The longitudinal course of gross motor activity in schizophrenia - within and between episodes

Schizophrenia is associated with heterogeneous course of positive and negative symptoms. In addition, reduced motor activity as measured by wrist actigraphy has been reported. However, longitudinal studies of spontaneous motor activity are missing. We aimed to explore whether activity levels were stable within and between psychotic episodes. Furthermore, we investigated the association with the course of negative symptoms. In 45 medicated patients, we investigated motor behavior within a psychotic episode. In addition, we followed 18 medicated patients across 2 episodes. Wrist actigraphy and psychopathological ratings were applied. Within an episode symptoms changed but activity levels did not vary systematically. Activity at baseline predicted the course of negative symptoms. Between two episodes activity recordings were much more stable. Again, activity at the index episode predicted the outcome of negative symptoms. In sum, spontaneous motor activity shares trait and state characteristics, the latter are associated with negative symptom course. Actigraphy may therefore become an important ambulatory instrument to monitor negative symptoms and treatment outcome in schizophrenia

Bitcoin and Its Position on Financial Markets

Import 22/07/2015Cílem této bakalářské práce je empiricky ověřit vliv finančních a makroekonomických ukazatelů na vývoj ceny Bitcoinu. Naplnění tohoto cíle je dosaženo pomocí vědeckých metod syntézy a deskriptivní a korelační analýzy. Důvodem pro ověřování byla skutečnost, že většina autorů prováděla svoji analýzu před téměř dvěma roky, což v případě Bitcoinu představuje dávnou minulost. Vliv konkrétních finančních a makroekonomických indikátorů na cenu Bitcoinu byl nejprve ověřen v dlouhém období, které vzniklo prodloužením původního období použitého v jiné předchozí práci. V tomto období byl potvrzen vztah mezi cenou Bitcoinu a hodnotou Dow Jones indexu. Nepotvrdila se předchozí koncepce závislosti mezi cenou Bitcoinu a hodnotou směnného kurzu USD/EUR, resp. cenou ropy. V tomto období byla rovněž objevena nepřímá závislost mezi cenou Bitcoinu a cenou zlata. V samostatné podkapitole byl pak ověřen vliv všech těchto veličin na cenu Bitcoinu v krátkém období.The aim of this bachelor thesis is to empirically check the influence of financial and macroeconomic indicators on Bitcoin price. This is achieved by using synthesis and descriptive and correlation analysis as the main scientific methods. The reason for the research was that the majority of authors have done their analysis almost two years ago, which in case of Bitcoin means a very long time. First, the influence of specific financial and macroeconomic indicators on Bitcoin price has been checked in the long run (a period that was created by prolonging the period used in previous literature). In this period the relationship between Bitcoin price and Dow Jones Index value has been confirmed. The previous concept of Bitcoin price and its dependence on USD/EUR exchange rate value and oil price respectively has not been confirmed. A negative correlation between Bitcoin price and gold price has been discovered. After that the influence of all these indicators on Bitcoin price has been checked for the short run as well.156 - Katedra národohospodářskávelmi dobř

Potential for Circular Autopoietic Economy in High River Po Valley

This Gigamap aims to describe and summarize a student work carried out during the semester course Open Systems Design at Politecnico di Torino. This is one of the outputs from an analysis of the High River Po Valley area in the Italian Piedmont (Southwest of Turin) and subsequently an in-depth study of the relationships and “flows” with certain “currencies” between some selected economic and public actors. The analysis was conducted through five economic sectors: nature & ecosystem services, tourism, mobility and infrastructure, local craft activities and agri-food. The investigation of the territorial economy was carried out by giving particular attention to the production sectors characteristic of the territory and examine their production lines. The holistic diagnosis has highlighted the existence of several problems related to the linearity of the production processes. Through the five types of system flows (material, CO2 emissions/energy, water, economic and social flows), we designed new opportunities, new activities and new potential companies, thinking circularly and systemically. The Gigamap will be presented to stakeholders in a public hearing and used to illustrate and incubate a more circular economy that is more resilient and more regenerative. Reading the map The reader can find in the first part an abstract with the aim of the Gigamap, the five topics for the investigation and actors’ selection, a timeline about important events and some peculiarities of the territory. In the middle, the territorial map of a suggested improved territorial economy based on circular flows: the 16 actors are localized on the territory with new circular flows “designed in” between them. The boxes describe connections for new circular opportunities, with flows explained under the territorial map—finally, some data about the territory and the three main outputs emerging from the new circular linkages

Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living

Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments

A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL

MOESM1 of Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Additional file 1: Table S1 Confusion matrix for the classification of data

Results for the objective and subjective measure of usability (mean ± SD).

<p>Results for the objective and subjective measure of usability (mean ± SD).</p

Participant during the experiment, picking an ingredient during the shopping task.

<p>(Participant on the picture gave written informed consent to be photographed for publication).</p