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. 2021 Jan 21;5(1):29-36.
doi: 10.1159/000512383. eCollection 2021 Jan-Apr.

Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology

Anzar Abbas  1 , Vijay Yadav  1 , Emma Smith  2 , Elizabeth Ramjas  2 , Sarah B Rutter  2 , Caridad Benavidez  2 , Vidya Koesmahargyo  1 , Li Zhang  1 , Lei Guan  1 , Paul Rosenfield  2 , Mercedes Perez-Rodriguez  2 , Isaac R Galatzer-Levy  1   3
Affiliations

Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology

Anzar Abbas et al. Digit Biomark. .

Abstract

Introduction: Motor 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.

Methods: Eighteen 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.

Results: The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; p = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (p = 0.04), primarily with negative symptoms of schizophrenia.

Conclusions: Remote, 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.

Keywords: Digital phenotyping; Head movement; Motor function; Schizophrenia.

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Conflict of interest statement

Dr. Mercedes Perez-Rodriguez has received research grant funding from Neurocrine Biosciences Inc., Millennium Pharmaceuticals, Takeda, Merck, and AiCure. She is an advisory board member for Neurocrine Biosciences Inc. and a consultant on an American Foundation for Suicide Prevention (AFSP) grant (LSRG-1-005-16, PI: Baca-Garcia). Anzar Abbas, Vijay Yadav, Vidya Koesmahargyo, Li Zhang, Lei Guan, and Isaac R. Galatzer-Levy are employed at AiCure and own shares or are eligible to own shares in AiCure.

Figures

Fig. 1
Fig. 1
The rate of head movement in participants with schizophrenia (SCZ) was significantly less than in healthy individuals (HC).
None
See this image and copyright information in PMC

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