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An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices [article]

Van-Tu Ninh and Manh-Duy Nguyen and Sinéad Smyth and Minh-Triet Tran and Graham Healy and Binh T. Nguyen and Cathal Gurrin
2022 arXiv   pre-print
they allow for the deployment of stress level detection and management systems in consumer-grade wearable devices without requiring training data for the end-user.  ...  Two common approaches to training a stress detection model are subject-dependent and subject-independent training methods.  ...  signals captured from consumer-grade wearable devices.  ... 
arXiv:2203.09663v1 fatcat:3hurqnotzffibnezbupkhz4uyu

Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices [article]

Van-Tu Ninh and Sinéad Smyth and Minh-Triet Tran and Cathal Gurrin
2022 arXiv   pre-print
Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns.  ...  user-dependent and user-independent models.  ...  They suggested applying a subject-wise feature selection to improve user-independent model instead of purely building personal models using personal training data.  ... 
arXiv:2203.09669v1 fatcat:xwcruxg5lfg7lhk6xjkysye2ma

Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review [article]

Gideon Vos, Kelly Trinh, Zoltan Sarnyai, Mostafa Rahimi Azghadi
2022 arXiv   pre-print
The aim of this review is to provide an overview of the current state of stress detection and monitoring using wearable devices, and where applicable, machine learning techniques utilized. Methods.  ...  This is further compounded by the lack of large, labeled datasets that can be utilized to build machine learning models for accurately detecting periods and levels of stress.  ...  Load Inference Using Wearable Sensors and Psychological Traits [7] Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection [12] An Advanced Stress Detection  ... 
arXiv:2209.15137v1 fatcat:msmxdbtrtzbrhmujjohjotnuzy

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Shweta Chakrabarti, Nupur Biswas, Lawrence D. Jones, Santosh Kesari, Shashaanka Ashili
2022 Diagnostics  
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services.  ...  In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic  ...  All authors have read and agreed to the published version of the manuscript. Table 1 . 1 List of consumer-grade wearable devices used as digital diagnostic tools.  ... 
doi:10.3390/diagnostics12092110 pmid:36140511 pmcid:PMC9498278 fatcat:j6lq7lbwcvfvzkugo5wtettn4a

Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices [article]

Gideon Vos, Kelly Trinh, Zoltan Sarnyai, Mostafa Rahimi Azghadi
2022 arXiv   pre-print
Ma-chine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection.  ...  Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it on two additional, unseen publicly available stress datasets (WESAD and  ...  Responses of Stress for Wearable Sensors in Connected Health [12] Advancing Stress Detection Methodology with Deep Learning Techniques [13] Analysing the Performance of Stress Detection Models on Consumer-Grade  ... 
arXiv:2209.15146v1 fatcat:ukiqt67hnrhb3mzvazs322xi3e

The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment

Sara Aristizabal, Kunjoon Byun, Nadia Wood, Aidan F. Mullan, Paige M. Porter, Carolina Campanella, Anja Jamrozik, Ivan Z. Nenadic, Brent A. Bauer
2021 IEEE Access  
The model was able to detect stress periods with 96% accuracy when using the combined wearable device and survey data, compared to the wearable device dataset alone (88% accuracy).  ...  INDEX TERMS Deep learning, perceived anxiety, perceived stress, stress detection model, TSST, wrist-worn wearable.  ...  We acknowledge Jo Bernau, Carole Wolfe, Kevin Hovde, Christian Ramos, Syed Shabih Hassan, and Jeyakumar Raman for their contributions to the study.  ... 
doi:10.1109/access.2021.3097038 fatcat:skzbrmdfnrel3mjp7vallsebjq

A Review on Mental Stress Detection using Wearable Sensors and Machine Learning Techniques

Shruti Gedam, Sanchita Paul
2021 IEEE Access  
In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques.  ...  Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring.  ...  Hence some researchers have used wearable devices to monitor and detect stress to improve the health and work safety of such personnel. U.  ... 
doi:10.1109/access.2021.3085502 fatcat:m5spbtol5ve5rkf4jjdincn6nq

Wearables Technology: Awareness, Adoption and Applications in Indian Health Insurance Industry

Aswin C Prakash, Manoj Kumar Pandey, Manoj Pareek
2021 Zenodo  
We examine why insurers are considering using wearable devices, how they might improve their business models, and how to avoid some common pitfalls.  ...  The wearable devices are touted to provide exact and detailed real-time data. This research investigates public perceptions of wearable technology and its possible applications.  ...  Several scientific and popular publications suggest how to use consumer wearables as "self-hacking" devices to improve sleep, control stress, and boost productivity.  ... 
doi:10.5281/zenodo.5797230 fatcat:r4jia2nshbacjatvw4u4zhrggy

