Design of a System for Early Detection and Treatment of Depression in Elderly Case Study

Almeida, Edwing; Ferruzca, Marco; Tlapanco, María del Pilar Morales

doi:10.1007/978-3-319-11564-1_12

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…1 These small single-board computers are quickly moving from the do-ityourself, or DIY, community into mainstream technology development. Many are being used to acquire a wide range of measurements and are being incorporated into instruments for a multitude of applications including medical support and e-health [2][3][4][5][6][7] robotics, 8 surveillance monitoring, 9 and food production optical sorting. 10 The advent of open source software (and some hardware) has only quickened this trend.…”

Section: Introductionmentioning

confidence: 99%

Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes

Pagnutti

Ryan

Cazenavette

et al. 2017

J. Electron. Imaging

101

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, "Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes," J. Electron. Abstract. A comprehensive radiometric characterization of raw-data format imagery acquired with the Raspberry Pi 3 and V2.1 camera module is presented. The Raspberry Pi is a high-performance single-board computer designed to educate and solve real-world problems. This small computer supports a camera module that uses a Sony IMX219 8 megapixel CMOS sensor. This paper shows that scientific and engineering-grade imagery can be produced with the Raspberry Pi 3 and its V2.1 camera module. Raw imagery is shown to be linear with exposure and gain (ISO), which is essential for scientific and engineering applications. Dark frame, noise, and exposure stability assessments along with flat fielding results, spectral response measurements, and absolute radiometric calibration results are described. This low-cost imaging sensor, when calibrated to produce scientific quality data, can be used in computer vision, biophotonics, remote sensing, astronomy, high dynamic range imaging, and security applications, to name a few.

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Section: Introductionmentioning

confidence: 99%

Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes

Pagnutti

Ryan

Cazenavette

et al. 2017

J. Electron. Imaging

101

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“…Many such systems have been developed to obtain health data of older adults using similar methodologies. For example, a protocol was developed in 2014 to detect geriatric depression symptoms 9 . However, it did not collect actual data from human subjects.…”

Section: Discussionmentioning

confidence: 99%

“…For example, a protocol was developed in 2014 to detect geriatric depression symptoms. 9 However, it did not collect actual data from human subjects. In contrast, we tested the monitoring system on a small-scaled sample and integrated multimodal data into the system.…”

Section: Feature Articlementioning

confidence: 99%

“…The IoMT is frequently applied to depression to understand symptoms and behavioral changes using certain devices. For example, sensor devices collect continuous real-time data of daily activity, light exposure, and heart rate variability, which provide proximal indicators of disturbed sleep, physical inactivity, and insufficient light exposure, which are all strongly related to depression 3,7–10 . In addition, a recent study used a multimodal evaluation of facial muscle movement, head movement dynamics, and vocal data and concluded that these various data are more helpful to detect depressive severity than using a single method 2 .…”

mentioning

confidence: 99%

“…For example, sensor devices collect continuous realtime data of daily activity, light exposure, and heart rate variability, which provide proximal indicators of disturbed sleep, physical inactivity, and insufficient light exposure, which are all strongly related to depression. 3,[7][8][9][10] In addition, a recent study used a multimodal evaluation of facial muscle movement, head movement dynamics, and vocal data and concluded that these various data are more helpful to detect depressive severity than using a single method. 2 Currently, several studies have tried to apply IoMT and use sensorbased data to understand depression in elderly individuals (hereafter referred to as geriatric depression).…”

mentioning

confidence: 99%

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Developing a Multimodal Monitoring System for Geriatric Depression

Cho

Lim

Lee

et al. 2022

CIN: Computers, Informatics, Nursing

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The Internet of Medical Things is promising for monitoring depression symptoms. Therefore, it is necessary to develop multimodal monitoring systems tailored for elderly individuals with high feasibility and usability for further research and practice. This study comprised two phases: (1) methodological development of the system; and (2) system validation to evaluate its feasibility. We developed a system that includes a smartphone for facial and verbal expressions, a smartwatch for activity and heart rate monitoring, and an ecological momentary assessment application. A sample of 21 older Koreans aged 65 years and more was recruited from a community center. The 4-week data were collected for each participant (n = 19) using self-report questionnaires, wearable devices, and interviews and were analyzed using mixed methods. The depressive group (n = 6) indicated lower user acceptance relative to the nondepressive group (n = 13). Both groups experienced positive emotions, had regular life patterns, increased their self-interest, and stated that a system could disturb their daily activities. However, they were interested in learning new technologies and actively monitored their mental health status. Our multimodal monitoring system shows potential as a feasible and useful measure for acquiring mental health information about geriatric depression.

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“Sensing the Mind”: An Exploratory Study About Sensors Used in E-Health and M-Health Applications for Diagnosis of Mental Health Condition

Ghosh

Dey

2021

Internet of Things

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Design of a System for Early Detection and Treatment of Depression in Elderly Case Study

Cited by 8 publications

References 13 publications

Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes

Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes

Developing a Multimodal Monitoring System for Geriatric Depression

“Sensing the Mind”: An Exploratory Study About Sensors Used in E-Health and M-Health Applications for Diagnosis of Mental Health Condition

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