Towards Affordable Home Health Care Devices Using Reconfigurable System-on-Chip Technology

Multi-channel data acquisition (DAQ) is a crucial component in digital instrumentation and control. It typically involves the sampling of multiple analog signals, and converting them into digital formats so that they can be processed either on-board or externally. In either cases, DAQ systems also involve microprocessors, microcontrollers, digital signal processing, and/or storage devices. Multi-channel DAQs, which utilize some sort of processing for simultaneous input channels, are needed in home health care monitoring devices. In this chapter, a low-cost real-time multi-channel Analog Signal Acquisition and Processing (ASAP) system is presented. It is divided into five systems. First, the Multi-channel Analog Signal Acquisition system is used to acquire multi-channel real-time analog signals. Second, Archiving system stores the acquired data into a Flash memory or SDRAM. Third, the Digital Signal Processing Unit performs digital signal processing. Fourth, the Frequency Deviation Monitoring (FREDM) system detects any change in input channels’ frequencies. Finally, the Heterogeneous Maximal Service (HMS) Scheduler is presented to be integrated with FREDM system. In home health care devices, storage is limited and power consumption need to be minimum. Therefore, fixed sampling rate is not the optimal solution for multi-channel human body data acquisition. Hence, heterogeneous sampling rates are identified for each channel, and optimized for best data quality with minimal storage requirement and power consumption. The fidelity of the ASAP system is increased by using reconfigurable chip technology, where flexibility, concurrency and reconfiguration can be achieved in hardware. The proposed ASAP allows for the sampling of up to 32 heterogeneous signals with a single high speed Analog to Digital Converter (ADC) taking into account the performance as well. In the biomedical field, the first step of diagnose a patient is recording biomedical data. Monitoring the vital signs of the patient in acute life-threatening states or being under surgical procedures or anesthesia conditions requires online analysis and immediate visualization. If the immediate visualization is irrelevant, storage of the acquired data is needed. Electrocardiogram (ECG) devices are the most important diagnostic tools for heart patients. Respiratory problems represent one of the main causes of disease in our world. Most of research papers proposed in this field use computer-based devices to acquire signals from the human body. Moreover, there were no scheduling algorithms used. The proposed ASAP 7

[1]  Francisco Cardells-Tormo,et al.  Configurable hardware/software architecture for data acquisition implementation on FPGA , 2005, International Conference on Field Programmable Logic and Applications, 2005..

[2]  Samuel P. Uselton,et al.  Statistically optimized sampling for distributed ray tracing , 1985, SIGGRAPH.

[3]  Mostafa Kaveh,et al.  Midwest Symposium On Circuits And Systems , 1989, Proceedings of the 39th Midwest Symposium on Circuits and Systems.

[4]  Don P. Mitchell,et al.  Generating antialiased images at low sampling densities , 1987, SIGGRAPH.

[5]  E. Engman,et al.  SIR-C/X-SAR as a bridge to soil moisture estimation using current and future operational satellite radars , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[6]  J.J. Liou,et al.  Modeling the soil moisture sensor using an automated data acquisition system , 1991, Conference Record of the 1991 IEEE Industry Applications Society Annual Meeting.

[7]  Carlo H. Séquin,et al.  Tapestry: A Dynamic Mesh-based Display Representation for Interactive Rendering , 2000, Rendering Techniques.

[8]  Michael Meurer,et al.  Enhancement of multichannel A/D conversion by a code division multiplex approach , 2000, 2000 IEEE Sixth International Symposium on Spread Spectrum Techniques and Applications. ISSTA 2000. Proceedings (Cat. No.00TH8536).

[9]  Joseph Y.-T. Leung,et al.  Handbook of Scheduling: Algorithms, Models, and Performance Analysis , 2004 .

[10]  J. Painter,et al.  Antialiased ray tracing by adaptive progressive refinement , 1989, SIGGRAPH.

[11]  Mateu Sbert,et al.  New Contrast Measures for Pixel Supersampling , 2002 .

[12]  Jacques Carlier,et al.  Handbook of Scheduling - Algorithms, Models, and Performance Analysis , 2004 .

[13]  Jiang Jing,et al.  A high-speed multi-channel data acquisition and processing system for coherent radar , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).

