47,950 research outputs found

    Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

    Full text link
    [EN] New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.This work is partially supported by Ministerio de Educacion, Cultura y Deporte (Spain) under grant FPU15/02870. The authors would like to thank Lucas Parra for the Myo device and Janne M. Hahne for discussions about the subject of the paper.Igual, C.; Camacho-García, A.; Bernabeu Soler, EJ.; Igual García, J. (2020). Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control. Applied Sciences. 10(8):1-19. https://doi.org/10.3390/app10082892S119108Esquenazi, A. (2004). Amputation rehabilitation and prosthetic restoration. From surgery to community reintegration. Disability and Rehabilitation, 26(14-15), 831-836. doi:10.1080/09638280410001708850Ziegler-Graham, K., MacKenzie, E. J., Ephraim, P. L., Travison, T. G., & Brookmeyer, R. (2008). Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050. Archives of Physical Medicine and Rehabilitation, 89(3), 422-429. doi:10.1016/j.apmr.2007.11.005Igual, C., Pardo, L. A., Hahne, J., & Igual, J. M. (2019). Myoelectric Control for Upper Limb Prostheses. Electronics, 8(11), 1244. doi:10.3390/electronics8111244Biddiss, E., & Chau, T. (2007). Upper-Limb Prosthetics. American Journal of Physical Medicine & Rehabilitation, 86(12), 977-987. doi:10.1097/phm.0b013e3181587f6cBiddiss, E. A., & Chau, T. T. (2007). Upper limb prosthesis use and abandonment. Prosthetics & Orthotics International, 31(3), 236-257. doi:10.1080/03093640600994581Davidson, J. (2002). A survey of the satisfaction of upper limb amputees with their prostheses, their lifestyles, and their abilities. Journal of Hand Therapy, 15(1), 62-70. doi:10.1053/hanthe.2002.v15.01562Datta, D., Selvarajah, K., & Davey, N. (2004). Functional outcome of patients with proximal upper limb deficiency–acquired and congenital. Clinical Rehabilitation, 18(2), 172-177. doi:10.1191/0269215504cr716oaVujaklija, I., Farina, D., & Aszmann, O. (2016). New developments in prosthetic arm systems. Orthopedic Research and Reviews, Volume 8, 31-39. doi:10.2147/orr.s71468Scheme, E. J., Englehart, K. B., & Hudgins, B. S. (2011). Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions. IEEE Transactions on Biomedical Engineering, 58(6), 1698-1705. doi:10.1109/tbme.2011.2113182Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848-854. doi:10.1109/tbme.2003.813539Parker, P., Englehart, K., & Hudgins, B. (2006). Myoelectric signal processing for control of powered limb prostheses. Journal of Electromyography and Kinesiology, 16(6), 541-548. doi:10.1016/j.jelekin.2006.08.006Fougner, A., Stavdahl, Ø., Kyberd, P. J., Losier, Y. G., & Parker, P. A. (2012). Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(5), 663-677. doi:10.1109/tnsre.2012.2196711Resnik, L., Huang, H. (Helen), Winslow, A., Crouch, D. L., Zhang, F., & Wolk, N. (2018). Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control. Journal of NeuroEngineering and Rehabilitation, 15(1). doi:10.1186/s12984-018-0361-3Sartori, M., Durandau, G., Došen, S., & Farina, D. (2018). Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling. Journal of Neural Engineering, 15(6), 066026. doi:10.1088/1741-2552/aae26bVelliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature, 453(7198), 1098-1101. doi:10.1038/nature06996Amsuess, S., Vujaklija, I., Goebel, P., Roche, A. D., Graimann, B., Aszmann, O. C., & Farina, D. (2016). Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(7), 744-753. doi:10.1109/tnsre.2015.2454240Kuiken, T. A., Miller, L. A., Turner, K., & Hargrove, L. J. (2016). A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis. IEEE Journal of Translational Engineering in Health and Medicine, 4, 1-8. doi:10.1109/jtehm.2016.2616123Phinyomark, A., N. Khushaba, R., & Scheme, E. (2018). Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors, 18(5), 1615. doi:10.3390/s18051615Asghari Oskoei, M., & Hu, H. (2007). Myoelectric control systems—A survey. Biomedical Signal Processing and Control, 2(4), 275-294. doi:10.1016/j.bspc.2007.07.009Spanias, J. A., Perreault, E. J., & Hargrove, L. J. (2016). Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(2), 226-234. doi:10.1109/tnsre.2015.2413393Castellini, C., & van der Smagt, P. (2008). Surface EMG in advanced hand prosthetics. Biological Cybernetics, 100(1), 35-47. doi:10.1007/s00422-008-0278-1Ameri, A., Akhaee, M. A., Scheme, E., & Englehart, K. (2019). Regression convolutional neural network for improved simultaneous EMG control. Journal of Neural Engineering, 16(3), 036015. doi:10.1088/1741-2552/ab0e2eHahne, J. M., Biebmann, F., Jiang, N., Rehbaum, H., Farina, D., Meinecke, F. C., … Parra, L. C. (2014). Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(2), 269-279. doi:10.1109/tnsre.2014.2305520Ameri, A., Scheme, E. J., Kamavuako, E. N., Englehart, K. B., & Parker, P. A. (2014). Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms. IEEE Transactions on Biomedical Engineering, 61(2), 279-287. doi:10.1109/tbme.2013.2281595Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., … Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164-171. doi:10.1038/nature04970Interface Prostheses With Classifier-Feedback-Based User Training. (2017). IEEE Transactions on Biomedical Engineering, 64(11), 2575-2583. doi:10.1109/tbme.2016.2641584Thomas, N., Ung, G., McGarvey, C., & Brown, J. D. (2019). Comparison of vibrotactile and joint-torque feedback in a myoelectric upper-limb prosthesis. Journal of NeuroEngineering and Rehabilitation, 16(1). doi:10.1186/s12984-019-0545-5Guémann, M., Bouvier, S., Halgand, C., Borrini, L., Paclet, F., Lapeyre, E., … de Rugy, A. (2018). Sensory and motor parameter estimation for elbow myoelectric control with vibrotactile feedback. Annals of Physical and Rehabilitation Medicine, 61, e467. doi:10.1016/j.rehab.2018.05.1090Markovic, M., Schweisfurth, M. A., Engels, L. F., Farina, D., & Dosen, S. (2018). Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping. Journal of NeuroEngineering and Rehabilitation, 15(1). doi:10.1186/s12984-018-0422-7Pasquina, P. F., Perry, B. N., Miller, M. E., Ling, G. S. F., & Tsao, J. W. (2015). Recent advances in bioelectric prostheses. Neurology: Clinical Practice, 5(2), 164-170. doi:10.1212/cpj.0000000000000132NING, J., & Dario, F. (2014). Myoelectric control of upper limb prosthesis: current status, challenges and recent advances. Frontiers in Neuroengineering, 7. doi:10.3389/conf.fneng.2014.11.00004Lendaro, E., Mastinu, E., Håkansson, B., & Ortiz-Catalan, M. (2017). Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain. Frontiers in Neurology, 8. doi:10.3389/fneur.2017.00470Mastinu, E., Ortiz-Catalan, M., & Hakansson, B. (2015). Analog front-ends comparison in the way of a portable, low-power and low-cost EMG controller based on pattern recognition. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2015.7318805BECK, T. W., HOUSH, T. J., CRAMER, J. T., MALEK, M. H., MIELKE, M., HENDRIX, R., & WEIR, J. P. (2008). Electrode Shift and Normalization Reduce the Innervation Zone’s Influence on EMG. Medicine & Science in Sports & Exercise, 40(7), 1314-1322. doi:10.1249/mss.0b013e31816c4822Pasquina, P. F., Evangelista, M., Carvalho, A. J., Lockhart, J., Griffin, S., Nanos, G., … Hankin, D. (2015). First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand. Journal of Neuroscience Methods, 244, 85-93. doi:10.1016/j.jneumeth.2014.07.016Fougner, A., Scheme, E., Chan, A. D. C., Englehart, K., & Stavdahl, Ø. (2011). Resolving the Limb Position Effect in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), 644-651. doi:10.1109/tnsre.2011.2163529Hwang, H.-J., Hahne, J. M., & Müller, K.-R. (2017). Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing. PLOS ONE, 12(11), e0186318. doi:10.1371/journal.pone.0186318Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2011). The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift. IEEE Transactions on Biomedical Engineering, 58(9), 2537-2544. doi:10.1109/tbme.2011.2159216Prahm, C., Schulz, A., Paaben, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., … Aszmann, O. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/tnsre.2019.2907200Cipriani, C., Sassu, R., Controzzi, M., & Carrozza, M. C. (2011). Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6090468Amsuss, S., Paredes, L. P., Rudigkeit, N., Graimann, B., Herrmann, M. J., & Farina, D. (2013). Long term stability of surface EMG pattern classification for prosthetic control. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2013.6610327Scheme, E., & Englehart, K. (2011). Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development, 48(6), 643. doi:10.1682/jrrd.2010.09.0177Scheme, E., Fougner, A., Stavdahl, Ø., Chan, A. D. C., & Englehart, K. (2010). Examining the adverse effects of limb position on pattern recognition based myoelectric control. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. doi:10.1109/iembs.2010.5627638Dohnalek, P., Gajdos, P., & Peterek, T. (2013). Human activity recognition on raw sensor data via sparse approximation. 2013 36th International Conference on Telecommunications and Signal Processing (TSP). doi:10.1109/tsp.2013.6614027Marasco, P. D., Hebert, J. S., Sensinger, J. W., Shell, C. E., Schofield, J. S., Thumser, Z. C., … Orzell, B. M. (2018). Illusory movement perception improves motor control for prosthetic hands. Science Translational Medicine, 10(432). doi:10.1126/scitranslmed.aao6990Mastinu, E., Doguet, P., Botquin, Y., Hakansson, B., & Ortiz-Catalan, M. (2017). Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant. IEEE Transactions on Biomedical Circuits and Systems, 11(4), 867-877. doi:10.1109/tbcas.2017.2694710Igual, C., Igual, J., Hahne, J. M., & Parra, L. C. (2019). Adaptive Auto-Regressive Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(2), 314-322. doi:10.1109/tnsre.2019.2894464Huang, Y., Englehart, K. B., Hudgins, B., & Chan, A. D. C. (2005). A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses. IEEE Transactions on Biomedical Engineering, 52(11), 1801-1811. doi:10.1109/tbme.2005.856295Hahne, J. M., Dahne, S., Hwang, H.-J., Muller, K.-R., & Parra, L. C. (2015). Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(4), 618-627. doi:10.1109/tnsre.2015.240113

    Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications

    Full text link
    [EN] The standard of in-body communications is limited to the use of narrowband systems. These systems are far from the high data rate connections achieved by other wireless telecommunication services today in force. The UWB frequency band has been proposed as a possible candidate for future in-body networks. However, the attenuation of body tissues at gigahertz frequencies could be a serious drawback. Experimental measurements for channel modeling are not easy to carry out, while the use of humans is practically forbidden. Sophisticated simulation tools could provide inaccurate results since they are not able to reproduce all the in-body channel conditions. Chemical solutions known as phantoms could provide a fair approximation of body tissues¿ behavior. In this work, the Time Reversal technique is assessed to increase the channel performance of in-body communications. For this task, a large volume of experimental measurements is performed at the low part of UWB spectrum (3.1-5.1 GHz) by using a highly accurate phantom-based measurement setup. This experimental setup emulates an in-body to in-body scenario, where all the nodes are implanted inside the body. Moreover, the in-body channel characteristics such as the path loss, the correlation in transmission and reception, and the reciprocity of the channel are assessed and discussed.This work was supported by the Programa de Ayudas de Investigacion y Desarrollo (PAID-01-16) from Universitat Politecnica de Valencia and by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Andreu-Estellés, C.; Garcia-Pardo, C.; Castelló-Palacios, S.; Cardona Marcet, N. (2018). Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications. Wireless Communications and Mobile Computing (Online). (8927107):1-12. https://doi.org/10.1155/2018/8927107S1128927107Fireman, Z. (2003). Diagnosing small bowel Crohn’s disease with wireless capsule endoscopy. Gut, 52(3), 390-392. doi:10.1136/gut.52.3.390Burri, H., & Senouf, D. (2009). Remote monitoring and follow-up of pacemakers and implantable cardioverter defibrillators. Europace, 11(6), 701-709. doi:10.1093/europace/eup110Scanlon, W. G., Burns, B., & Evans, N. E. (2000). Radiowave propagation from a tissue-implanted source at 418 MHz and 916.5 MHz. IEEE Transactions on Biomedical Engineering, 47(4), 527-534. doi:10.1109/10.828152Chavez-Santiago, R., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., Vermeeren, G., Joseph, W., … Cardona, N. (2015). Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2015.2418757Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2010). Ultra-wideband pulse-based data communications for medical implants. IET Communications, 4(15), 1889. doi:10.1049/iet-com.2009.0692Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2011). Ultra-wideband statistical propagation channel model for implant sensors in the human chest. IET Microwaves, Antennas & Propagation, 5(15), 1805. doi:10.1049/iet-map.2010.0537Kurup, D., Scarpello, M., Vermeeren, G., Joseph, W., Dhaenens, K., Axisa, F., … Vanfleteren, J. (2011). In-body path loss models for implants in heterogeneous human tissues using implantable slot dipole conformal flexible antennas. EURASIP Journal on Wireless Communications and Networking, 2011(1). doi:10.1186/1687-1499-2011-51Floor, P. A., Chavez-Santiago, R., Brovoll, S., Aardal, O., Bergsland, J., Grymyr, O.