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Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones
KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinska sensorer, signaler och system. University of Boras, Boras, Sweden.
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2015 (Engelska)Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, nr 10, s. 25607-25627Artikel i tidskrift (Refereegranskat) Published
Resurstyp
Text
Abstract [en]

Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

Ort, förlag, år, upplaga, sidor
MDPI AG , 2015. Vol. 15, nr 10, s. 25607-25627
Nyckelord [en]
physiological measurements, smart textiles, smartphone, ECG, bioimpedance, stress detection, ergonomics
Nationell ämneskategori
Medicinsk laboratorie- och mätteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-179614DOI: 10.3390/s151025607ISI: 000364242300048PubMedID: 26457710Scopus ID: 2-s2.0-84943742389OAI: oai:DiVA.org:kth-179614DiVA, id: diva2:892793
Anmärkning

QC 20160111

Tillgänglig från: 2016-01-11 Skapad: 2015-12-17 Senast uppdaterad: 2017-12-01Bibliografiskt granskad
Ingår i avhandling
1. Modular textile-enabled bioimpedance system for personalized health monitoring applications
Öppna denna publikation i ny flik eller fönster >>Modular textile-enabled bioimpedance system for personalized health monitoring applications
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

A growing number of factors, including costs, technological advancements, ageing populations, and medical errors, are leading industrialized countries to invest in research on alternative solutions to improve their health-care systems and increase patients’ quality of life. Personal health systems (PHS) examplify the use of information and communication technologies that enable a paradigm shift from the traditional hospital-centered healthcare delivery model toward a preventive and person-centered approach. PHS offer the means to monitor a patient’s health using wearable, portable or implantable systems that offer ubiquitous, unobtrusive biodata

acquisition, allowing remote monitoring of treatment and access to the patient’s status. Electrical bioimpedance (EBI) technology is non-invasive, quick and relatively affordable technique that can be used for assessing and monitoring different health conditions, e.g., body composition assessments for nutrition. When combined with state-of-the-art advances in sensors and textiles, EBI technologies are fostering the implementation of wearable bioimpedance monitors that use functional garments for personalized healthcare applications. This research work is

focused on the development of wearable EBI-based monitoring systems for ubiquitous health monitoring applications. The monitoring systems are built upon portable monitoring instrumentation and custom-made textile electrode garments.

Portable EBI-based monitors have been developed using the latest material technology and advances in system-on-chip technology. For instance, a portable EBI spectrometer has been validated against a commercial spectrometer for total body composition assessment using functional textile electrode garments. The development of wearable EBI-based monitoring units using functional garments and dry textile electrodes for body composition assessment and respiratory monitoring has been shown to be a feasible approach. The availability of these measurement systems indicates progress toward the real implementation of personalized healthcare systems.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH Royal Institute of Technology, 2017. s. 89
Serie
TRITA-STH ; 2017:6
Nyckelord
personal healthcare system, electrical bioimpedance, wearable sensors, pervasive monitoring, portable monitoring, body composition, chronic kidney disease, wireless sensor, ubiquitous, instrumentation
Nationell ämneskategori
Medicinsk laboratorie- och mätteknik
Forskningsämne
Teknik och hälsa
Identifikatorer
urn:nbn:se:kth:diva-207135 (URN)978-91-7729-377-4 (ISBN)
Disputation
2017-06-02, M402, Allégatan 1, Borås, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20170517

Tillgänglig från: 2017-05-17 Skapad: 2017-05-17 Senast uppdaterad: 2017-05-17Bibliografiskt granskad

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Gil-Pita, RobertoSeoane, Fernando

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Gil-Pita, RobertoFerreira, JavierSeoane, Fernando
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