ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

Automated Remote Pulse Oximetry System (ARPOS)

Funding: This research is funded by the School of Computer Science and by St Leonard’s Postgraduate College Doctoral Scholarship, both at the University of St Andrews for Pireh Pirzada’s PhD. Early work was funded by the Digital Health & Care Innovation Centre (DHI).Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B and IR data. Moreover, no formal remote photoplethysmography studies have been done in real life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from Colour, IR and Depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.Publisher PDFPeer reviewe

The design and evaluation of discrete wearable medical devices for vital signs monitoring

The observation, recording and appraisal of an individual’s vital signs, namely temperature, heart rate, blood pressure, respiratory rate and blood oxygen saturation (SpO2), are key components in the assessment of their health and wellbeing. Measurements provide valuable diagnostic data, facilitating clinical diagnosis, management and monitoring. Respiratory rate sensing is perhaps the most under-utilised of all the vital signs, being routinely assessed by observation or estimated algorithmically from respiratory-induced beat-to-beat variation in heart rate. Moreover there is an unmet need for wearable devices that can measure all or most of the vital signs. This project therefore aims to a) develop a device that can measure respiratory rate and b) develop a wearable device that can measure all or most of the vital signs. An accelerometer-based clavicular respiratory motion sensor was developed and compared with a similar thoracic motion sensor and reference using exhalatory flow. Pilot study results established that the clavicle sensor accurately tracked the reference in monitoring respiratory rate and outperformed the thoracic device. An Ear-worn Patient Monitoring System (EPMS) was also developed, providing a discrete telemonitoring device capable of rapidly measuring tympanic temperature, heart rate, SpO2 and activity level. The results of a comparative pilot study against reference instruments revealed that heart rate matched the reference for accuracy, while temperature under read (< 1°C) and SpO2 was inconsistent with poor correlation. In conclusion, both of the prototype devices require further development. The respiratory sensor would benefit from product engineering and larger scale testing to fully exploit the technology, but could find use in both hospital and community-based The design and evaluation of discrete wearable medical devices for vital signs monitoring DG Pitts ii Cranfield University monitoring. The EPMS has potential for clinical and community use, having demonstrated its capability of rapidly capturing and wirelessly transmitting vital signs readings. Further development is nevertheless required to improve the thermometer probe and resolve outstanding issues with SpO2 readings

Data integrity based methodology and checklist for identifying implementation risks of physiological sensing in mHealth projects

Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.; This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks.; We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved.; Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified.; We developed a data integrity-based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

A synergistic wearable health monitoring system using cellular network technology

Thesis (M.S.) University of Alaska Fairbanks, 2017This thesis presents a synergistic approach to healthcare applications by integrating a wearable health monitoring system into a smart home system. By exploiting synergy within each system and between these two systems, this thesis shows that the efficiency of the health care can be increased while providing the added advantage of utmost user-friendly environment. Initially, a wearable health monitoring prototype system was developed for vital sign data collection and processing. The developed system used biosensor integration to distinguish amongst multiple physical activities and to compare the variations in physiological conditions according to physical activity of the user. Afterward, system learning techniques were established for accomplishing the scalability of the health monitoring system. The resulting system is able to monitor different users without the need for explicitly changing the thresholds for the individual user. The health monitoring was further improved through integration with the smart home system to exploit synergy between various physiological sensors and to reduce false alarms generated by the system. A cellular communication interface was developed for transmitting the collected data to a remote caregiver and also to store the time-stamped data on the online web server. A web interface was developed to allow monitoring user's health and activity data, along with their surrounding environment

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Data Analytics in Chronic Disease Self-Management: Statistical and Machine Learning Methodologies for Knowledge Discovery based on Quantified Self Data

