Relevant bibliographies by topics / Evaluation of stress Wearable electronics

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Evaluation of stress Wearable electronics'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Evaluation of stress Wearable electronics"

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Giorgi, Andrea, Vincenzo Ronca, Alessia Vozzi, Nicolina Sciaraffa, Antonello di Florio, Luca Tamborra, Ilaria Simonetti, et al. "Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies." Sensors 21, no. 7 (March 26, 2021): 2332. http://dx.doi.org/10.3390/s21072332.

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The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.
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Chen, Xiaochen, Leena Ukkonen, and Johanna Virkki. "Reliability evaluation of wearable radio frequency identification tags: Design and fabrication of a two-part textile antenna." Textile Research Journal 89, no. 4 (January 11, 2018): 560–71. http://dx.doi.org/10.1177/0040517517750651.

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Passive radio frequency identification-based technology is a convincing approach to the achievement of versatile energy- and cost-efficient wireless platforms for future wearable applications. By using two-part antenna structures, the antenna-electronics interconnections can remain non-stressed, which can significantly improve the reliability of the textile-embedded wireless components. In this article, we describe fabrication of two-part stretchable and non-stretchable passive ultra-high frequency radio frequency identification textile tags using electro-textile and embroidered antennas, and test their reliability when immersed as well as under cyclic strain. The results are compared to tags with traditional one-part dipole antennas fabricated from electro-textiles and by embroidery. Based on the results achieved, the initial read ranges of the two-part antenna tags, around 5 m, were only slightly shorter than those of the one-part antenna tags. In addition, the tag with two-part antennas can maintain high performance in a moist environment and during continuous stretching, unlike the one-part antenna tag where the antenna-integrated circuit attachment is under stress.
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Tonacci, Alessandro, Lucia Billeci, Elisa Burrai, Francesco Sansone, and Raffaele Conte. "Comparative Evaluation of the Autonomic Response to Cognitive and Sensory Stimulations through Wearable Sensors." Sensors 19, no. 21 (October 27, 2019): 4661. http://dx.doi.org/10.3390/s19214661.

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Psychological stress is known to activate the autonomic nervous system (ANS), thus representing a useful target to be monitored to understand the physiological, unconscious effect of stress on the human body. However, little is known about how differently the ANS responds to cognitive and sensory stimulations in healthy subjects. To this extent, we enrolled 23 subjects and administered a stress protocol consisting of the administration of sensory (olfactory) and cognitive (mathematical) stressors. Autonomic parameters were unobtrusively monitored through wearable sensors for capturing electrocardiogram and skin conductance signals. The results obtained demonstrated an increase of the heart rate during both stress protocols, with a similar decrease of the heart rate variability. Cognitive stress test appears to affect the autonomic parameters to a greater extent, confirming its effects on the human body. However, olfactory stimulation could be useful to study stress in specific experimental settings when the administration of complex cognitive testing is not feasible.
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Liapis, Alexandros, Evanthia Faliagka, Christos P. Antonopoulos, Georgios Keramidas, and Nikolaos Voros. "Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables." Electronics 10, no. 13 (June 26, 2021): 1550. http://dx.doi.org/10.3390/electronics10131550.

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Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.
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Conforti, Ilaria, Ilaria Mileti, Zaccaria Del Prete, and Eduardo Palermo. "Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach." Sensors 20, no. 6 (March 11, 2020): 1557. http://dx.doi.org/10.3390/s20061557.

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Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.
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Leonidis, Asterios, Maria Korozi, Eirini Sykianaki, Eleni Tsolakou, Vasilios Kouroumalis, Danai Ioannidi, Andreas Stavridakis, Margherita Antona, and Constantine Stephanidis. "Improving Stress Management and Sleep Hygiene in Intelligent Homes." Sensors 21, no. 7 (March 30, 2021): 2398. http://dx.doi.org/10.3390/s21072398.