-J. H. N., … Balasingham, I. (2015). In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals. IEEE Journal of Biomedical and Health Informatics, 19(3), 938-948. doi:10.1109/jbhi.2015.2417805Shimizu, Y., Anzai, D., Chavez-Santiago, R., Floor, P. A., Balasingham, I., & Wang, J. (2017). Performance Evaluation of an Ultra-Wideband Transmit Diversity in a Living Animal Experiment. IEEE Transactions on Microwave Theory and Techniques, 65(7), 2596-2606. doi:10.1109/tmtt.2017.2669039Anzai, D., Katsu, K., Chavez-Santiago, R., Wang, Q., Plettemeier, D., Wang, J., & Balasingham, I. (2014). Experimental Evaluation of Implant UWB-IR Transmission With Living Animal for Body Area Networks. IEEE Transactions on Microwave Theory and Techniques, 62(1), 183-192. doi:10.1109/tmtt.2013.2291542Chou, C.-K., Chen, G.-W., Guy, A. W., & Luk, K. H. (1984). Formulas for preparing phantom muscle tissue at various radiofrequencies. Bioelectromagnetics, 5(4), 435-441. doi:10.1002/bem.2250050408Cheung, A. Y., & Koopman, D. W. (1976). Experimental Development of Simulated Biomaterials for Dosimetry Studies of Hazardous Microwave Radiation (Short Papers). IEEE Transactions on Microwave Theory and Techniques, 24(10), 669-673. doi:10.1109/tmtt.1976.1128936YAMAMOTO, H., ZHOU, J., & KOBAYASHI, T. (2008). Ultra Wideband Electromagnetic Phantoms for Antennas and Propagation Studies. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E91-A(11), 3173-3182. doi:10.1093/ietfec/e91-a.11.3173Lazebnik, M., Madsen, E. L., Frank, G. R., & Hagness, S. C. (2005). Tissue-mimicking phantom materials for narrowband and ultrawideband microwave applications. Physics in Medicine and Biology, 50(18), 4245-4258. doi:10.1088/0031-9155/50/18/001Yilmaz, T., Foster, R., & Hao, Y. (2014). Broadband Tissue Mimicking Phantoms and a Patch Resonator for Evaluating Noninvasive Monitoring of Blood Glucose Levels. IEEE Transactions on Antennas and Propagation, 62(6), 3064-3075. doi:10.1109/tap.2014.2313139Gezici, S., Zhi Tian, Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., & Sahinoglu, Z. (2005). Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Processing Magazine, 22(4), 70-84. doi:10.1109/msp.2005.1458289Marinova, M., Thielens, A., Tanghe, E., Vallozzi, L., Vermeeren, G., Joseph, W., … Martens, L. (2015). Diversity Performance of Off-Body MB-OFDM UWB-MIMO. IEEE Transactions on Antennas and Propagation, 63(7), 3187-3197. doi:10.1109/tap.2015.2422353SHI, J., ANZAI, D., & WANG, J. (2012). Channel Modeling and Performance Analysis of Diversity Reception for Implant UWB Wireless Link. IEICE Transactions on Communications, E95.B(10), 3197-3205. doi:10.1587/transcom.e95.b.3197Pajusco, P., & Pagani, P. (2009). On the Use of Uniform Circular Arrays for Characterizing UWB Time Reversal. IEEE Transactions on Antennas and Propagation, 57(1), 102-109. doi:10.1109/tap.2008.2009715Chavez-Santiago, R., Sayrafian-Pour, K., Khaleghi, A., Takizawa, K., Wang, J., Balasingham, I., & Li, H.-B. (2013). Propagation models for IEEE 802.15.6 standardization of implant communication in body area networks. IEEE Communications Magazine, 51(8), 80-87. doi:10.1109/mcom.2013.6576343Andreu, C., Castello-Palacios, S., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., & Cardona, N. (2016). Spatial In-Body Channel Characterization Using an Accurate UWB Phantom. IEEE Transactions on Microwave Theory and Techniques, 64(11), 3995-4002. doi:10.1109/tmtt.2016.2609409Pahlavan, K., & Levesque, A. H. (2005). Wireless Information Networks. doi:10.1002/0471738646Qiu, R. C., Zhou, C., Guo, N., & Zhang, J. Q. (2006). Time Reversal With MISO for Ultrawideband Communications: Experimental Results. IEEE Antennas and Wireless Propagation Letters, 5, 269-273. doi:10.1109/lawp.2006.875888Ando, H., Takizawa, K., Yoshida, T., Matsushita, K., Hirata, M., & Suzuki, T. (2016). Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces. IEEE Transactions on Biomedical Circuits and Systems, 10(6), 1068-1078. doi:10.1109/tbcas.2016.251452

