3,558 research outputs found

    Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

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    Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

    Automated smart home assessment to support pain management: Multiple methods analysis

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    ©Roschelle L Fritz, Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, Diane J Cook. Objective: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.Background: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.Methods: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.Results: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P \u3c .001). The regression formulation achieved moderate correlation, with r=0.42.Conclusions: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance

    Free will vs determinism: reconstructing the model for understanding space-time dynamics and the role of consciousness within the universe

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    The thrust of this thesis was to approach the historical question of whether or not “thought” or “mind” can affect physical processes from a different perspective. Alterations in generate random numbers from PN junction which are synapse-like interfaces mediating electron movement were assessed when people intended upon altering these fluctuations while being exposed to weak magnetic fields that could affect intention. The results indicated that specific physiological patterns of transcerebral magnetic fields interacted with intention to alter random fluctuation. Paired exposure of two random number devices at non-traditional distances to these patterned magnetic fields with changing angular velocities demonstrated clear evidence of classic excess correlation or “entanglement”. As the random variation drifted in one direction for one device the variation drifted in the other direction for the other device but only when the magnetic fields were operating. Quantitative electroencephalography (QEEG) correlates of the multiple subscales of a questionnaire by which “imaginative absorption” is inferred, indicated surprisingly strong associations between scores for specific subscales coupled to successful intention-related deviation of random numbers and low frequency power (theta-alpha range) within the right temporal lobe. However many other strong correlations were also observed. These results suggest that intention, an important traditional associate of “free will”, can affect random variations of electron-tunnelling processes but this coupling can be enhanced by externally originating pattern magnetic fields. These same fields when applied to two different spaces produce changes in random fluctuations that success excess correlation. One conclusion is that external forces that synchronize local spaces also occupied by brains could be a recondite determinant of the ultimate activity in electron movement in tissue whose correlative experience is the sense of “free will”.Master of Arts (MA) in Human Developmen

    Early diagnosis of disorders based on behavioural shifts and biomedical signals

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    There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life. Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing. This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.Jendearen portaeran eragin zuzena duten gaixotasun ugari daude. Hala ere, askotan, gaixotasuna pairatzen duten pertsonak ez dira euren egoerataz ohartzen, eta familiarteko edo lankideek identifikatu ohi dute berau jokabide aldaketetaz ohartzean. Portaera aldaketa hauek nabarmentzean, ordea, beranduegi izan ohi da eta atzerazeinak diren kalteak eraginda egon ohi dira. Osasun kalte larriak eta gehiegizko kostuak ekiditeko eta gaixoen bizi kalitatea hobetzeko gakoa, gaixotasuna garaiz detektatzea da. Gaur egun, etengabe zabaltzen ari den Nonahiko Konputazioaren paradigmari esker, erabiltzaileen portaera ereduak era diskretu batean monitorizatu daitezke, gailu teknologikoekin izandako interakzioari esker. Eskuartean dugun arazoari konponbidea emateko teknologi hau erabiltzeak gaixotasunen sorrera eta aurrerapena nonahi eta era diskretu batean monitorizatzeko gai diren sistemak garatzea ekarriko luke, hauek garaiz hautematea ahalbidetuz. Gaixotasun konkretu batzuentzat soluzioak proposatu izan dira teknologi honetan oinarrituz, baina metodologia orokor bat, gaixotasun sorta zabal baten detekzio goiztiarrerako erabilgarria izango dena, oraindik ez da aurkeztu. Tesi honek hutsune hori betetzea du helburu, mota honetako gaixotasunak garaiz hautemateko, era diskretu batean atzitutako datu fisiologiko eta konportamentalen erabileran oinarritzen den behaketa sistema orokor bat proposatuz. Proposatutako metodologia bi balidazio egoera desberdinen arteko analisi gurutzatu baten emaitza da: estresa lantokian eta Alzheimerra etxean, balidazio egoera bakoitzari dagozkion ekarpenak ere ondorioztatu ahal izan direlarik. Bi kasuen arteko antzekotasun eta desberdintasunen analisiak, gaixotasun berri bat nonahiko konputazioan oinarrituta detektatzeko jarraitu beharreko pausoak bere osotasunean eta era orokor batean definitzea ahalbidetu du

    Measuring acute effects of subanesthetic ketamine on cerebrovascular hemodynamics in humans using TD-fNIRS

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    Quantifying neural activity in natural conditions (i.e. conditions comparable to the standard clinical patient experience) during the administration of psychedelics may further our scientific understanding of the effects and mechanisms of action. This data may facilitate the discovery of novel biomarkers enabling more personalized treatments and improved patient outcomes. In this single-blind, placebo-controlled study with a non-randomized design, we use time-domain functional near-infrared spectroscopy (TD-fNIRS) to measure acute brain dynamics after intramuscular subanesthetic ketamine (0.75 mg/kg) and placebo (saline) administration in healthy participants (n = 15, 8 females, 7 males, age 32.4 ± 7.5 years) in a clinical setting. We found that the ketamine administration caused an altered state of consciousness and changes in systemic physiology (e.g. increase in pulse rate and electrodermal activity). Furthermore, ketamine led to a brain-wide reduction in the fractional amplitude of low frequency fluctuations, and a decrease in the global brain connectivity of the prefrontal region. Lastly, we provide preliminary evidence that a combination of neural and physiological metrics may serve as predictors of subjective mystical experiences and reductions in depressive symptomatology. Overall, our study demonstrated the successful application of fNIRS neuroimaging to study the physiological effects of the psychoactive substance ketamine in humans, and can be regarded as an important step toward larger scale clinical fNIRS studies that can quantify the impact of psychedelics on the brain in standard clinical settings

