Could it be advantageous to tune the temperature controller during radiofrequency ablation? A feasibility study using theoretical models

Purpose: To assess whether tailoring the Kp and Ki values of a proportional-integral (PI) controller during radiofrequency (RF) cardiac ablation could be advantageous from the point of view of the dynamic behaviour of the controller, in particular, whether control action could be speeded up and larger lesions obtained. Methods: Theoretical models were built and solved by the finite element method. RF cardiac ablations were simulated with temperature controlled at 55 degrees C. Specific PI controllers were implemented with Kp and Ki parameters adapted to cases with different tissue values (specific heat, thermal conductivity and electrical conductivity) electrode-tissue contact characteristics (insertion depth, cooling effect of circulating blood) and electrode characteristics (size, location and arrangement of the temperature sensor in the electrode). Results: The lesion dimensions and T(max) remained almost unchanged when the specific PI controller was used instead of one tuned for the standard case: T(max) varied less than 1.9 degrees C, lesion width less than 0.2 mm, and lesion depth less than 0.3 mm. As expected, we did observe a direct logical relationship between the response time of each controller and the transient value of electrode temperature. Conclusion: The results suggest that a PI controller designed for a standard case (such as that described in this study), could offer benefits under different tissue conditions, electrode-tissue contact, and electrode characteristics.This work received financial support from the Spanish 'Plan Nacional de I+D+I del Ministerio de Ciencia e Innovacion' Grant no. TEC2008-01369/TEC and FEDER Project MTM2010-14909. The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain. The authors alone are responsible for the content and writing of the paperAlba Martínez, J.; Trujillo Guillen, M.; Blasco Giménez, RM.; Berjano Zanón, E. (2011). Could it be advantageous to tune the temperature controller during radiofrequency ablation? A feasibility study using theoretical models. International Journal of Hyperthermia. 27(6):539-548. https://doi.org/10.3109/02656736.2011.586665S539548276Gaita, F., Caponi, D., Pianelli, M., Scaglione, M., Toso, E., Cesarani, F., … Leclercq, J. F. (2010). Radiofrequency Catheter Ablation of Atrial Fibrillation: A Cause of Silent Thromboembolism? Circulation, 122(17), 1667-1673. doi:10.1161/circulationaha.110.937953Anfinsen, O.-G., Aass, H., Kongsgaard, E., Foerster, A., Scott, H., & Amlie, J. P. (1999). Journal of Interventional Cardiac Electrophysiology, 3(4), 343-351. doi:10.1023/a:1009840004782PETERSEN, H. H., CHEN, X., PIETERSEN, A., SVENDSEN, J. H., & HAUNSO, S. (2000). Tissue Temperatures and Lesion Size During Irrigated Tip Catheter Radiofrequency Ablation: An In Vitro Comparison of Temperature-Controlled Irrigated Tip Ablation, Power-Controlled Irrigated Tip Ablation, and Standard Temperature-Controlled Ablation. Pacing and Clinical Electrophysiology, 23(1), 8-17. doi:10.1111/j.1540-8159.2000.tb00644.xTungjitkusolmun, S., Woo, E. J., Cao, H., Tsai, J. Z., Vorperian, V. R., & Webster, J. G. (2000). Thermal—electrical finite element modelling for radio frequency cardiac ablation: Effects of changes in myocardial properties. Medical & Biological Engineering & Computing, 38(5), 562-568. doi:10.1007/bf02345754Lai, Y.-C., Choy, Y. B., Haemmerich, D., Vorperian, V. R., & Webster, J. G. (2004). Lesion Size Estimator of Cardiac Radiofrequency Ablation at Different Common Locations With Different Tip Temperatures. IEEE Transactions on Biomedical Engineering, 51(10), 1859-1864. doi:10.1109/tbme.2004.831529Jain, M. K., & Wolf, P. D. (1999). Temperature-controlled and constant-power radio-frequency ablation: what affects lesion growth? IEEE Transactions on Biomedical Engineering, 46(12), 1405-1412. doi:10.1109/10.804568Panescu, D., Whayne, J. G., Fleischman, S. D., Mirotznik, M. S., Swanson, D. K., & Webster, J. G. (1995). Three-dimensional finite element analysis of current density and temperature distributions during radio-frequency ablation. IEEE Transactions on Biomedical Engineering, 42(9), 879-890. doi:10.1109/10.412649Hong Cao, Vorperian, V. R., Tungjitkusolmun, S., Jan-Zern Tsai, Haemmerich, D., Young Bin Choy, & Webster, J. G. (2001). Flow effect on lesion formation in RF cardiac catheter ablation. IEEE Transactions on Biomedical Engineering, 48(4), 425-433. doi:10.1109/10.915708Tungjitkusolmun, S., Vorperian, V. R., Bhavaraju, N., Cao, H., Tsai, J.