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High stress levels and sleep deprivation may cause several mental or physical health issues, such as depression, impaired memory, decreased motivation, obesity, etc. The COVID-19 pandemic has produced unprecedented changes in our lives, generating significant stress, and worries about health, social isolation, employment, and finances. To this end, nowadays more than ever, it is crucial to deliver solutions that can help people to manage and control their stress, as well as to reduce sleep disturbances, so as to improve their health and overall quality of life. Technology, and in particular Ambient Intelligence Environments, can help towards that direction, when considering that they are able to understand the needs of their users, identify their behavior, learn their preferences, and act and react in their interest. This work presents two systems that have been designed and developed in the context of an Intelligent Home, namely CaLmi and HypnOS, which aim to assist users that struggle with stress and poor sleep quality, respectively. Both of the systems rely on real-time data collected by wearable devices, as well as contextual information retrieved from the ambient facilities of the Intelligent Home, so as to offer appropriate pervasive relaxation programs (CaLmi) or provide personalized insights regarding sleep hygiene (HypnOS) to the residents. This article will describe the design process that was followed, the functionality of both systems, the results of the user studies that were conducted for the evaluation of their end-user applications, and a discussion about future plans.
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Bello, Hymalai, Bo Zhou, and Paul Lukowicz. "Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones." Sensors 20, no. 17 (August 30, 2020): 4904. http://dx.doi.org/10.3390/s20174904.

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Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).
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Debard, Glen, Nele De Witte, Romy Sels, Marc Mertens, Tom Van Daele, and Bert Bonroy. "Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project." Journal of Sensors 2020 (November 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8846077.

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Over the past years, mobile health (mHealth) applications and specifically wearables have become able and available to collect data of increasing quality of relevance for mental health. Despite the large potential of wearable technology, mental healthcare professionals are currently lacking tools and knowledge to properly implement and make use of this technology in practice. The Carewear project is aimed at developing and evaluating an online platform, allowing healthcare professionals to use data from wearables in their clinical practice. Carewear implements data collection through self-tracking, which is aimed at helping people in their behavioral change process, as a component of a broader intervention or therapy guided by a mental healthcare professional. The Empatica E4 wearables are used to collect accelerometer data, electrodermal activity (EDA), and blood volume pulse (BVP) in real life. This data is uploaded to the Carewear platform where algorithms calculate moments of acute stress, average resting heart rate (HR), HR variability (HRV), step count, active periods, and total active minutes. The detected moments of acute stress can be annotated to indicate whether they are associated with a negative feeling of stress. Also, the mood of the day can be elaborated on. The online platform presents this information in a structured way to both the client and their mental healthcare professional. The goal of the current study was a first assessment of the accuracy of the algorithms in real life through comparisons with comprehensive annotated data in a small sample of five healthy participants without known stress-related complaints. Additionally, we assessed the usability of the application through user reports concerning their experiences with the wearable and online platform. While the current study shows that a substantial amount of false positives are detected in a healthy sample and that usability could be improved, the concept of a user-friendly platform to combine physiological data with self-report to inform on stress and mental health is viewed positively in our pilots.
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Kwon, Yong‐Wook, Jong‐Sung Lee, Young‐Chang Joo, and Byoung‐Joon Kim. "In Twisting Motion, Stress‐Free Zone of Wearable Electronics." Advanced Electronic Materials 6, no. 3 (January 22, 2020): 1901239. http://dx.doi.org/10.1002/aelm.201901239.

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Rukasha, Tendai, Sandra I Woolley, Theocharis Kyriacou, and Tim Collins. "Evaluation of Wearable Electronics for Epilepsy: A Systematic Review." Electronics 9, no. 6 (June 10, 2020): 968. http://dx.doi.org/10.3390/electronics9060968.

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Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices.
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Dissertations / Theses on the topic "Evaluation of stress Wearable electronics"

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Muschalik, David. "Vyhodnocování úrovně stresu pilota při přistání s využitím nositelné elektroniky." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443772.

