Analyzing sensor based human activity data using time series segmentation to determine sleep duration

Sleep is the most important thing to rest our brain and body. A lack of sleep has adverse effects on overall personal health and may lead to a variety of health disorders. According to Data from the Center for disease control and prevention in the United States of America, there is a formidable increase in the number of people suffering from sleep disorders like insomnia, sleep apnea, hypersomnia and many more. Sleep disorders can be avoided by assessing an individual\u27s activity over a period of time to determine the sleep pattern and duration. The sleep pattern and duration can be determined for an individual with the help of commercially available fitness devices such as Fitbit, Nike, Apple, and many others, which are activity trackers with accelerometer sensors. But these devices determine sleep duration from a \u27Proprietary Algorithm\u27, which processes the movement sensor data. Due to the proprietary nature, in a long-term study, the developer of the algorithm could update and make changes to the algorithm without revealing the details of the update to the user. This affects the measures reported by the algorithm. Hence to determine correct and reliable sleep duration, an Algorithm is developed by directly analyzing the actigraphy signals using time series segmentation. The study was done on a group of 20 healthy Undergraduate students from Missouri University of Science and Technology, whose daily physical activities were recorded using the GENEActiv accelerometer wristwatch worn on the non-dominant wrist. In this thesis, an open source algorithm has been developed using the daily physical activity data to estimate the sleep duration for any individual --Abstract, page iii

Exploring outcome measures for adults with myotonic dystrophy type 1

PhD ThesisMyotonic Dystrophy type 1 (DM1) is a multisystem progressive disorder with high heterogeneity. Novel emerging therapies require assessment tools that can effectively assess the effects of an intervention. The Outcome Measures in 5 Myotonic Dystrophy (OMMYD) Consortium has proposed a battery of functional outcome measures (FOM) identified as relevant for clinical trials in DM1. However, due to the variable nature of the disease and a scarcity of resources, there is a lack of systematic research that properly explores the use of these FOM. The current study examined three of these FOM and one extra related to 10 patients’ daily life performance. These are: (1) the ten-meters walk test; (2) the ten-meters walk/run test; (3) the 30-seconds sit and stand test; and, (4) a tri-axial accelerometer. By exploring the reliability, validity and responsiveness of these outcomes, we aimed to establish reference values and standard methodologies that could serve as guidance for clinical trials in DM1. A cohort of DM1 adults 15 screened for the two largest-to-date trials in DM1 (OPTIMSITIC and PHENO-DM1) were examined in relation to a set of pre-specified assessments and disease-burden scores. The results of this thesis supply disease-specific evidence of their validity, reliability and feasibility. The FOM, have shown to be psychometrically robust measures of functionality in DM1 and to be feasible for 20 clinical trials; they can provide a picture of patients’ muscle strength and perceived mobility and participation in life. The accelerometer can objectively quantify joints accelerations when walking at different speeds and summarise a DM1 patient’s habitual physical activity. The final choice of an outcome measure for a clinical trial in DM1 should be guided by disease domain that an intervention 25 is likely to impact on; but, a disease-specific study like this one will reduce the burden of protocol design whilst providing evidence supporting the decision-making process.the Medical Research Council Centre for Neuromuscular Diseases, Consejo Nacional de Ciencia y Tecnologia of Mexico and the Barbour Foundation.

Methods to Improve Our Understanding of the Health and Welfare Status of Sheep (Ovis Aries) and the Influences of their Immediate Environment

