04-01-2015, 02:24 AM
hello, mama send your project copy pls
ttharma1[at]gmail.com
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complete personal mobile system
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hello, mama send your project copy pls
ttharma1[at]gmail.com
30-03-2017, 11:38 AM
A variety of legacy and emerging health applications are designed to monitor the information detected on a person's physiological signals over time. Such applications include systems for monitoring cardiac conditions that have been in use by cardiologists for decades to recent prototypes for the control of elderly in cognitive impairment. This thesis focuses on addressing the challenges inherent in an interactive monitoring system - how and when to interact with a user. This research aims to improve these systems in two main ways: 1) explore how to interact through social-emotional and relational dialogue, and 2) explore when to interact by adjusting the timing of these interruptions. An interactive health application for data collection, annotation and feedback has been developed that is part of a longer-term research plan to collect data to understand more about stress, the physiological signals involved in its expression, and the interaction between Stress and interruptibility. The system has been developed on a mobile platform and uses affect and interrupt-sensitive strategies to engage users and enable real-time logging of information from stress, activity and timing through text and The audio input.
The health, mood and activities of people are closely related to their environment and seasons. Countries in extreme latitudes (eg Sweden, U.K. and Norway) experience enormous variations in their light levels, affecting the mental state, well-being and energy levels of the population. Advanced detection technologies in smartphones allow non-intrusive and longitudinal monitoring of user states. The data collected allow health professionals and individuals to diagnose and rectify problems caused by seasonality. In this article we introduce a personal mobile detection system that explores technologies in smartphones to efficiently and accurately detect exposure to light, mood and activity levels of people. We did a 2 year experiment with many users to test the functionality and performance of our system. The results show that we can obtain an accurate estimate of exposure to light by opportunistic measurement of light data in smart phones, tracking personal exposure to light and general seasonal trends. An optional questionnaire also provides information on the correlation between a user's mood and energy level. Our system is able to inform users of the dim light they are experiencing in the winter. It can also correlate light exposure data with reduced mood and energy, and provide quantitative measurements for changes in lifestyle.
Adult learning practice studies showed that learning activity is mobile between placements, time intervals and subject areas. In addition, learning follows a hierarchical organization at three operational levels: learning activities are discrete acts, grouped together to form learning episodes, which in turn are grouped together to form learning projects. KLeOS reflects this hierarchical structure and allows the user to organize and manage their learning experiences and resources as a visual timeline. In addition, it incorporates a knowledge map, which is updated as the user progresses through learning experiences. The organizer interface is based on the idea of timelines and employs project lines and lines of activity to represent lifelong user learning. The organizer forms a bridge between the timeline and the knowledge map by labeling the knowledge nodes with the context of the learning episode in which that knowledge was acquired. The architecture of KLeOS allows its use in a series of different platforms, guaranteeing the mobility.
The health, mood and activities of people are closely related to their environment and seasons. Countries in extreme latitudes (eg Sweden, U.K. and Norway) experience enormous variations in their light levels, affecting the mental state, well-being and energy levels of the population. Advanced detection technologies in smartphones allow non-intrusive and longitudinal monitoring of user states. The data collected allow health professionals and individuals to diagnose and rectify problems caused by seasonality. In this article we introduce a personal mobile detection system that explores technologies in smartphones to efficiently and accurately detect exposure to light, mood and activity levels of people. We did a 2 year experiment with many users to test the functionality and performance of our system. The results show that we can obtain an accurate estimate of exposure to light by opportunistic measurement of light data in smart phones, tracking personal exposure to light and general seasonal trends. An optional questionnaire also provides information on the correlation between a user's mood and energy level. Our system is able to inform users of the dim light they are experiencing in the winter. It can also correlate light exposure data with reduced mood and energy, and provide quantitative measurements for changes in lifestyle.
Adult learning practice studies showed that learning activity is mobile between placements, time intervals and subject areas. In addition, learning follows a hierarchical organization at three operational levels: learning activities are discrete acts, grouped together to form learning episodes, which in turn are grouped together to form learning projects. KLeOS reflects this hierarchical structure and allows the user to organize and manage their learning experiences and resources as a visual timeline. In addition, it incorporates a knowledge map, which is updated as the user progresses through learning experiences. The organizer interface is based on the idea of timelines and employs project lines and lines of activity to represent lifelong user learning. The organizer forms a bridge between the timeline and the knowledge map by labeling the knowledge nodes with the context of the learning episode in which that knowledge was acquired. The architecture of KLeOS allows its use in a series of different platforms, guaranteeing the mobility.
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