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The thesis focuses on the possibilities of objective stress measurement. It chooses parameters that are suitable for measurement by commonly available wearable electronics. It establishes a methodology for measuring stress using these devices, which it then verifies by conducting experiments with flight school students. It serves as proof of concept for similar measurements in further research or in flight schools.
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"Experimental Evaluation of the Feasibility of Wearable Piezoelectric Energy Harvesting." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.62820.

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abstract: Technological advances in low power wearable electronics and energy optimization techniques make motion energy harvesting a viable energy source. However, it has not been widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This work addresses this problem by analyzing the feasibility of powering low wearable power devices using piezoelectric energy generated at the human knee. We start with a novel mathematical model for estimating the power generated from human knee joint movements. This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting and validating this analytical model using a commercially available piezoelectric module. To this end, we implemented an experimental setup that replicates a human knee. Then, we performed experiments at different excitation frequencies and amplitudes with two commercially available Macro Fiber Composite (MFC) modules. These experimental results are used to validate the analytical model and predict the energy harvested as a function of the number of steps taken in a day. The model estimates that 13μWcan be generated on an average while walking with a 4.8% modeling error. The obtained results show that piezoelectricity is indeed a viable approach for powering low-power wearable devices.
Dissertation/Thesis
Masters Thesis Electrical Engineering 2020
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Book chapters on the topic "Evaluation of stress Wearable electronics"

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Carbonaro, Nicola, Pietro Cipresso, Alessandro Tognetti, Gaetano Anania, Danilo De Rossi, Federica Pallavicini, Andrea Gaggioli, and Giuseppe Riva. "Psychometric Assessment of Cardio-Respiratory Activity Using a Mobile Platform." In Wearable Technologies, 862–79. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5484-4.ch037.

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It is increasingly recognized that stress has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user psychophysiological parameter during daily life can be greatly helpful in stress evaluation. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors.In this study a wearable biosensor platform able to collect physiological and behavioral parameters is reported. The developed mobile platform, in terms of hardware and processing algorithms, is described. Moreover the use of this wearable biosensor platform in combination with advanced simulation technologies, such as virtual reality, offer interesting opportunities for innovative personal health-care solutions to stress.
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Trmčić, Branka Rodić, Aleksandra Labus, Svetlana Mitrović, Vesna Buha, and Gordana Stanojević. "Internet of Things in E-Health." In Wearable Technologies, 880–85. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5484-4.ch038.

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The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.
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Trmčić, Branka Rodić, Aleksandra Labus, Svetlana Mitrović, Vesna Buha, and Gordana Stanojević. "Internet of Things in E-Health." In Emerging Trends and Applications of the Internet of Things, 191–97. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2437-3.ch007.

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The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.
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Conference papers on the topic "Evaluation of stress Wearable electronics"

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Beskardesler, Kemal, and R. Burak Arslan. "Web-Based Evaluation of User Stress Using Stroop Test and Wearable Hardware." In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2019. http://dx.doi.org/10.23919/eleco47770.2019.8990655.

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Bu, Nan. "Stress evaluation index based on Poincaré plot for wearable health devices." In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE, 2017. http://dx.doi.org/10.1109/healthcom.2017.8210779.

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Hilbich, Daniel, Lesley Shannon, and Bonnie L. Gray. "Stretchable electronics for wearable and high-current applications." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Vijay K. Varadan. SPIE, 2016. http://dx.doi.org/10.1117/12.2219284.

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Elpidio, H., Lucas F. da Cruz, Vandermi J. da Silva, Mauro R. Da S. Teofilo Raimundo, Barreto, and Vicente F. Lucena. "I-Jack: Wearable system for collection and evaluation physiological data." In 2018 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2018. http://dx.doi.org/10.1109/icce.2018.8326191.