-Z., & Webster, J. G. (2001). Guidelines for predicting lesion size at common endocardial locations during radio-frequency ablation. IEEE Transactions on Biomedical Engineering, 48(2), 194-201. doi:10.1109/10.909640Schutt, D., Berjano, E. J., & Haemmerich, D. (2009). Effect of electrode thermal conductivity in cardiac radiofrequency catheter ablation: A computational modeling study. International Journal of Hyperthermia, 25(2), 99-107. doi:10.1080/02656730802563051Langberg, J. J., Calkins, H., el-Atassi, R., Borganelli, M., Leon, A., Kalbfleisch, S. J., & Morady, F. (1992). Temperature monitoring during radiofrequency catheter ablation of accessory pathways. Circulation, 86(5), 1469-1474. doi:10.1161/01.cir.86.5.1469Calkins, H., Prystowsky, E., Carlson, M., Klein, L. S., Saul, J. P., & Gillette, P. (1994). Temperature monitoring during radiofrequency catheter ablation procedures using closed loop control. Atakr Multicenter Investigators Group. Circulation, 90(3), 1279-1286. doi:10.1161/01.cir.90.3.1279Lennox CD, Temperature controlled RF coagulation. Patent number: 5.122.137 Hudson NHEdwards SD, Stern RA, Electrode and associated system using thermally insulated temperature sensing elements. Patent number: US Patent 5,456,682Panescu D, Fleischman SD, Whayne JG, Swanson DK, (EP Technology. Effects of temperature sensor placement on performance of temperature-controlled ablation. IEEE 17th Annual Conference, Engineering in Medicine and Biology Society, Montreal, Canada (1995)BLOUIN, L. T., MARCUS, F. I., & LAMPE, L. (1991). Assessment of Effects of a Radiofrequency Energy Field and Thermistor Location in an Electrode Catheter on the Accuracy of Temperature Measurement. Pacing and Clinical Electrophysiology, 14(5), 807-813. doi:10.1111/j.1540-8159.1991.tb04111.xBerjano, E. J. (2006). BioMedical Engineering OnLine, 5(1), 24. doi:10.1186/1475-925x-5-24Bhavaraju, N. C., Cao, H., Yuan, D. Y., Valvano, J. W., & Webster, J. G. (2001). Measurement of directional thermal properties of biomaterials. IEEE Transactions on Biomedical Engineering, 48(2), 261-267. doi:10.1109/10.909647Hong Cao, Tungjitkusolmun, S., Young Bin Choy, Jang-Zern Tsai, Vorperian, V. R., & Webster, J. G. (2002). Using electrical impedance to predict catheter-endocardial contact during RF cardiac ablation. IEEE Transactions on Biomedical Engineering, 49(3), 247-253. doi:10.1109/10.983459PETERSEN, H. H., & SVENDSEN, J. H. (2003). Can Lesion Size During Radiofrequency Ablation Be Predicted By the Temperature Rise to a Low Power Test Pulse in Vitro? Pacing and Clinical Electrophysiology, 26(8), 1653-1659. doi:10.1046/j.1460-9592.2003.t01-1-00248.xLANGBERG, J. J., LEE, M. A., CHIN, M. C., & ROSENQVIST, M. (1990). Radiofrequency Catheter Ablation: The Effect of Electrode Size on Lesion Volume In Vivo. Pacing and Clinical Electrophysiology, 13(10), 1242-1248. doi:10.1111/j.1540-8159.1990.tb02022.

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

[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

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

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

Improved reception of in-body signals by means of a wearable multi-antenna system

High data-rate wireless communication for in-body human implants is mainly performed in the 402-405 MHz Medical Implant Communication System band and the 2.45 GHz Industrial, Scientific and Medical band. The latter band offers larger bandwidth, enabling high-resolution live video transmission. Although in-body signal attenuation is larger, at least 29 dB more power may be transmitted in this band and the antenna efficiency for compact antennas at 2.45 GHz is also up to 10 times higher. Moreover, at the receive side, one can exploit the large surface provided by a garment by deploying multiple compact highly efficient wearable antennas, capturing the signals transmitted by the implant directly at the body surface, yielding stronger signals and reducing interference. In this paper, we implement a reliable 3.5 Mbps wearable textile multi-antenna system suitable for integration into a jacket worn by a patient, and evaluate its potential to improve the In-to-Out Body wireless link reliability by means of spatial receive diversity in a standardized measurement setup. We derive the optimal distribution and the minimum number of on-body antennas required to ensure signal levels that are large enough for real-time wireless endoscopy-capsule applications, at varying positions and orientations of the implant in the human body