DetectStress: A Novel Stress Detection System Based on Smartphone and Wireless Physical Activity Tracker

Padmaja, B.; Prasad, V. Ramachandra; Sunitha, K. V. N.; Reddy, N. Chandra Sekhar; Anil, C. H.

doi:10.1007/978-981-13-1580-0_7

Cited by 14 publications

(10 citation statements)

References 18 publications

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“…Studies show that interventions to encourage employees to move more during work have been successful (Guitar et al, 2018). Other studies have tracked mood and provided financial incentives to engage in remote monitoring (Losina et al, 2017; Mark et al, 2016; Padmaja et al, 2019). These studies, as well as others, have shown that smartwatch accuracy in tracking is sufficient for baseline screening (Benedetto et al, 2018; Feehan et al, 2018; Reingle Gonzalez et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…These studies, as well as others, have shown that smartwatch accuracy in tracking is sufficient for baseline screening (Benedetto et al, 2018; Feehan et al, 2018; Reingle Gonzalez et al, 2019). Heart rate is a suitable variable to capture on the smartwatch, and heart rate can be used as a proxy for times of increased stress (Nakayama et al, 2018; Padmaja et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Monitoring of a Work-From-Home Employee to Identify Stress: A Case Report

Gleason¹

2021

Workplace Health Saf

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How do you assess the mental wellness of your work-from-home employees? This case study reports on how an occupational health nurse used work-from-home employee’s own phone and Fitbit™ smartwatch to obtain heart rate data to screen for high periods of stress. Telemedicine and telemetry allowed the occupational health nurses to screen an employee when the nurse could not assess the employee face-to-face. When the occupational health nurses identified an at-risk employee, the occupational health nurses referred the employee to the Employee Assistance Program (EAP) for counseling. Leveraging heart rate data on a smartwatch is a free intervention that is scalable and has a demonstrated outcome measure with a positive return on investment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Remote Monitoring of a Work-From-Home Employee to Identify Stress: A Case Report

Gleason¹

2021

Workplace Health Saf

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“…An automatic stress detection system is implemented in real-time through physiological signals gathered from wearable devices and to extract necessary information from the physiological signals the modality-specific artifact removal and feature extraction are employed [7]. A cognitive stress-level detection system (Detect Stress) is designed for evaluating the stress levels of an individual obtained through data through a smartphone and wireless physical activity tracker (FITBIT) [8]. Electroencephalography (EEG) sensor is integrated into this study for detecting the stress of students and EEG signal is preprocessed with Hilbert-Huang Transform (HHT) to eliminate the artifacts [9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A technique is proposed in this study to identify stress with GSR sensor where it is interfaced with Arduino and Bluetooth and further processed using LABVIEW and MATLAB. GSR utilizes two electrodes that were implanted on its fingers for sensing the stress [8]. The system transmits the information to a computer coordinator through Bluetooth and provides different data to a coordinator at the same time.…”

Section: Introductionmentioning

confidence: 99%

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Singh¹,

Gehlot²,

Saxena³

et al. 2023

Computers, Materials &Amp; Continua

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Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic skin reaction (GSR) sensor is proposed and built for real-time stress monitoring. The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system, physical stress is created on 10 different participants with three distinct tasks namely reading, visualizing the timer clock, and watching videos. MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e., relaxed for <1.75 volts; Normal: between 1.75 and 1.44 volts and stressed: >1.44 volts. In addition, LabVIEW is used as a data acquisition system, and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.

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“…without users elucidation effort. Padmaja et al (2016a;Sunitha et al, 2018;Padmaja et al, 2016b;2016c;2018c) given approaches, for activity recognition in various environments like WSNs, IoT etc. and analyzes the challenges involved in application to practical scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset

Padmaja¹,

Vaddella²,

Sunitha³

2019

Journal of Computer Science

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Sensor-based human motion detection requires the subtle amount of knowledge about various human activities from fitted sensor observations and readings. The prevalent pattern recognition methodologies have made immense progress over recent years. Nonetheless, these kind of methods usually rely on the particular heuristic variable extraction, which could inhibit generalization realization. This paper presents a distributed and parallel decision forest approach for modeling the Human Activity Recognition Using Smartphones Data. We made an attempt to achieve an optimal generalization performance with possible reduction in overfitting. Later, we compared the performance of proposed procedure with some existing approaches. It is observed that our adopted procedure outperforms with comparatively better statistical performance measures. It also gained 4.7x speed up in computation.

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DetectStress: A Novel Stress Detection System Based on Smartphone and Wireless Physical Activity Tracker

Cited by 14 publications

References 18 publications

Remote Monitoring of a Work-From-Home Employee to Identify Stress: A Case Report

Remote Monitoring of a Work-From-Home Employee to Identify Stress: A Case Report

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset

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