Η διαχείριση των χρόνιων παθήσεων συνιστά μια από τις σημαντικότερες προκλήσεις των σύγχρονων συστημάτων υγείας. Η επιτακτική ανάγκη της συνεχούς διαχείρισης των νοσημάτων αυτών, που συνιστούν αιτία θανάτου για περισσότερο από το 70% του πληθυσμού παγκοσμίως [1], ήταν ένας από τους λόγους που οδήγησαν τον τομέα της ηλεκτρονικής υγείας να γνωρίσει ραγδαία ανάπτυξη. Παράλληλα, η ιδέα της αυτοδιαχείρισης προσωπικών δεδομένων υγείας και τρόπου ζωής, υπό το πρίσμα των νέων τεχνολογιών, κερδίζει έδαφος πολύ γρήγορα. Στις μέρες μας, οι αισθητήρες συνιστούν αναπόσπαστο κομμάτι της καθημερινότητας και συλλέγουν τεράστιες ποσότητες δεδομένων, ελέγχοντας κάθε πτυχή αυτής. Η πρόκληση, λοιπόν, είναι πώς θα καταφέρουμε να διαχειριστούμε όλα αυτά τα δεδομένα που προκύπτουν από το συνδυασμό των υπηρεσιών ηλεκτρονικής υγείας με τις τεχνολογίες φορετών αισθητήρων και κυρίως πώς θα τα ερμηνεύσουμε, ώστε να διευρύνουμε τους ορίζοντες της επιστημονικής έρευνας [2]. Στο σημείο αυτό, ο τομέας της ανάλυσης δεδομένων καλείται να αναλάβει καθοριστικό ρόλο. Οι ασθενείς που χρησιμοποιούν τέτοιες τεχνολογίες, αποκτούν τη δυνατότητα να καταγράψουν και να επεξεργαστούν τα βιοσήματά τους, τις αθλητικές τους δραστηριότητες, τις καθημερινές συνήθειές τους ή ακόμα και τα συναισθήματά τους [3]. Τα δεδομένα που προκύπτουν συνιστούν τον πολύτιμο λίθο της στατιστικής και των τεχνικών μηχανικής μάθησης, η εφαρμογή των οποίων θα οδηγήσει σε εξόρυξη γνώσεων σχετικά με τους παράγοντες αυξημένης επικινδυνότητας για την υγεία ενός ασθενούς και θα παράσχει τη δυνατότητα εξατομικευμένης ιατρικής παρακολούθησης και άμεσης ενημέρωσης για αποφυγή επειγόντων περιστατικών. Η παρούσα διπλωματική εργασία προτείνει μια μεθοδολογία ανάλυσης δεδομένων που θα εξετάσει τη συνέπεια των ασθενών στο πρόγραμμα λήψης των μετρήσεών τους και θα μελετήσει την αλληλεπίδραση μεταξύ των διαφορετικών ημερήσιων μετρήσεων, με σκοπό τον προσδιορισμό του τρόπου με τον οποίο αυτοί οι παράγοντες μπορούν να επηρεάσουν την παρακολούθηση της υγείας των ασθενών. Παράλληλα, θα πραγματοποιηθούν μελέτες που γενικεύονται σε δημογραφικό επίπεδο, συμπεριλαμβα-νομένου του φύλου, της ηλικίας και της γεωγραφικής κατανομής, έτσι ώστε να εντοπιστούν οι στατιστικά σημαντικές διαφορές στις ιατρικές τιμές ανα πληθυσμιακή ομάδα και να εξαχθούν πιο στοχευμένα, κατάλληλα συμπεράσματα. Στοχεύοντας στη βελτίωση και εξατομίκευση της ιατρικής παρακολούθησης χρόνιων καταστάσεων υγείας, η προτεινόμενη λύση δύναται να αντιμετωπίσει τις προκλήσεις των ηλεκτρονικών υπηρεσιών υγείας, παρέχοντας στους ασθενείς τη δυνατότητα έγκαιρου εντοπισμού επικίνδυνων καταστάσεων, ενίσχυση της ευημερίας τους, κινητοποίηση για συμμόρφωση στο πρόγραμμα λήψης των μετρήσεών τους αλλά και την εξειδικευμένη θεραπευτική τους αγωγή, δέσμευση για άσκηση και, τέλος, μοντελοποίηση της συμπεριφοράς τους με σκοπό τη βελτίωση της φροντίδας του εαυτού τους και την απόκτηση μιας καλύτερης ποιότητας ζωής.Chronic diseases management is one of the greatest challenges of modern healthcare systems. Given the fact that non-communicable diseases are responsible for more than 70% of deaths worldwide [1], the constant monitoring of a patient’s health condition has become vital need and, hence, the era of mobile health starts to rise. At the same time, the idea of self-managing personal aspects of life, and not only, through the prism of new technologies, the so-called Quantified Self, gains ground rapidly. Nowadays, sensors constitute an integral part of the daily life and monitor almost every aspect of it, gathering enormous quantities of data. The challenge is how to control the data that derive from the combination of electronic health services with wearable sensor technologies and broaden the horizons of scientific research [2]. At this point, data analytics assumes its decisive role. Patients using such technologies gain the capability to record and process their vital signs, fitness activities, everyday habits, or even feelings [3]. The resulting data constitute the gemstone for statistical and machine learning techniques to be performed so that knowledge discovery can take place and, as a consequence, identify the risk factors in patients’ health and provide personalized medical follow-up and immediate feedback to avoid emergent situations. This graduate thesis proposes a data analytics solution that will examine patients’ consistency in their measurement schedule and study the interaction among the different daily measurements, with the scope of determining how these factors can influence the monitoring of their health. Studies generalized on a demographic level, including sex, age and geolocation, will also take place so that statistical significant differences can be identified in the medical values and, thus, appropriate recommendations can be derived per population group. Aiming at improving and personalizing the medical monitoring of chronic health conditions, the proposed solution can circumvent the challenges of electronic health systems and provide benefits for the involved patients, such as enhancement of their welfare, early detection of dangerous situations, assumption of further targeted monitoring, motivation to engage in self-caring activities and follow treatment and, last, modeling of their behavior to improve self-care and enjoy a better quality of life