Studies into the effective use of accelerometers in the automated assessment of sheep behaviour to improve welfare has increased exponentially with promising preliminary results. Previous research has focused primarily on explicit behaviour classification, for example, parturition and urination events, with a view to create a commercial tool that will provide health warnings for farmers. Yet the majority of trials have not been conducted in a farm environment. This study aims to provide essential primary research investigating environmental variables that may influence the behavioural patterns of a commercial flock. This vital information has been largely overlooked and crucial when considering tools that provide health warnings, due to the many factors that influence sheep behaviour such as weather, vegetation, soil type, land typography and breed (Hinch, 2017). The primary aim of this study was to assess the most appropriate model to predict the behaviours of commercial ewes. This was achieved by deploying accelerometers on a commercial flock and simultaneously collecting manual observations and video recordings of flock’s individual activity. The raw acceleration data was processed to create 6 variables. Behaviour classification was also evaluated using three ethograms, each with two mutually exclusive behavioural/postural states: 1. Head Position (head up/down), 2. Posture (standing/lying), 3. Activity (resting/grazing). Three Window setting (3, 5 and 7 seconds) and five machine learning algorithms 4 (Linear Discriminate Analysis (LDA), Classification and Regression Trees (CART), K Nearest Neighbour (KNN), Support Vector Machines (SVM) and Random Forest (RF)) were evaluated. Results indicated a RF with a 7 second window the optimal model across all ethograms. (Accuracy by ethogram; 1) 91.5%, 2) 91.0% and 3) 99.3%). The secondary aim of this study was to use a Linear Mixed Model (LMM) to investigate the influence of temperature and rainfall on grazing and resting behaviours. This was accomplished by using the initially developed model (RF) on data collected from an unsupervised commercial flock, recorded in a second trial. Results indicated that there was a significant positive relationship between grazing durations and rainfall (p.001), this finding conflicts with previous research observations and is yet unpublished. In addition, prior sheep behaviour research has suggested ‘foraging’ as the dominant activity, results from this trial indicate the dominant daily activity was resting (67% of daily activity). In conclusion this study highlights the difficultly of defining what ‘normal’ sheep behaviour is and that it is not viable to implement a ‘one-size fits all’ approach. Further research is required in the behavioural assessment for this particularly malleable species

Identification of earlier predictors of pregnancy complications through wearable technologies in a Brazilian multicentre cohort : Maternal Actigraphy Exploratory Study I (MAES-I) study protocol

Introduction Non-invasive tools capable of identifying predictors of maternal complications would be a step forward for improving maternal and perinatal health. There is an association between modification in physical activity (PA) and sleep–wake patterns and the occurrence of inflammatory, metabolic, pathological conditions related to chronic diseases. The actigraphy device is validated to estimate PA and sleep–wake patterns among pregnant women. In order to extend the window of opportunity to prevent, diagnose and treat specific maternal conditions, would it be possible to use actigraphy data to identify risk factors for the development of adverse maternal outcomes during pregnancy? Methods and analysis A cohort will be held in five centres from the Brazilian Network for Studies on Reproductive and Perinatal Health. Maternal Actigraphy Exploratory Study I (MAES-I) will enrol 400 low-risk nulliparous women who will wear the actigraphy device on their wrists day and night (24 hours/day) uninterruptedly from 19 to 21 weeks until childbirth. Changes in PA and sleep–wake patterns will be analysed throughout pregnancy, considering ranges in gestational age in women with and without maternal complications such as pre-eclampsia, preterm birth (spontaneous or provider-initiated), gestational diabetes, maternal haemorrhage during pregnancy, in addition to perinatal outcomes. The plan is to design a predictive model using actigraphy data for screening pregnant women at risk of developing specific adverse maternal and perinatal outcomes

Calibration and Cross-validation of Accelerometry in Children and Adolescents with Cystic Fibrosis

Commonly used cut-points may misclassify physical activity (PA) in people with cystic fibrosis (CF). The aim of this study was to develop and cross-validate condition-specific cut-points in children and adolescents with CF. Thirty-five children and adolescents with CF (15 girls; 11.6 ± 2.8 years) and 28 controls (16 girls; 12.2 ± 2.7 years), had their energy expenditure and triaxial acceleration measured during six daily activities of varying intensities. Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) were extracted using both GENEActiv (both wrists) and ActiGraph GT9X (both wrists and right waist) accelerometers. ROC curves were used to determine healthy and CF-specific raw acceleration cut-points for sedentary time (SED), moderate physical activity (MPA) and vigorous physical activity (VPA). The PA cut-points were generally lower in CF compared to controls for both ENMO (60.2–73.1 vs. 63.5–86.8 mg) and MAD (58.9–85.2 vs. 75.9–93.7 mg). These substantial inter-cut-point differences support the need for disease-specific cut-points.Output Status: Forthcoming/Available Onlin

