The Experts below are selected from a list of 48597 Experts worldwide ranked by ideXlab platform
Luca Benini - One of the best experts on this subject based on the ideXlab platform.
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a versatile Embedded Platform for emg acquisition and gesture recognition
IEEE Transactions on Biomedical Circuits and Systems, 2015Co-Authors: Simone Benatti, Filippo Casamassima, Bojan Milosevic, Elisabetta Farella, Philipp Schonle, Schekeb Fateh, Thomas Burger, Qiuting Huang, Luca BeniniAbstract:Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an Embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable Platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable Platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.
Bogdan Kwolek - One of the best experts on this subject based on the ideXlab platform.
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fall detection on Embedded Platform using kinect and wireless accelerometer
International Conference on Computers Helping People with Special Needs, 2012Co-Authors: Michal Kepski, Bogdan KwolekAbstract:In this paper we demonstrate how to accomplish reliable fall detection on a low-cost Embedded Platform. The detection is achieved by a fuzzy inference system using Kinect and a wearable motion-sensing device that consists of accelerometer and gyroscope. The foreground objects are detected using depth images obtained by Kinect, which is able to extract such images in a room that is dark to our eyes. The system has been implemented on the PandaBoard ES and runs in real-time. It permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection.
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ICCHP (2) - Fall detection on Embedded Platform using kinect and wireless accelerometer
Lecture Notes in Computer Science, 2012Co-Authors: Michal Kepski, Bogdan KwolekAbstract:In this paper we demonstrate how to accomplish reliable fall detection on a low-cost Embedded Platform. The detection is achieved by a fuzzy inference system using Kinect and a wearable motion-sensing device that consists of accelerometer and gyroscope. The foreground objects are detected using depth images obtained by Kinect, which is able to extract such images in a room that is dark to our eyes. The system has been implemented on the PandaBoard ES and runs in real-time. It permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection.
Simone Benatti - One of the best experts on this subject based on the ideXlab platform.
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a versatile Embedded Platform for emg acquisition and gesture recognition
IEEE Transactions on Biomedical Circuits and Systems, 2015Co-Authors: Simone Benatti, Filippo Casamassima, Bojan Milosevic, Elisabetta Farella, Philipp Schonle, Schekeb Fateh, Thomas Burger, Qiuting Huang, Luca BeniniAbstract:Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an Embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable Platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable Platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.
Radu Munteanu - One of the best experts on this subject based on the ideXlab platform.
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Embedded Platform for Web-based monitoring and control of a smart home
2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), 2015Co-Authors: Daniel Moga, Nicoleta Stroia, Dorin Petreus, Rozica Moga, Radu MunteanuAbstract:This paper presents the architecture of a low cost Embedded Platform for Web-based monitoring and control of a smart home. The Platform consists of a distributed sensing and control network, devices for access control and a residential gateway with touch-screen display offering an easy to use interface to the user as well as providing remote, Web based access. The key issues related to the design of the proposed Platform were addressed: the problem of security, the robustness of the distributed control network to faults and a low cost hardware implementation.
Guessoumabderrezak - One of the best experts on this subject based on the ideXlab platform.
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Object detection and segmentation algorithm implemented on a reconfigurable Embedded Platform based FPGA
WSEAS Transactions on Signal Processing archive, 2008Co-Authors: Saadislami, Mekkihamza, GuessoumabderrezakAbstract:In this article, we present a mixed software/hardware Implementation on a Xilinx's Microblaze Soft core based FPGA Platform. The reconfigurable Embedded Platform designed to support an important al...
