An Efficient IAKF Approach for Indoor Positioning Drift Correction.

In this study, an indoor positioning shift correction architecture was developed with an improved adaptive Kalman filter (IAKF) algorithm for the people interference condition. Indoor positioning systems (IPSs) use ultra-wideband (UWB) communication technology. Triangulation positioning algorithms are generally employed for determining the position of a target. However, environmental communication factors and different network topologies produce localization drift errors in IPSs. Therefore, the drift error of real-time positioning points under various environmental factors and the correction of the localization drift error are discussed. For localization drift error, four algorithms were simulated and analyzed: movement average (MA), least square (LS), Kalman filter (KF), and IAKF. Finally, the IAKF algorithm was implemented and verified on the UWB indoor positioning system. The measurement results showed that the drift errors improved by 60% and 74.15% in environments with and without surrounding crowds, respectively. Thus, the coordinates of real-time positioning points are closer to those of actual targets.

Low-power wireless ECG acquisition and classification system for body sensor networks.

A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on-off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc-air batteries as the power supply; the power consumption is 586.5 µW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-µm standard CMOS process.

Implementation of a wireless ECG acquisition SoC for IEEE 802.15.4 (ZigBee) applications.

This paper presents a wireless biosignal acquisition system-on-a-chip (WBSA-SoC) specialized for electrocardiogram (ECG) monitoring. The proposed system consists of three subsystems, namely, 1) the ECG acquisition node, 2) the protocol for standard IEEE 802.15.4 ZigBee system, and 3) the RF transmitter circuits. The ZigBee protocol is adopted for wireless communication to achieve high integration, applicability, and portability. A fully integrated CMOS RF front end containing a quadrature voltage-controlled oscillator and a 2.4-GHz low-IF (i.e., zero-IF) transmitter is employed to transmit ECG signals through wireless communication. The low-power WBSA-SoC is implemented by the TSMC 0.18-µm standard CMOS process. An ARM-based displayer with FPGA demodulation and an RF receiver with analog-to-digital mixed-mode circuits are constructed as verification platform to demonstrate the wireless ECG acquisition system. Measurement results on the human body show that the proposed SoC can effectively acquire ECG signals.