Les récents progrès technologiques ont permis la miniaturisation des capteurs de mouvement permettant ainsi leur intégration dans des micro-systèmes médicaux de manière à ce qu'ils soient aisément portables par les personnes. Dans cette thèse nous proposons des algorithmes de traitement de données permettant d’interpréter les mesures de capteurs accélérométriques et de les associer aux différentes activités. Notre approche est fondée sur l’observation que la séquence d’activités à détecter possède une structure forte de dépendance temporelle. Nous proposons un système de reconnaissance de l’activité reposant sur une modélisation des activités comme une chaîne de Markov. En outre, notre système s’appuie sur des méthodes de classification paramétriques et non paramétriques. La sortie douce des classifieurs permet de construire des mesures de confiance en les activités. Ces mesures sont injectées dans l’algorithme de Viterbi qui fournit la séquence d’activités finale. Nos algorithmes sont validés à partir d’une base de données comportant 48 sujets ayant chacun réalisé environ 90 minutes d’activités physiques variées. Par ailleurs, cette thèse vise à fournir des réponses pratiques aux problèmes posés par l’élaboration d’un système de reconnaissance de l’activité physique. En premier lieu, nous nous posons la question du placement optimal des capteurs sur le corps, et du nombre de capteurs nécessaires pour une estimation fiable de l’activité. Nous abordons le problème de la sélection des caractéristiques pertinentes pour les classifieurs. Enfin, un problème crucial est lié à l’estimation autodidacte et en ligne de l’orientation des capteurs sur le corps du sujet : il s’agit du problème de la calibration des capteurs. Pour terminer, nous fournissons une implémentation “temps-réel” du système proposé, et construisons une base de données en vie libre pour valider notre démonstrateur temps-réel. / Advances in technology have led to the miniaturization of motion sensors, facilitating their use in small comfortable wearable devices. Such devices are of great interest in the biomedical field especially for applications aimed at estimating the daily physical activity of people. In this thesis, we propose signal processing algorithms allowing better interpretation of sensors measures and thus their mapping to different activities. Our approach is based on that activities have strong temporal dependencies. We propose a recognition system that models the activity sequence by a Markov chain. Our system relies on parametric and non-parametric classification methods. The soft output of classifiers permits the construction of confidence measures in the activities. These measures are later used as input to a Viterbi algorithm that gives the final estimation of the activity sequence. We validate our algorithms using a database containing 48 subjects, each of whom having carried out activities for more than 90 minutes. Moreover, this thesis aims at providing practical answers to challenges concerning the development of an activity recognition system. First of all, we wonder about the optimal sensor placement on the body, and about the number of sensors needed for a reliable estimation of activities. We also approach the problem of selecting relevant features for classifiers. Another crucial issue concerns the estimation of the sensor’s orientation on the body : this involves the problem of sensor calibration. Finally, we provide a “real-time” implementation of our system, and collect a database under realistic conditions to validate our implemented real-time demostrator.

Thesis (Ph. D.)--University of Oregon, 2008. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 99-111). Also available online in Scholars' Bank; and in ProQuest, free to University of Oregon users.

The concept of sleep disturbance is rather vague. Many people claim to suffer from sleep disturbance, but yet find it hard to describe exactly what they mean by the label in subjective terms. Sleep researchers have a similar problem, it is difficult to describe what is meant by sleep disturbance either in an objective or a subjective way, and harder still to relate sleep disturbance to sleepiness shown the following day.

Thesis (M.S. in Human Systems Integration)--Naval Postgraduate School, September 2009. / Thesis Advisor(s): Miller, Nita Lewis. "September 2009." Description based on title screen as viewed on 5 November 2009. Author(s) subject terms: Navy Standard Workweek (NSWW), Fatigue Avoidance Scheduling Tool (FAST), sleep, activity, Fatigue and Task Effectiveness Model (SAFTE), shiftwork, circadian rhythm, sleep, fatigue management, actigraphy, operational manning requirements. Includes bibliographical references (p. 87-89). Also available in print.

No description available.

Background: Sleep is commonly disrupted following pediatric concussion. Recently, post-concussion sleep quality has been identified as a potential factor that may influence recovery length. However, few studies have examined this relationship using objective sleep measures in a pediatric population. Nor, have any studies examined the trajectory of objective sleep parameters after pediatric concussion. Objectives: The purpose of this thesis is to: 1) Provide a review of current literature regarding pediatric concussion, healthy sleep quality, and sleep quality in the context of concussion and recovery from concussion; 2) Examine how objective sleep outcomes are affected and change post-concussion in children and adolescents; 3). Explore how post-concussion sleep quality parameters are related to length of recovery, quality of life (QOL), and depression symptomatology; and 4) Discusses the results from the two studies in the context of current literature and of each other. Methods: Sleep quality was inferred from the outcomes of five objective sleep parameters. Sleep parameters were measured using actigraphy in children and adolescents with concussion following return to school (RTA) and return to activity (RTA) protocols. Sleep data during the initial 4 weeks of recovery was assessed as an outcome, as a predictor of recovery length, and as a correlate of quality of life (QOL) and depression symptomatology Results: Most objective post-concussion sleep quality parameters were adversely affected by concussion but show trajectories indicating improvement throughout the initial 4 weeks of recovery. Sleep quality parameters were not associated with time to complete return to school or activity protocols. Sleep parameters were not strongly correlated with QOL or depression symptomatology outcomes. Conclusions: These results indicate that objective post-concussion sleep quality is impaired following concussion, but these outcomes do not appear to be associated with recovery, QOL or depression symptomatology. Other factors, or improvements in sleep quality may better explain recovery outcomes. / Thesis / Master of Science (MSc) / Children and adolescents are at risk for experiencing changes in sleep following concussion that result in poor sleep quality. Given the important role of sleep in maintaining our overall health, sleep likely plays a role in recovery. However, this relationship is poorly understood. The purpose of this dissertation is to identify how sleep changes following concussion and how these changes affect sleep quality and recovery. Study results reveal that the sleep parameters in the initial 4 weeks following concussion are significantly affected by concussion, suggesting that the sleep quality of children and adolescents is poorer than healthy youth. However, sleep quality does appear to improve with time. Further analyses found sleep quality does not appear to be related to recovery length or outcomes. Thus, the sleep quality of youth may be negatively impacted by concussion, but this may not directly influence recovery.