    A Neurophysiologic Study Of Visual Fatigue In Stereoscopic Related Displays

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    Two tasks were investigated in this study. The first study investigated the effects of alignment display errors on visual fatigue. The experiment revealed the following conclusive results: First, EEG data suggested the possibility of cognitively-induced time compensation changes due to a corresponding effect in real-time brain activity by the eyes trying to compensate for the alignment. The magnification difference error showed more significant effects on all EEG band waves, which were indications of likely visual fatigue as shown by the prevalence of simulator sickness questionnaire (SSQ) increases across all task levels. Vertical shift errors were observed to be prevalent in theta and beta bands of EEG which probably induced alertness (in theta band) as a result of possible stress. Rotation errors were significant in the gamma band, implying the likelihood of cognitive decline because of theta band influence. Second, the hemodynamic responses revealed that significant differences exist between the left and right dorsolateral prefrontal due to alignment errors. There was also a significant difference between the main effect for power band hemisphere and the ATC task sessions. The analyses revealed that there were significant differences between the dorsal frontal lobes in task processing and interaction effects between the processing lobes and tasks processing. The second study investigated the effects of cognitive response variables on visual fatigue. Third, the physiologic indicator of pupil dilation was 0.95mm that occurred at a mean time of 38.1min, after which the pupil dilation begins to decrease. After the average saccade rest time of 33.71min, saccade speeds leaned toward a decrease as a possible result of fatigue on-set. Fourth, the neural network classifier showed visual response data from eye movement were identified as the best predictor of visual fatigue with a classification accuracy of 90.42%. Experimental data confirmed that 11.43% of the participants actually experienced visual fatigue symptoms after the prolonged task

    Quantifying individual variation in fine-scale time and energy trade-offs in breeding grey seals: How do differing behavioural types solve these trade-offs?

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    Lactation is one of the most energetically demanding periods of any female mammal’s life history, where individuals strike a balance with limited resources between their daily activity and towards the growth of their offspring, while still maintaining enough energy stores to maintain themselves in the process. Capital breeding systems mean that females must sustain themselves and their offspring while fasting exclusively on energy reserves acquired beforehand. Female phocids as a result must deal with pressures of a brief terrestrial existence where trade-offs in time, behaviour, energy, and responsiveness to the environment can have tangible consequences to short-term fitness and health. The aim of this thesis was to use new techniques, specifically animal-borne accelerometers and heart rate monitors, to track behaviour and physiology and assess the inherent trade-offs therein through the core duration of lactation in a capital breeding phocid, the grey seal (Halichoerus grypus). Female grey seals were equipped with biologging devices on the Isle of May over three consecutive breeding seasons. Using accelerometry and heart rate techniques, I aimed (1) to remotely classify behaviour using machine learning techniques, (2) to assess trade-offs in time-activity for the duration of lactation, (3) to build a holistic picture of energy allocation within the species, and (4) to develop new methods for tracking heart rate and breathing for terrestrial mammals using grey seals as a model. I also assessed the effect that consistent individual variability in behaviour, stress-coping styles, may have on the methods developed here and how they may drive behaviour and energy trade-offs over time. Accelerometers presented a useful way to remotely track several key behaviours, accurately classifying the core static behaviours over lactation. Consistent individual differences in stress-coping styles, as determined from measures of heart rate variability, modulated almost every aspect of behaviour and physiology measured in this study. More specifically, consistent trade-offs were identified for grey seal mothers between balancing time spent in a state of rest against remaining vigilant across multiple contexts, but also that these individual differences drove how individuals manage and expend that energy, ultimately resulting in differences in short-term fitness outcomes. Effort towards nursing, however, appeared to be largely fixed. Individual differences in energy management also appear to result in different levels of plasticity to environmental pressures, suggesting that future ambient conditions may not be suitable for breeding seals. This thesis also successfully detected breathing rates on land, revealing new evidence as to the energy saving and water conservation benefits of regularly engaging in periods of breath-hold while at rest. Overall, this thesis has provided new tools for exploring behaviour and physiology, and the inherent trade-offs therein, with minimal disturbance to lactating phocid seals. These differences, while minute in the scope of evolutionary constraints, may be among the most important drivers for the success and survival of populations in the face of greater environmental variability as the climate continues to change

    Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

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    openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia Ăš in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attivitĂ  di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

    Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974

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    This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974
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