167 research outputs found

    Wireless body sensor networks for health-monitoring applications

    Get PDF
    This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

    Efficient and Interference-Resilient Wireless Connectivity for IoT Applications

    Full text link
    With the coming of age of the Internet of Things (IoT), demand on ultra-low power (ULP) and low-cost radios will continue to boost tremendously. The Bluetooth-Low-energy (BLE) standard provides a low power solution to connect IoT nodes with mobile devices, however, the power of maintaining a connection with a reasonable latency remains the limiting factor in defining the lifetime of event-driven BLE devices. BLE radio power consumption is in the milliwatt range and can be duty cycled for average powers around 30ÎŒW, but at the expense of long latency. Furthermore, wireless transceivers traditionally perform local oscillator (LO) calibration using an external crystal oscillator (XTAL) that adds significant size and cost to a system. Removing the XTAL enables a true single-chip radio, but an alternate means for calibrating the LO is required. Innovations in both the system architecture and circuits implementation are essential for the design of truly ubiquitous receivers for IoT applications. This research presents two porotypes as back-channel BLE receivers, which have lower power consumption while still being robust in the presents of interference and able to receive back-channel message from BLE compliant transmitters. In addition, the first crystal-less transmitter with symmetric over-the-air clock recovery compliant with the BLE standard using a GFSK-Modulated BLE Packet is presented.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162942/1/abdulalg_1.pd

    Design of software defined radio based testbed for smart healthcare

    Get PDF
    Human Activity Recognition (HAR) help to sense the environment of a human being with an objective to serve a diverse range of human-centric applications in health care, smart-homes and the military. The prevailing detection techniques use ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concern as well. Monitoring human activities of daily living is a possible way of describing the functional and health status of a human. Therefore, human activity recognition (HAR) is one of genuine components in personalized life-care and healthcare systems, especially for the elderly and disabled. Recent advances in wireless technologies have demonstrated that a person’s activity can modulate the wireless signal, and enable the transfer of information from a human to an RF transceiver, even when the person does not carry a transmitter. The aim of this PhD project is to design a novel, non-invasive, easily deployable, flexible and scalable test-bed for detecting human daily activities that can help to assess the general physical health of a person based on Software Defined Radios (SDRs). The proposed system also allows us to modify the power level of transceiver model, change the operating frequency, use self-design antennas and change the number of subcarriers in real-time. The results obtained using USRP based wireless sensing for activities of daily living are highly accurate as compared to off-the-shelf wireless devices each time when activities and experiments are performed. This system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis, and Naıve Bayes are used to evaluate the overall performance of the test-bed and achieved a high accuracy. The K-nearest neighbour outperformed all classifiers, providing an accuracy of 89.73% for activity detection and 91.01% for breathing monitoring. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being

    Ultra Wideband

    Get PDF
    Ultra wideband (UWB) has advanced and merged as a technology, and many more people are aware of the potential for this exciting technology. The current UWB field is changing rapidly with new techniques and ideas where several issues are involved in developing the systems. Among UWB system design, the UWB RF transceiver and UWB antenna are the key components. Recently, a considerable amount of researches has been devoted to the development of the UWB RF transceiver and antenna for its enabling high data transmission rates and low power consumption. Our book attempts to present current and emerging trends in-research and development of UWB systems as well as future expectations
    • 

    corecore