    A computational model for real-time calculation of electric field due to transcranial magnetic stimulation in clinics

    Get PDF
    The aim of this paper is to propose an approach for an accurate and fast (real-time) computation of the electric field induced inside the whole brain volume during a transcranial magnetic stimulation (TMS) procedure. The numerical solution implements the admittance method for a discretized realistic brain model derived from Magnetic Resonance Imaging (MRI). Results are in a good agreement with those obtained using commercial codes and require much less computational time. An integration of the developed codewith neuronavigation toolswill permit real-time evaluation of the stimulated brain regions during the TMSdelivery, thus improving the efficacy of clinical applications

    3-D printed UWB microwave bodyscope for biomedical measurements

    Get PDF
    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this letter, a three-dimensional (3-D) printed compact ultrawideband (UWB) extended gap ridge horn (EGRH) antenna designed to be used for biological measurements of the human body is described. The operational frequency covers the microwave band of interest from 0.5 to 3.0 GHz (for an S 11 under -7 dB). The 3-D printed EGRH antenna is dielectrically matched to the permittivity of the human body, and because of its compactness, it can be visualized as a general-purpose microwave probe among the RF biomedical community. The probe has proven its capability as a pass-through propagation sensor for different parts of the human body and as a sensor detecting a 1 cm diameter object placed inside an artificial head phantom.Peer ReviewedPostprint (author's final draft

    Photoacoustic Imaging using Combination of Eigenspace-Based Minimum Variance and Delay-Multiply-and-Sum Beamformers: Simulation Study

    Full text link
    Delay and Sum (DAS), as the most common beamforming algorithm in Photoacoustic Imaging (PAI), having a simple implementation, results in a low-quality image. Delay Multiply and Sum (DMAS) was introduced to improve the quality of the reconstructed images using DAS. However, the resolution improvement is now well enough compared to high resolution adaptive reconstruction methods such as Eigenspace- Based Minimum Variance (EIBMV). We proposed to integrate the EIBMV inside the DMAS formula by replacing the existing DAS algebra inside the expansion of DMAS, called EIBMV-DMAS. It is shown that EIBMV-DMAS outperforms DMAS in the terms of levels of sidelobes and width of mainlobe significantly. For instance, at the depth of 35 mm, EIBMV-DMAS outperforms DMAS and EIBMV in the term of sidelobes for about 108 dB, 98 dB and 44 dB compared to DAS, DMAS, and EIBMV, respectively. The quantitative comparison has been conducted using Full-Width-Half-Maximum (FWHM) and Signal-to-Noise Ratio (SNR), and it was shown that EIBMV-DMAS reduces the FWHM about 1.65 mm and improves the SNR about 15 dB, compared to DMAS.Comment: Submitted in 24th Iranian Conference on Biomedical Engineering (ICBME 2017
    corecore