Pedro Silva Girão

Smart Sensors Network for Air Quality Monitoring Applications

This paper presents a network for indoor and outdoor air quality monitoring. Each node is installed in a different room and includes tin dioxide sensor arrays connected to an acquisition and control system. The nodes are hardwired or wirelessly connected to a central monitoring unit. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor influence quantities, i.e., temperature and humidity, are also measured. Advanced processing based on multiple-input-single-output neural networks is implemented at the network sensing nodes to obtain temperature and humidity compensated gas concentration values. Anomalous operation of the network sensing nodes and power consumption are also discussed.

Theory and Developments in an Unobtrusive Cardiovascular System Representation: Ballistocardiography

Due to recent technological improvements, namely in the field of piezoelectric sensors, ballistocardiography – an almost forgotten physiological measurement – is now being object of a renewed scientific interest. Transcending the initial purposes of its development, ballistocardiography has revealed itself to be a useful informative signal about the cardiovascular system status, since it is a non-intrusive technique which is able to assess the body’s vibrations due to its cardiac, and respiratory physiological signatures. Apart from representing the outcome of the electrical stimulus to the myocardium – which may be obtained by electrocardiography – the ballistocardiograph has additional advantages, as it can be embedded in objects of common use, such as a bed or a chair. Moreover, it enables measurements without the presence of medical staff, factor which avoids the stress caused by medical examinations and reduces the patient’s involuntary psychophysiological responses. Given these attributes, and the crescent number of systems developed in recent years, it is therefore pertinent to revise all the information available on the ballistocardiogram’s physiological interpretation, its typical waveform information, its features and distortions, as well as the state of the art in device implementations.

Physiological Parameters Measurement Based on Wheelchair Embedded Sensors and Advanced Signal Processing

This paper presents a multisensing system with wireless communication capabilities embedded on a smart wheelchair that can measure physiological parameters such as heart rate and respiratory rate in an unobtrusive way. Ballistocardiography (BCG) sensors and a three-axis inertial microelectromechanical system accelerometer are embedded on the seat or in the backrest of the wheelchair and the acquired data are transmitted by Wi-Fi to a laptop computer for advanced data processing and logging. In addition, a 3-D accelerometer with ZigBee communication capability is used to extract information about the users posture. Considering the static and dynamic use of the wheelchair, an extended set of measurements for different utilization scenarios was analyzed. An important part of this paper is focused on BCG noise and artifacts removal and heart rate and respiratory rate accurate estimation from BCG signal using wavelet-based filtering and independent component analysis algorithms. A study on wavelet-based filtering considering different types of mother wavelets and different levels of decomposition was also carried out. In the future, other signals will also be acquired to improve the system capabilities and flexibility.

An overview and a contribution to the optical measurement of linear displacement

The present work is a contribution to the field of linear displacement measurements by optical means. For that purpose, a brief overview of some existing solutions is presented and two systems for axial linear displacement measurement based on light intensity detection are introduced. The systems have redundancy and were designed with the purpose of achieving identification and automatic correction of errors arising from inadvertent angular variations between the sensor and the light beam positions.

An FFT-based method to evaluate and compensate gain and offset errors of interleaved ADC systems

Interleaved analog-digital converter (ADC) systems can be used to increase the sampling rate for a given ADC implementation technique. In theory, the maximum sampling rate that can be achieved is limited only by the bandwidth and the practical limits related to the power and space of integrated circuits. In this paper, a solution to increase the sampling rate of a digitizing system based on interleaved ADCs is presented. An error analysis, which takes into consideration offset and gain errors of the different ADC channels, is performed in order to quantify the effect of such errors in the systems performance. A software method based on the fast Fourier transform is presented for offset and gain error compensation of interleaved ADC associations. Numerical simulations and experimental results are used to validate the theory and the proposed compensation algorithm.

Vital Signs Monitoring System Based on EMFi Sensors and Wavelet Analysis

The paper presents a monitoring system for non-obtrusive measuring of vital signs such as respiration and heart activity. Two sensing channels including electromechanical film sensors (EMFi sensor) tapped on an office chair are used to obtain the ballistocardiographyc (BCG) signals from human subjects. The conditioning circuits include amplification stages (charge amplifier, programmable gain amplifier) and 20 Hz low-pass and 50 Hz notch active filters used to improve the signal-to-noise ratio (SNR). The gain control and analog-to-digital conversion is performed using a multifunction I/O device USB compatible connected to a laptop PC. The digital processing of the acquired signals is an important part of the work. It includes digital filtering, based on Stationary Wavelet Transform, and signal decomposition, based on Discrete Wavelet Transform, in order to extract the respiration and heart rate based on BCG signal analyze. A practical approach concerning the dependence of the used wavelet type on vital signs estimation accuracy is reported. The results on respiration rate and heart rate using BCG signal and the implemented algorithms are compared with the values of these parameters measured through ECG and spirometry analysis.

Self organizing maps application in a remote water quality monitoring system

This work was developed in the context of a system for remote water quality monitoring based on a wireless local area network (WLAN) and includes a Kohonen self-organizing map (K-SOM) implementation in order to perform sensor data validation and reconstruction and sensor failure and pollution event detections. Simulation and experimental results are presented.

Microwave FMCW Doppler radar implementation for in-house pervasive health care system

In recent years, the research in the area of ubiquitous healthcare has intensified. There are many technological advances regarding the development of unobtrusive sensors for cardiac and respiratory activity, but the current scenario is still far away from an everyday life fulfilled with ubiquitous healthcare systems. In this paper, it is described the usage of 24GHz microwave FMCW (frequency modulated continuous wave) Doppler radar (MDR) as one of the main components of a pervasive biomedical system that is part of an assistive environment for the people with less mobility or people with long term health condition. As parts of the present work, in this paper are mentioned the design and implementation of an assistive environment based on a MDR sensor, an experimental study concerning the microwave Doppler radar characteristics and remote sensing of heart rate and breath rate, based on acquisition and processing of the signals delivered by the used radar.

Minimising temperature drift errors of conditioning circuits using artificial neural networks

Temperature drift errors are a problem that affect the accuracy of measurement systems. When small amplitude signals from transducers are considered and environmental conditions of conditioning circuits exhibit a large temperature range, the temperature drift errors have a real impact in systems accuracy. In this paper, a solution to overcome the problem of temperature drift errors of conditioning circuits is proposed. As an example, a thermocouple-based temperature measurement system is considered, and the stability of its conditioning circuit (AD595) is analyzed in two cases: with and without temperature drift error compensation. An Artificial Neural Network (ANN) is used for data optimization and a Virtual Instrument, using GPIB instrumentation, is used to collect experimental data. Final results show a significant improvement in the accuracy of the system when the proposed temperature drift error compensation technique is applied to compensate errors caused by temperature variations.

Implementation of compressed sensing in telecardiology sensor networks

Mobile solutions for patient cardiac monitoring are viewed with growing interest, and improvements on current implementations are frequently reported, with wireless, and in particular, wearable devices promising to achieve ubiquity. However, due to unavoidable power consumption limitations, the amount of data acquired, processed, and transmitted needs to be diminished, which is counterproductive, regarding the quality of the information produced. Compressed sensing implementation in wireless sensor networks (WSNs) promises to bring gains not only in power savings to the devices, but also with minor impact in signal quality. Several cardiac signals have a sparse representation in some wavelet transformations. The compressed sensing paradigm states that signals can be recovered from a few projections into another basis, incoherent with the first. This paper evaluates the compressed sensing paradigm impact in a cardiac monitoring WSN, discussing the implications in data reliability, energy management, and the improvements accomplished by in-network processing.