Both sleep diaries and actigraphy have been recommended to assess sleep in research and clinical settings. Investigators have traditionally used sleep diaries that were completed daily by participants and collected weekly but have recently begun using sleep diaries that are both completed and collected daily. No research had previously assessed the agreement between daily-collected sleep diaries and actigraph data over one week. Undergraduate students were randomly assigned to use daily- or weekly-collected sleep diaries. Sleep parameters obtained from these measures were compared to each other via concordance with concurrent actigraph data. It was hypothesized that daily-collected sleep diaries would have greater concordance with actigraphy than weekly-collected sleep diaries. Results indicated that daily-collected sleep diaries provided more reliable data than weekly-collected sleep diaries, but the differences were not statistically significant. Additional aims examined self-reported sleep diary adherence, the participation day number, and day of the week. There were trends for the Daily group to have better adherence. Overall concordance did not change based on the day number or day of the week. Both sleep diaries yield comparable sleep parameter data, suggesting that clinicians and researchers can use either method to estimate sleep parameters.

The amount of sleep per day among new Cadets at West Point during Cadet Basic Training (CBT) was investigated. Sleep was measured using actigraphy. The results indicated that new Cadets slept an average of approximately 340 minutes or 5 hours, 40 minutes per night. The results were compared with survey data to determine whether or not reported sleep prior to arrival at West Point matched measured sleep at CBT. The findings indicate that the study population is sleep-deprived during CBT. Additionally, the results show that, on average, New Cadets receive 2 hours, 6 minutes less sleep per night during CBT than before their arrival at West Point. The findings also indicate that sleep achieved was not due to the various comparison factors: Gender, Race, Company, Age, Recruited Athlete, and Morningness/Eveningness preference.

Approved for public release; distribution is unlimited / The Marine Aviation Weapons and Tactics Squadron One (MAWTS-1) command requested a sleep and performance study of the instructors and students of Weapons and Tactics Instructor (WTI) course 2- 16. Specifically, MAWTS-1 leadership wanted to know whether crew rest periods were being used for sleep and whether fatigue posed a risk to personnel during the course. This thesis expanded upon prior studies in military educational environments. The WTI 2-16 study collected sleep and performance data via wrist-worn actigraphy and psychomotor vigilance tests (PVT). Sleep duration and efficiency remained high throughout the course, but participation waned before the study ended. Both instructors and students appeared to receive adequate sleep of good quality (overall mean 7.4 hrs/night). There was little variability in the sleep patterns (i.e., WTI participants were abiding by the crew rest regulations). Compliance posed a significant challenge and limited the ability to correlate sleep to performance. We also measured self-reported fatigue and mood using standardized questionnaires. Results from subjective assessments showed a significant increase in self-reported fatigue as the course progressed. This thesis outlines a detailed methodology and lessons learned for follow-on studies of this type and recommends improvements to future studies. / Major, United States Marine Corps

Obstructive sleep apnoea (OSA) is a common disorder involving repeated cessations of breathing due to airway collapse, causing disruption of sleep cycles. The condition is under-diagnosed and the side effects are many and varied. Currently, the ‘gold standard’ diagnostic tool for OSA is a polysomnogram (PSG) which is carried out overnight in a hospital using multiple sensors. A PSG is expensive to set-up, run and analyse, and some subjects experience different sleep patterns due to the artificial conditions of the sleep laboratory. The aim of this thesis was to find a parsimonious and easy-to-collect set of signals (from the superset of signals recorded in sleep clinics) and other related information (such as demographics), and a set of automated methods that reliably determine which subjects are suitable for standard treatments, i.e. classify subjects requiring treatment (moderate OSA, severe OSA) from those not requiring treatment (normal, snorer, mild OSA), using a smartphone. Data were collected from 1354 subjects in the home using the Grey Flash polysomnographic recording device (Stowood Scientific Instruments, Oxford, UK). Analysis of the audio signal was initially performed using standard speech processing methods, where individual events were annotated and classified. The results achieved (accuracy (Ac) = 69.6%) using this approach were lower than those required for clinical acceptance. In all subsequent work in the thesis, subjects were classified from entire recordings rather than events. Multiscale entropy (MSE) was used to identify non-linear correlations in the audio data and quantify the irregularity of the data over many time scales. The inter-snore interval (ISI) was developed, motivated by clinical intuition. MSE and ISI were then applied to both actigraphy and photoplethysomgraphy (PPG) data, and different combinations of features were analysed. The features which displayed the highest predictive accuracy were derived from the PPG signal (Ac = 89.2%). This work demonstrated that, although audio- and actigraphy-based OSA screening is possible, to achieve clinically acceptable performance PPG remains an important key factor in diagnosis.