ArticleDigital biomarkers
Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology.
Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2023Pooled it
- A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review.PloS one · 2022Pooled it
- Analyzing Generative AI and Machine Learning in Auto-Assessing Schizophrenia's Negative Symptoms.Schizophrenia bulletin · 2026Article
- Turn-taking fluency in free conversations with individuals diagnosed with schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Smartphone-Based Markerless Motion Capture for Accessible Rehabilitation: A Computer Vision Study.Sensors (Basel, Switzerland) · 2025Article
- Exploring the clinical utility of rhythmic digital markers for schizophrenia.PLOS digital health · 2025Article
- Capturing Motor Signs in Psychosis: How the New Technologies Can Improve Assessment and Treatment?Schizophrenia bulletin · 2025Article
- Assessment of Negative Symptoms in Schizophrenia: From the Consensus Conference-Derived Scales to Remote Digital Phenotyping.Brain sciences · 2025Review
- Evaluating and mitigating unfairness in multimodal remote mental health assessments.PLOS digital health · 2024Article
- Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.IEEE journal of biomedical and health informatics · 2024Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Safety monitoring method of moving target in underground coal mine based on computer vision processing.Scientific reports · 2022Article
- Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task.Frontiers in neuroscience · 2022Article
- Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study.Journal of medical Internet research · 2021Article
- Remote Digital Measurement of Facial and Vocal Markers of Major Depressive Disorder Severity and Treatment Response: A Pilot Study.Frontiers in digital health · 2021Article
- Prognostic Biomarkers on a Competitive Endogenous RNA Network Reveals Overall Survival in Triple-Negative Breast Cancer.Frontiers in oncology · 2021Article
- Video Assessment to Detect Amyotrophic Lateral Sclerosis.Digital biomarkersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMotor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote "digital phenotyping" of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones.
methodsEighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed.
resultsThe rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame;
conclusionsRemote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.