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Mental Health Monitoring and Psychiatric Practice Using Sensors and Wearables
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Mental Health Monitoring and Psychiatric Practice Using Sensors and Wearables

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 7138

Special Issue Editor

Dr. Toshikazu Shinba

E-Mail Website
Guest Editor
Department of Psychiatry, Shizuoka Saiseikai General Hospital, Shizuoka 422-8527, Japan
Interests: psychophysiology; arousal; consciousness; mental disorders; EEG; ERP; heart rate variability; skin conductance; brain stimulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In psychiatric diagnosis and mental health management, there is an increasing need for practical objective measures in addition to psychological and behavioral assessment. This Special Issue welcomes the research utilizing biosignals using Sensors and Wearables as practical objective measures to understand the mental disturbances and to develop their treatment. Both clinical and basic studies will be accepted.

The objective measures include all types of biosignals including heart rate, heart rate variability, skin conductance, EEG, event-related potential, brain blood flow, body movement, etc.

The research targets cover psychiatric disorders including depression, anxiety, stress-related disorders, schizophrenia, developmental disorders, dementia, sleep disturbances and delirium. Research on mental health in the normal population is also welcome.

The research may discuss the ways of interventions for treating the symptoms depending on the results obtained by Sensors and Wearables. The interventions can be physical (such as brain stimulation), pharmacological, behavioral and psychological. Studies employing neurofeedback and a brain-machine interface are of interest. Human basic studies and animal studies aiming to develop the new systems are also appropriate.

Dr. Toshikazu Shinba
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biosensors
  • practical and objective measures
  • psychiatric disorders
  • mental health
  • diagnostic aid
  • symptom evaluation
  • development of new treatment

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Published Papers (3 papers)

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Research

18 pages, 2527 KB  
Article
Advancing Mobile Neuroscience: A Novel Wearable Backpack for Multi-Sensor Research in Urban Environments
by João Amaro, Rafael Ramusga, Ana Bonifácio, André Almeida, João Frazão, Bruno F. Cruz, Andrew Erskine, Filipe Carvalho, Gonçalo Lopes, Ata Chokhachian, Daniele Santucci, Paulo Morgado and Bruno Miranda
Sensors 2025, 25(23), 7163; https://doi.org/10.3390/s25237163 - 24 Nov 2025
Cited by 1 | Viewed by 1984
Abstract
Rapid global urbanization has intensified the demand for sensing solutions that can capture the complex interactions between urban environments and their impact on human physical and mental health. Conventional laboratory-based approaches, while offering high experimental control, often lack ecological validity and fail to [...] Read more.
Rapid global urbanization has intensified the demand for sensing solutions that can capture the complex interactions between urban environments and their impact on human physical and mental health. Conventional laboratory-based approaches, while offering high experimental control, often lack ecological validity and fail to represent real-world exposures. To address this gap, we present the eMOTIONAL Cities Walker—a portable multimodal sensing platform designed as a wearable backpack unit developed for the synchronous collecting of multimodal data in either indoor or outdoor settings. The system integrates a suite of environmental sensors (covering microclimate, air pollution and acoustic monitoring) with physiological sensing technologies, including electroencephalography (EEG), mobile eye-tracking and wrist-based physiological monitoring. This configuration enables real-time acquisition of environmental and physiological signals in dynamic, naturalistic settings. Here, we describe the system’s technical architecture, sensor specifications, and field deployment across selected Lisbon locations, demonstrating its feasibility and robustness in urban environments. By bridging controlled laboratory paradigms with ecologically valid real-world sensing, this platform provides a novel tool to advance translational research at the intersection of sensor technology, human experience, and urban health. Full article
(This article belongs to the Special Issue Mental Health Monitoring and Psychiatric Practice Using Sensors and Wearables)
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18 pages, 734 KB  
Article
Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications
by Charalampos Tsirmpas, Stasinos Konstantopoulos, Dimitris Andrikopoulos, Konstantina Kyriakouli and Panagiotis Fatouros
Sensors 2025, 25(14), 4406; https://doi.org/10.3390/s25144406 - 15 Jul 2025
Cited by 3 | Viewed by 2108
Abstract
Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus [...] Read more.
Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. Full article
(This article belongs to the Special Issue Mental Health Monitoring and Psychiatric Practice Using Sensors and Wearables)
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9 pages, 1115 KB  
Article
The Presence/Absence of an Awake-State Dominant EEG Rhythm in Delirious Patients Is Related to Different Symptoms of Delirium Evaluated by the Intensive Care Delirium Screening Checklist (ICDSC)
by Toshikazu Shinba, Yusuke Fujita, Yusuke Ogawa, Yujiro Shinba and Shuntaro Shinba
Sensors 2024, 24(24), 8097; https://doi.org/10.3390/s24248097 - 19 Dec 2024
Cited by 3 | Viewed by 2137
Abstract
(1) Background: Delirium is a serious condition in patients undergoing treatment for somatic diseases, leading to poor prognosis. However, the pathophysiology of delirium is not fully understood and should be clarified for its adequate treatment. This study analyzed the relationship between confusion symptoms [...] Read more.
(1) Background: Delirium is a serious condition in patients undergoing treatment for somatic diseases, leading to poor prognosis. However, the pathophysiology of delirium is not fully understood and should be clarified for its adequate treatment. This study analyzed the relationship between confusion symptoms in delirium and resting-state electroencephalogram (EEG) power spectrum (PS) profiles to investigate the heterogeneity. (2) Methods: The participants were 28 inpatients in a general hospital showing confusion symptoms with an Intensive Care Delirium Screening Checklist (ICDSC) score of 4 or above. EEG was measured at Pz in the daytime awake state for 100 s with the eyes open and 100 s with the eyes closed on the day of the ICDSC evaluation. PS analysis was conducted consecutively for each 10 s datum. (3) Results: Two resting EEG PS patterns were observed regarding the dominant rhythm: the presence or absence of a dominant rhythm, whereby the PS showed alpha or theta peaks in the former and no dominant rhythm in the latter. The patients showing a dominant EEG rhythm were frequently accompanied by hallucination or delusion (p = 0.039); conversely, those lacking a dominant rhythm tended to exhibit fluctuations in the delirium symptoms (p = 0.020). The other ICDSC scores did not differ between the participants with these two EEG patterns. (4) Discussion: The present study indicates that the presence and absence of a dominant EEG rhythm in delirious patients are related to different symptoms of delirium. Using EEG monitoring in the care of delirium will help characterize its heterogeneous pathophysiology, which requires multiple management strategies. Full article
(This article belongs to the Special Issue Mental Health Monitoring and Psychiatric Practice Using Sensors and Wearables)
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