11,819 research outputs found

    A Data Fusion System to Study Synchronization in Social Activities

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    As the world population gets older, the healthcare system must be adapted, among others by providing continuous health monitoring at home and in the city. The social activities have a significant role in everyone health status. Hence, this paper proposes a system to perform a data fusion of signals sampled on several subjects during social activities. This study implies the time synchronization of data coming from several sensors whether these are embedded on people or integrated in the environment. The data fusion is applied to several experiments including physical, cognitive and rest activities, with social aspects. The simultaneous and continuous analysis of four subjects cardiac activity and GPS coordinates provides a new way to distinguish different collaborative activities comparing the measurements between the subjects and along time.Comment: Healthcom 201

    A novel Big Data analytics and intelligent technique to predict driver's intent

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    Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

    Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

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    In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

    Individualisation of time-motion analysis : a method comparison and case report series

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    © Georg Thieme Verlag KG. This study compared the intensity distribution of time-motion analysis data, when speed zones were categorized by different methods. 12 U18 players undertook a routine battery of laboratory- and field-based assessments to determine their running speed corresponding to the respiratory compensation threshold (RCT), maximal aerobic speed (MAS), maximal oxygen consumption (vVO 2max ) and maximal sprint speed (MSS). Players match-demands were tracked using 5 Hz GPS units in 22 fixtures (50 eligible match observations). The percentage of total distance covered running at high-speed (%HSR), very-high speed (%VHSR) and sprinting were determined using the following speed thresholds: 1) arbitrary; 2) individualised (IND) using RCT, vVO 2max and MSS; 3) individualised via MAS per se; 4) individualised via MSS per se; and 5) individualised using MAS and MSS as measures of locomotor capacities (LOCO). Using MSS in isolation resulted in 61 % and 39 % of player's % HSR and % VHSR, respectively, being incorrectly interpreted, when compared to the IND technique. Estimating the RCT from fractional values of MAS resulted in erroneous interpretations of % HSR in 50 % of cases. The present results suggest that practitioners and researchers should avoid using singular fitness characteristics to individualise the intensity distribution of time-motion analysis data. A combination of players' anaerobic threshold, MAS, and MSS characteristics are recommended to individualise player-tracking data

    The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

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    In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş

    Multimodal segmentation of lifelog data

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    A personal lifelog of visual and audio information can be very helpful as a human memory augmentation tool. The SenseCam, a passive wearable camera, used in conjunction with an iRiver MP3 audio recorder, will capture over 20,000 images and 100 hours of audio per week. If used constantly, very soon this would build up to a substantial collection of personal data. To gain real value from this collection it is important to automatically segment the data into meaningful units or activities. This paper investigates the optimal combination of data sources to segment personal data into such activities. 5 data sources were logged and processed to segment a collection of personal data, namely: image processing on captured SenseCam images; audio processing on captured iRiver audio data; and processing of the temperature, white light level, and accelerometer sensors onboard the SenseCam device. The results indicate that a combination of the image, light and accelerometer sensor data segments our collection of personal data better than a combination of all 5 data sources. The accelerometer sensor is good for detecting when the user moves to a new location, while the image and light sensors are good for detecting changes in wearer activity within the same location, as well as detecting when the wearer socially interacts with others
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