[14]  Turner Whitted,et al.  An improved illumination model for shaded display , 1979, CACM.

[15]  Mark A. Z. Dippé,et al.  Antialiasing through stochastic sampling , 1985, SIGGRAPH.

[16]  Mateu Sbert,et al.  Refinement Criteria Based on f-Divergences , 2003, Rendering Techniques.

[17]  Mateu Sbert,et al.  A Novel Adaptive Sampling by Tsallis Entropy , 2007, Computer Graphics, Imaging and Visualisation (CGIV 2007).

[18]  Young Huh,et al.  The design of the multiplexing data acquisition and monitoring system for magnetocardiography (MCG) , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[19]  Ivars Bilinskis.  Digital alias-free signal processing , 2007 .

[20]  Henrik Wann Jensen,et al.  Adaptive Smpling and Bias Estimation in Path Tracing , 1997, Rendering Techniques.

[21]  C.M.B.A. Correia,et al.  A high performance reconfigurable hardware platform for digital pulse processing , 2004, IEEE Transactions on Nuclear Science.

[22]  C.F.M. Loureiro,et al.  Innovative modular high-speed data-acquisition architecture , 2002 .

[23]  Carlos Correia,et al.  Data acquisition and signal processing system based on TMS320C50 and on a IMSA100 processing cascade , 1991, [1991 Proceedings] 6th Mediterranean Electrotechnical Conference.

[24]  B.S. Pimentel,et al.  A FPGA implementation of a DCT-based digital electrocardiographic signal compression device , 2001, Symposium on Integrated Circuits and Systems Design.

[25]  I. Santamaria-Caballero,et al.  Simultaneous sampling by digital phase correction , 1997, IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings.

[26]  Hae-Seung Lee,et al.  16-channel oversampled analog-to-digital converter , 1994 .

[27]  Chien-In Henry Chen,et al.  Dynamic Kernel Function Fast Fourier Transform With Variable Truncation Scheme for Wideband Coarse Frequency Detection , 2009, IEEE Transactions on Instrumentation and Measurement.

[28]  A. Diaz-Sanchez,et al.  Three transistor exponential transconductor , 2005, 48th Midwest Symposium on Circuits and Systems, 2005..

[29]  Davor Petrinovic.  High efficiency multiplexing scheme for multi-channel A/D conversion , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

[30]  Tao Lin,et al.  The implementation of 100MHz data acquisition based on FPGA , 2003, The 3rd IEEE International Workshop on System-on-Chip for Real-Time Applications, 2003. Proceedings..

[31]  Keith D. Paulsen,et al.  A multichannel continuously selectable multifrequency electrical impedance spectroscopy measurement system , 2000, IEEE Transactions on Biomedical Engineering.

[32]  Ivars Bilinskis,et al.  Specifics of Constant Envelope Digital Signals , 2008 .

[33]  M.A. Johnson,et al.  Development of a high-speed multi-channel analog data acquisitioning architecture , 2006, 2006 IEEE Aerospace Conference.

[34]  Khalil Arshak,et al.  Low‐power wireless smart data acquisition system for monitoring pressure in medical application , 2007 .

[35]  Omar S. Elkeelany,et al.  A Low-Cost Stand-Alone Multichannel Data Acquisition, Monitoring, and Archival System With On-Chip Signal Preprocessing , 2011, IEEE Transactions on Instrumentation and Measurement.

[36]  Werner Purgathofer,et al.  A statistical method for adaptive stochastic sampling , 1986, Comput. Graph..

[37]  Peter Brucker,et al.  Scheduling Algorithms , 1995 .

[38]  Ivars Bilinskis,et al.  Modular multi-channel data acquisition systems , 2008, Automatic Control and Computer Sciences.

[39]  T. Backlund,et al.  A flexible measurement system for physiological signals in mobile health care , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[40]  J.M. Dias Pereira,et al.  A DSP Based Prototype for Water Conductivity Measurements , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[41]  Robert Rieger,et al.  An adaptive sampling system for sensor nodes in body area networks. , 2009, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[42]  Hu Bing,et al.  Design of Logic Control for Micro-power A/D with a Serial Interface Using FPGA , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[43]  Mateu Sbert,et al.  Entropy-based Adaptive Sampling , 2003, Graphics Interface.