ArticleDiagnostics (Basel, Switzerland)2019
Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients.
Article in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- The Performance of Wearable Device-Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2026Pooled it
- Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices.International journal of environmental research and public health · 2023Pooled it
- Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Integrated Analysis of Circadian and Sleep Signatures in Depression and Schizophrenia Using Multi-Day Actigraphy.Bioengineering (Basel, Switzerland) · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Explainable AI for Depression Detection and Severity Classification From Activity Data: Development and Evaluation Study of an Interpretable Framework.JMIR mental health · 2025Article
- A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.Scientific reports · 2025Article
- Convolutional Neural Network for Depression and Schizophrenia Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters.Diagnostics (Basel, Switzerland) · 2025Article
- Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.NPJ digital medicine · 2023Article
- Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.Journal of medical Internet research · 2023Article
- Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset.Bioengineering (Basel, Switzerland) · 2022Article
- Machine Learning on Early Diagnosis of Depression.Psychiatry investigation · 2022Article
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data.Journal of medical systems · 2020Article
- Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients; nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the "Depresjon" database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier. Results show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.
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Registered trials
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