• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection [69.88072583383085]
  This work shows that depression changes the correlation between features extracted from speech. Using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs.
  arXiv  Detail & Related papers  (2023-07-06T09:54:35Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan [49.209225484926634]
  We propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification. Our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
  arXiv  Detail & Related papers  (2020-05-07T12:16:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.