With recent advancements in the tele-monitoring and ambient assisted living technology, human activity recognition (HAR) has proven enormously important in elderly healthcare. With the rapid increase in the use of smartphones embedded with a wide variety of latest locomotion sensors in our daily life, a new role for smartphones as the performance evaluator for physical activity recognition has emerged. HAR by using the fusion of smartphone sensors data is comparatively a new area for exploration. In this paper, we have evaluated different classification algorithms for recognition of eight physical activities performed by individuals using the smartphone tri-axial accelerometer, gyroscope and magnetometer sensors. Our analyses of collected data indicate that sensor combination improves the overall performance of the classifiers to the maximum compared to their individual performances especially for walking upstairs and downstairs activities. Moreover, we propose the use of sensor fusion for activity monitoring and diagnostic suitable for heart failure patients.
Abstract. How humans infer probable information from the limited observed data? How they are able to build on little knowledge about the context in hand? Is the human memory repeatedly constructing and reconstructing the events that are being recalled? These are a few questions that we are interested in answering with our multimodal memory game (MMG) platform that studies human memory and their behaviors while watching and remembering TV dramas for a better recall. Based on the preliminary results of human learning obtained from the MMG games, we attempt to show that the human memory recall improves steadily with the number of game sessions. As an example case, we provide a comparison for the text-to-text and text-image-to-text learning and demonstrate that the addition of image context is useful in improving the learning.
Abstract. With recent advancements in the medical field, activity monitoring applications that are proficient in measuring health-related data have become common. These devices are capable of transmitting information wirelessly and can be used in conjunction with smart phones to pass on data content to devices with similar operating capability. Motion sensor embedded smartphones provide users information about their own physical activity in an understandable format that may be used for a variety of applications. In this paper, I introduce the design and implementation of a health activity monitoring and diagnostic system suitable for heart failure patients. The main objective of this work is to collect data from external sensors that measure patient's vital signs and predict future events by observing these physical readings of the heart patient.
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