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

Assessment of Physical Activity in Adults with Progressive Muscle Disease

Introduction: Insufficient physical activity is a major threat to global health. Physical activity benefits peoples’ physical and mental health. The general population, including people living with disabilities and muscle wasting conditions, are recommended to avoid excessive sedentary time and engage in daily activity. Adults with progressive muscle disease experience barriers to physical activity participation, including muscle weakness, fatigue, physical deconditioning, impairment, activity limitations and participation restrictions (including societal and environmental factors), and fear of symptom exacerbation. More research is required to understand the inter-relationship between health and physical activity for adults with progressive muscle disease, particularly non-ambulant people who are under-represented in the existing research literature. Accurate measurement of FITT (frequency, intensity, time, and type of physical activity) is vital for high-quality physical activity assessment. The aim of this thesis was to assess the physical activity of ambulant and non-ambulant adults with progressive muscle disease.Systematic review findings identified various measures used to assess physical activity in adults with muscular dystrophy, including accelerometers, direct observation, heart rate monitors, calorimetry, positioning systems, activity diaries, single scales, interviews and questionnaires. None of the measures identified in the systematic review had well established measurement properties for adults with muscular dystrophy.Patient and public involvement interviews highlighted the importance of inclusive, remote, and technology-facilitated research design, the potential intrusion of direct observations of physical activity, the familiarity of questionnaires for data collection, and practical considerations to ensure wearing an activity monitor was not too burdensome.A feasibility study using multiple methods in 20 ambulant and non-ambulant adults with progressive muscle disease revealed satisfactory acceptability, interpretability, and usability of Fitbit and activity questionnaires, in both paper and electronic formats. During supervised activity tasks, Fitbit was found to have satisfactory criterion validity, reliability, and responsiveness and measurement properties were strengthened using multisensory measurement.An observational, longitudinal study that included 111 ambulant and non-ambulant adults with progressive muscle disease showed that:Activity monitoring had satisfactory validity, reliability and responsiveness using Fitbit, but there was considerable measurement error between Fitbit and the research grade GENEActiv accelerometer. Fitbit thresholds and multiple metrics (including accelerometer and heart rate data extrapolations of FITT) were appropriate for physical activity assessment in ambulant and non-ambulant adults with progressive muscle disease.Activity self-report had unsatisfactory concurrent validity, test-retest reliability, and responsiveness with substantial activity overestimation using the modified International Physical Activity Questionnaire. However, self-report properties were improved when used concurrently with Fitbit.Observed physical activity in adults with progressive muscle disease was generally low with excessive daily sedentary time. Activity frequencies, intensities and durations were lower, and activity types were more domestic, for wheelchair users and during the COVID-19 lockdown. Lower physical activity was significantly associated with greater functional impairment, less cardiorespiratory fitness, worse metabolic health, and lower quality of life. Activity optimisation thresholds and minimal clinically important differences were established.Discussion: The implications of this thesis include guidance for selection of appropriate physical activity measures by clinicians and researchers working with adults with progressive muscle disease. Fitbit is suitable in clinical practice and research for interactive, weekly remote activity monitoring or to support activity self-management and may represent an appropriate compromise between potential underestimation by accelerometry alone, and overestimation by self-report alone. A draft conceptual framework for physical activity measurement was also proposed. It includes frequency, intensity, time, and type of physical activity, and incorporates wider aspects of the physical activity construct, including somatic factors (relating to progressive muscle disease and underlying fitness) and contextual factors (relating to personal, social, and environmental situations). Future research will build on the knowledge gained in this thesis, furthering understanding of the inter-relationships between physical activity, health and wider contexts. Implementation will include testing a remote physical activity optimisation intervention that is inclusive of ambulant and non-ambulant participants, featuring Fitbit self-monitoring with a focus on optimisation of daily activity frequency and regularly interrupting sedentary time.</div

Exploring the relationship between patterns of physical activity, sedentary behaviour and cardiometabolic health in middle-aged Irish adults