Smart wearable body sensors for patient self-assessment and monitoring

Geoff Appelboom, Elvis Camacho, Mickey E Abraham, Samuel S Bruce, Emmanuel LP Dumont, Brad E Zacharia, Randy D'Amico, Justin Slomian, Jean Yves Reginster, Olivier Bruyère, E Sander Connolly
2014 Archives of Public Health  
The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors.  ...  Conclusion: Although these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry.  ...  The majority of SWS fall under the telemonitoring model but a few possess the ability to allow the user to input subjective data as well, which then follows the QSHM model.  ... 
doi:10.1186/2049-3258-72-28 pmid:25232478 pmcid:PMC4166023 fatcat:47lphtscjfawvmgyk2cpfmkspa

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Thomas Kundinger, Nikoletta Sofra, Andreas Riener
2020 Sensors  
Several machine learning algorithms for binary classification were applied in user-dependent and independent tests.  ...  This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn  ...  Acknowledgments: We applied the SDC approach for the sequence of authors. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20041029 pmid:32075030 pmcid:PMC7070962 fatcat:wsfo24ukybbvpfow5u3do27tpu

Machine Learning for Healthcare Wearable Devices: The Big Picture

Farida Sabry, Tamer Eltaras, Wadha Labda, Khawla Alzoubi, Qutaibah Malluhi, Yuxiang Wu
2022 Journal of Healthcare Engineering  
Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market.  ...  It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's  ...  Deployment Alternatives. ere are three deployment alternatives for the machine learning model for the wearable device scenario, either to deploy the model on the wearable device, or on an edge device or  ... 
doi:10.1155/2022/4653923 pmid:35480146 pmcid:PMC9038375 fatcat:ib3allbsanf5jhefovkfdqh2ri

Personal stress-level clustering and decision-level smoothing to enhance the performance of ambulatory stress detection with smartwatches

Yekta Said Can, Niaz Chalabianloo, Deniz Ekiz, Javier Fernandez-Alvarez, Giuseppe Riva, Cem Ersoy
2020 IEEE Access  
Since patterns of stress are ideographic, person-independent models have generally lower accuracies.  ...  In this study, we developed a hybrid approach of personal level stress clustering by using baseline stress self-reports to increase the success of person-independent models without requiring a substantial  ...  In this work, we improved our multi-level stress detection system which uses unobtrusive smart wearable devices.  ... 
doi:10.1109/access.2020.2975351 fatcat:anmzfneihndnxkmxavzvr2bqsm

Wireless Sensors for Brain Activity—A Survey

Mahyar TajDini, Volodymyr Sokolov, Ievgeniia Kuzminykh, Stavros Shiaeles, Bogdan Ghita
2020 Electronics  
In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their  ...  miniature hardware, more specifically wireless wearable EEG devices.  ...  Admittedly, the recently applied low fee, wireless, lightweight, and easy-to-use wearability has established an impact on the increasing attraction towards the noninvasive consumer-grade EEG gadgets among  ... 
doi:10.3390/electronics9122092 fatcat:jewq23j5kredpehwggkixde22i

Wearable Technologies for Developing Sleep and Circadian Biomarkers: A Summary of Workshop Discussions

Christopher M Depner, Philip C Cheng, Jaime K Devine, Seema Khosla, Massimiliano de Zambotti, Rébecca Robillard, Andrew Vakulin, Sean P A Drummond
2019 Sleep  
use of wearables in sleep and circadian science; 3) identify current barriers for applying wearables to sleep and circadian science; and 4) identify goals and opportunities for wearables to advance sleep  ...  The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice.  ...  algorithms used to process the device outcomes are subjected to frequent manufacturer updates. • Provide date (beginning and end) of data collection, specific device model and firmware version.  ... 
doi:10.1093/sleep/zsz254 pmid:31641776 pmcid:PMC7368340 fatcat:al53sgw7j5gybbk4ya57hbc2tu

ESC Working Group on e-Cardiology Position Paper: Use of Commercially Available Wearable Technology for Heart Rate and Activity Tracking in Primary and Secondary Cardiovascular Prevention - In collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee

Magnus T Jensen, Roderick W Treskes, Enrico G Caiani, Ruben Casado-Arroyo, Martin R Cowie, Polychronis Dilaveris, David Duncker, Ines Frederix, Natasja De Groot, Philippe H Kolh, Hareld Kemps, Mamas Mamas (+11 others)
2021 European Heart Journal - Digital Health  
The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate and activity tracking, in clinical cardiovascular healthcare to  ...  While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary  ...  It is challenging to assess the accuracy of HR measurement by consumer devices as published studies present data of different subsets of devices tested through different protocols, applied in different  ... 
doi:10.1093/ehjdh/ztab011 fatcat:jaw5hdqwyfhatlzax7z3xzyuee
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