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Kathpalia, Bharat, David Tan, Ilan Stern, and Alper Erturk. "Evaluation of human-scale motion energy harvesting for wearable electronics." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Gyuhae Park. SPIE, 2017. http://dx.doi.org/10.1117/12.2260385.

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Cazacu, Dumitru, Silviu Ionita, and Sebastian Parlac. "The Evaluation of the PCB's Behaviour under the Mechanical Stress." In 2007 30th International Spring Seminar on Electronics Technology. IEEE, 2007. http://dx.doi.org/10.1109/isse.2007.4432842.

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Seung Seoup Lee, Jong Whan Baik, Jin Soo Kim, Hyung Jin Jeon, and Sung Yi. "Wafer Level Packaging by residual stress evaluation using piezoresistive stress sensors for the enhancement of reliability." In 2008 33rd IEEE/CPMT International Electronics Manufacturing Technology Conference (IEMT). IEEE, 2008. http://dx.doi.org/10.1109/iemt.2008.5507784.

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Koganemaru, M., T. Ikeda, M. Komori, N. Miyazaki, and H. Tomokage. "Evaluation of stress-induced effect on electronic characteristics of nMOSFETs using mechanical stress simulation and drift-diffusion device simulation." In 2008 2nd Electronics Systemintegration Technology Conference. IEEE, 2008. http://dx.doi.org/10.1109/estc.2008.4684461.

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Kumar, Aditya, Xiaowu Zhang, Q. X. Zhang, M. C. Jong, G. B. Huang, L. W. S. Vincent, V. Kripesh, et al. "Evaluation of Stresses in Thin Device Wafer using Piezoresistive Stress Sensor." In 2008 10th Electronics Packaging Technology Conference (EPTC 2008). IEEE, 2008. http://dx.doi.org/10.1109/eptc.2008.4763605.

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Lall, Pradeep, Hao Zhang, and Rahul Lall. "Design and Development of Biometric Sensor Wearable Band Using Flexible Electronics." In ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems collocated with the ASME 2017 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ipack2017-74232.

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Flexible electronics have a myriad of potential applications in fields such as healthcare, soldier situational awareness, soldier rehabilitation, sports performance, and textile manufacturing among other areas. The primary benefits that flexible electronics provide to both the producers and consumers are their light weight, low power consumption, efficiency, low cost of production, flexibility, and scalability. In comparison to rigid electronics, these systems would be subjected to a greater amount of mechanical and thermal stress in real-time due to their ability to be flexed, rolled, folded, and stretched. Environmental conditions such as bending, mechanical shock, water immersion, sweat, UV radiation, and temperature exposure could degrade the performance of these embedded electronic systems. At this time, there is a lack of suitable test standards and reliability data about flexible electronics manufacturing, assembly, and real-time use. In this paper, a fully flexible medical electronics system was built in full dimension to study the assembly and operation-related failure mechanisms of flexible and wearable electronics. The fabricated flexible electronics system measures pulse and muscle activity, and then transmits this data to a paired mobile device. The pulse rate was measured using an LED and a photo diode, while an electromyography (EMG) sensor was used to measure muscle activity. After collecting the data, the microcontroller sends it to a Bluetooth module, which can in turn transmit this information to a paired mobile device. Through experimentation with the fabricated flexible electronics device, unexpected degradation and quality issues were observed. In flexible PCBs, the space between the IC lead could not be isolated by the solder mask because of its large feature size and as a result, increases the risk of shortage between IC leads when subjected to mechanical stress. In addition, during the assembly process, high reflow temperature was found to subject a huge thermal stress on the connections between the solder pad and copper trace. Proper support of the solder pad should be designed to compensate the thermal stress during the reflow process, and prevent the copper joint on top of the board from being damaged. A set of guidelines for flexible medical electronics and an implementable reliability test standard can, therefore, be established for medical device manufacturers based on these reliability assessments.
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