Background and Study Rationale Being physically active is a major contributor to both physical and mental health. More specifically, being physically active lowers risk of coronary heart disease, high blood pressure, stroke, metabolic syndrome (MetS), diabetes, certain cancers and depression, and increases cognitive function and wellbeing. The physiological mechanisms that occur in response to physical activity and the impact of total physical activity and sedentary behaviour on cardiometabolic health have been extensively studied. In contrast, limited data evaluating the specific effects of daily and weekly patterns of physical behaviour on cardiometabolic health exist. Additionally, no other study has examined interrelated patterns and minute-by-minute accumulation of physical behaviour throughout the day across week days in middle-aged adults. Study Aims The overarching aims of this thesis are firstly to describe patterns of behaviour throughout the day and week, and secondly to explore associations between these patterns and cardiometabolic health in a middle-aged population. The specific objectives are to: 1 Compare agreement between the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and GENEActiv accelerometer-derived moderate-to-vigorous (MVPA) activity and secondly to compare their associations with a range of cardiometabolic and inflammatory markers in middle-aged adults. 2 Determine a suitable monitoring frame needed to reliably capture weekly, accelerometer-measured, activity in our population. 3 Identify groups of participants who have similar weekly patterns of physical behaviour, and determine if underlying patterns of cardiometabolic profiles exist among these groups. 4 Explore the variation of physical behaviour throughout the day to identify whether daily patterns of physical behaviour vary by cardiometabolic health. Methods All results in this thesis are based on data from a subsample of the Mitchelstown Cohort; 475 (46.1% males; mean aged 59.7±5.5 years) middle-aged Irish adults. Subjective physical activity levels were assessed using the IPAQ-SF. Participants wore the wrist GENEActiv accelerometer for 7 consecutive days. Data was collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised based on validated cut-offs. Data on cardiometabolic and inflammatory markers was collected according to standard protocol. Cardiometabolic outcomes (obesity, diabetes, hypertension and MetS) were defined according to internationally recognised definitions by World Health Organisation (WHO) and Irish Diabetes Federation (IDF). Results The results of the first chapter suggest that the IPAQ-SF lacks the sensitivity to assess patterning of activity and guideline adherence and assessing the relationship with cardiometabolic and inflammatory markers. Furthermore, GENEActiv accelerometer-derived MVPA appears to be better at detecting relationships with cardiometabolic and inflammatory markers. The second chapter examined variations in day-to-day physical behaviour levels between- and within-subjects. The main findings were that Sunday differed from all other days in the week for sedentary behaviour and light activity and that a large within-subject variation across days of the week for vigorous activity exists. Our data indicate that six days of monitoring, four weekdays plus Saturday and Sunday, are required to reliably estimate weekly habitual activity in all activity intensities. In the next chapter, latent profile analysis of weekly, interrelated patterns of physical behaviour identified four distinct physical behaviour patterns; Sedentary Group (15.9%), Sedentary; Lower Activity Group (28%), Sedentary; Higher Activity Group (44.2%) and a Physically Active Group (11.9%). Overall the Sedentary Group had poorer outcomes, characterised by unfavourable cardiometabolic and inflammatory profiles. The remaining classes were characterised by healthier cardiometabolic profiles with lower sedentary behaviour levels. The final chapter, which aimed to compare daily cumulative patterns of minute-by-minute physical behaviour intensities across those with and without MetS, revealed significant differences in weekday and weekend day MVPA. In particular, those with MetS start accumulating MVPA later in the day and for a shorted day period. Conclusion In conclusion, the results of this thesis add to the evidence base regards an optimal monitoring period for physical behaviour measurement to accurately capture weekly physical behaviour patterns. In addition, the results highlight whether weekly and daily distribution of activity is associated with cardiometabolic health and inflammatory profiles. The key findings of this thesis demonstrate the importance of daily and weekly physical behaviour patterning of activity intensity in the context of cardiometabolic health risk. In addition, these findings highlight the importance of using physical behaviour patterns of free-living adults observed in a population-based study to inform and aid health promotion activity programmes and primary care prevention and treatment strategies and development of future tailored physical activity based interventions