1,788 research outputs found
Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels
Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit
Signal Processing and Machine Learning Techniques Towards Various Real-World Applications
abstract: Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Methods and techniques for analyzing human factors facets on drivers
Mención Internacional en el tÃtulo de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident.
Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces.
The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed.
Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn.
From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models.
Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y TecnologÃa Informática por la Universidad Carlos III de MadridPresidente: José MarÃa Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart
Pulse Signal System: Sensing, Data Acquisition and Body Area Network
Heart rate variability (HRV) is an important physiological signal of the human body, which
can serve as a useful biomarker for the cardiovascular health status of an individual. There are
many methods to measure the HRV using electrical devices, such as ECG and PPG etc. This work
presents a novel HRV detection method which is based on pressure detection on the human wrist.
This method has been compared with existing HRV detection methods.
In this work, the proposed system for HRV detection is based on polyvinylidene difluoride
(PVDF) sensor, which can measure tiny pressure on its surface. Three PVDF sensors are mounted
on the wrist, and a three-channel conditioning circuit is used to amplify signals generated by the
sensors. An analog-to-digital converter and Arduino microcontroller are used to sample and process
the signal. Based on the obtained signals, the HRV can be processed and detected by the
proposed PVDF-sensor-based system.
Another contribution of this work is in designing a wireless body area network (WBAN) to
transmit data acquired on the human body. This WBAN combines two different wireless network
protocols, for both efficient power consumption and data rate. Bluetooth Low Energy protocol is
used for transmitting data from the microcontroller to a personal device, and Wi-Fi is used to send
data to other terminals. This provides the potential for remote HRV signal monitoring.
A dataset consisting of two subjects was used to experimentally validate the proposed system
design and signal processing method. ECG signals are acquired from subjects with wrist pulse
signals for comparison as standard signal. The waveforms of ECG signals and wrist pulse signals
are compared and HRV values are calculated from these two signals separately. The result shows
that HRV calculated by wrist pulse has low error rate. A test of movement effect shows the sensor
can resist mild motions of wrist. Some future improvements of system design and further signal
processing methods are also discussed in the last chapter
Ultrasound and fluorescence optical imaging biomarkers for early diagnosis and prediction of rheumatoid arthritis
Prevention of rheumatoid arthritis (RA) is most desirable together with curative treatment
which is, however, not yet available. Today, the correct timely diagnosis and early
treatment interventions to prevent disease progression remain the best options for our
patients. In this thesis, I explore the diagnostic and predictive value of musculoskeletal
ultrasound (MSUS) and fluorescence optical imaging (FOI) in identifying biological
features on images indicative of existing or emerging joint inflammation (synovitis).
In study 1, we tested and compared the diagnostic utility of FOI with clinical examination
and musculoskeletal ultrasound (MSUS) to detect active synovitis in 872 joints of 26
patients with different rheumatic diseases (46% early RA). Fluorescence optical imaging
proved to be 80% sensitive and 96% specific, having a 77% positive predictive value (PPV)
and 97% negative predictive value (NPV) for detecting silent synovitis.
In study 2 we showed FOI’s ability to quantify digital disease activity (DACT) scores of
1326 joints in 39 early RA patients to be 81% sensitive and 90% specific, with 96% PPV and
61% NPV. These results justify FOI use in clinical practice, to assist the rheumatologist to
make an earlier diagnosis with greater confidence. Unsupervised cluster differences
emerged for seropositive and seronegative RA patients showing FOI’s ability to
objectively quantify hand joint inflammation using novel DACT scoring methods.
In study 3 we report good association among the two ultrasound semi-quantitative
scoring (SQS) methods to that of a novel quantitative scoring (QS) measure of color
Doppler pixel counts in 37 established RA patients. Although SQS well correlated with QS
to assess active synovitis, the SQS methods lacked visual perceptions of raters to
distinguish between grade cut-offs which may help to further revise the criteria used to
objectively quantify disease activity.
In study 4, we show the value of ultrasound and immune-inflammatory biomarkers in
predicting arthritis onset in individuals positive for Anti-CCP with musculoskeletal
complaints at risk of RA development. We propose the recognition of a high-risk RA phase
characterized by presence of certain ACPA reactivities, IL15-Rα, IL6; and ultrasound
detected tenosynovitis, and possibilities to identify (low and high) risk groups for arthritis
progression.
Overall, our findings on imaging contribute towards a) silent synovitis detection despite
negative clinical investigation, b) objective quantitative measures to monitor the effects
of RA therapy and c) early identification of certain predictive imaging and biological
features/biomarkers that precede arthritis development (tenosynovitis and/or bursitis) in
individuals at risk for developing RA, enabling closer monitoring and early diagnosis
Non-Contact Sleep Monitoring
"The road ahead for preventive medicine seems clear. It is the delivery
of high quality, personalised (as opposed to depersonalised) comprehensive
medical care to all." Burney, Steiger, and Georges (1964)
This world's population is ageing, and this is set to intensify over the next forty years.
This demographic shift will result in signicant economic and societal burdens (partic-
ularly on healthcare systems). The instantiation of a proactive, preventative approach
to delivering healthcare is long recognised, yet is still proving challenging. Recent work
has focussed on enabling older adults to age in place in their own homes. This may
be realised through the recent technological advancements of aordable healthcare sen-
sors and systems which continuously support independent living, particularly through
longitudinally monitoring deviations in behavioural and health metrics. Overall health
status is contingent on multiple factors including, but not limited to, physical health,
mental health, and social and emotional wellbeing; sleep is implicitly linked to each of
these factors.
This thesis focusses on the investigation and development of an unobtrusive sleep mon-
itoring system, particularly suited towards long-term placement in the homes of older
adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing
grid designed to infer bed times and bed exits, and also for the detection of development
of bedsores. This work extends the capacity of this sensor. Specically, the novel contri-
butions contained within this thesis focus on an in-depth review of the state-of-the-art
advances in sleep monitoring, and the development and validation of algorithms which
extract and quantify UMBS-derived sleep metrics.
Preliminary experimental and community deployments investigated the suitability of the
sensor for long-term monitoring. Rigorous experimental development rened algorithms
which extract respiration rate as well as motion metrics which outperform traditional
forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal
features were derived from UMBS data as a means of describing movement during sleep.
These features were compared across experimental, domestic and clinical data sets, and
across multiple sleeping episodes. Lastly, the optimal classier (built using a combina-
tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and
reliably across both younger and older cohorts.
Through long-term deployment, it is envisaged that the UMBS-derived features (in-
cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and
sleep/wake state) may be used to provide unobtrusive, continuous insights into over-
all health status, the progression of the symptoms of chronic conditions, and allow the
objective measurement of daily (sleep/wake) patterns and routines
The Impact of Digital Technologies on Public Health in Developed and Developing Countries
This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic
Improving Access and Mental Health for Youth Through Virtual Models of Care
The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial
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