IJEM Vol. 15, No. 5, 8 Oct. 2025
Cover page and Table of Contents: PDF (size: 990KB)
SA-I Afferents, Indentation, Cadence Virtuoso
The development of upper limb prostheses poses a significant challenge in providing amputees with sensory feedback. This paper presents a novel approach by proposing a biomimetic circuit specifically designed to replicate the behavior of slowly adapting (SA-I) afferents, which are responsible for encoding sustained indentation and offering crucial sensory feedback. The circuit has been meticulously designed and simulated using Cadence Virtuoso software, a powerful tool for circuit design and optimization. To validate the functionality and performance of the biomimetic circuit, a grid of mechanoreceptors is simulated and tested, providing realistic inputs for the circuit. The circuit successfully emulates the response of SA-I afferents to sustained indentation, exhibiting a slowly adapting discharge that linearly correlates with the depth of indentation. This ability to replicate the natural behavior of SA-I afferents represents a significant advancement in the field of providing sensory feedback for upper limb prostheses.
The biomimetic circuit holds great promise in addressing the crucial need for sensory feedback in upper limb prosthetics. By integrating this circuit into upper limb prostheses, amputees can experience more intuitive and realistic sensations during interactions with their environment. The replication of SA-I afferent behavior provides users with vital information about the magnitude and duration of applied forces, enhancing their overall perception and control of the prosthesis.
The findings of this study contribute to the ongoing progress in the field of prosthetics, particularly in the development of more sophisticated and advanced upper limb prostheses. The successful implementation and simulation of the biomimetic circuit demonstrate its potential as a viable solution for providing amputees with enhanced sensory feedback, ultimately improving their quality of life and reintegrating them into daily activities more seamlessly. The new approach emphasizes the development of a biomimetic circuit tailored to replicate SA-I afferent behavior. The proposal addresses the challenge of providing sensory feedback in upper limb prostheses. The study utilizes Cadence Virtuoso software for precise design, layout, and simulation, offering a practical solution for realistic sensory feedback. By accurately emulating the response of SA-I afferents to sustained indentation, the circuit holds the potential to significantly enhance amputees' quality of life and integration into daily activities. The proposed circuit contributes to the advancement of upper limb prosthetics and represents a significant leap forward in achieving more intuitive and authentic sensory experiences for prosthesis users.
Chaitra H., Punith Kumar M. B., "Design and Simulation of Biomimetic Controller", International Journal of Engineering and Manufacturing (IJEM), Vol.15, No.5, pp. 1-17, 2025. DOI:10.5815/ijem.2025.05.01
[1]M. M. Iskarous and N. V. Thakor, “E-skins: Biomimetic sensing and encoding for upper limb prostheses,” Proc. IEEE, vol. 107, no. 10, pp. 2052–2064, Oct. 2019.
[2]B. Lee, F. J. Attenello, C. Y. Liu, M. P. McLoughlin, and M. L. J. Apuzzo, “Recapitulating flesh with silicon and steel: Advancements in upper extremity robotic prosthetics,” World Neurosurg., vol. 81, nos. 5–6, pp. 730–741, May/Jun. 2014.
[3]M. Zhu and C. J. Rozell, “Modeling inhibitory interneurons in efficient sensory coding models,” PLOS Comput. Biol., vol. 11, no. 7, Jul. 2015, Art. no. e1004353.
[4]M. Knibestöl, “Stimulus-response functions of slowly adapting mechanoreceptors in the human glabrous skin area,” J. Physiol., vol. 245, no. 1, pp. 63–80, Feb. 1975.
[5]B. P. Delhaye, K. H. Long, and S. J. Bensmaia, “Neural basis of touch and proprioception in primate cortex,” Comprehensive Physiol., vol. 8, no. 4, pp. 1575–1602, 2011.
[6]S.-H. Woo, E. A. Lumpkin, and A. Patapoutian, “Merkel cells and neurons keep in touch,” Trends Cell Biol., vol. 25, no. 2, pp. 74–81, Feb. 2015.
[7]M. Nakatani, S. Maksimovic, Y. Baba, and E. A. Lumpkin, “Mechanotransduction in epidermal Merkel cells,” Pflügers Arch., Eur. J. Physiol., vol. 467, no. 1, pp. 101–108, Jan. 2015.
[8]R. S. Johansson, “Tactile sensibility in the human hand: Receptive field characteristics of mechanoreceptive units in the glabrous skin area,” J. Physiol., vol. 281, no. 1, pp. 101–125, Aug. 1978.
[9]J. A. Pruszynski and R. S. Johansson, “Edge-orientation processing in first-order tactile neurons,” Nature Neurosci., vol. 17, no. 10, pp. 1404–1409, 2014.
[10]R. S. Johansson, U. Landstrom, and R. Lundstrom, “Responses of mechanoreceptive afferent units in the glabrous skin of the human hand to sinusoidal skin displacements,” Brain Res., vol. 244, no. 1, pp. 17–25, 1982.
[11]T. A. de Oliveira, A.-M. Cretu, and E. Petriu, “Multimodal bio-inspired tactile sensing module for surface characterization,” Sensors, vol. 17, no. 6, p. 1187, May 2017.
[12]M. M. Iskarous, H. H. Nguyen, L. E. Osborn, J. L. Betthauser, and N. V. Thakor, “Unsupervised learning and adaptive classification of neuromorphic tactile encoding of textures,” in Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), Oct. 2018, pp. 1–4.
[13]R. Madler, A. Kundu, A. Fahmy, E. Patrick, R. Bashirullah, and N. Maghari, “Multi-path integrate and fire circuit for determination of tactile sensations in a prosthetic limb,” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), May 2019, pp. 1–4.
[14]H. P. Saal, B. P. Delhaye, B. C. Rayhaun, and S. J. Bensmaia, “Simulating tactile signals from the whole hand with millisecond precision,” Proc. Nat. Acad. Sci. USA, vol. 114, no. 28, pp. E5693–E5702, Jul. 2017.
[15]C. M. Oddo et al., “Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans,” Elife, vol. 5, Mar. 2016, Art. no. e09148.
[16]K. E. Friedl, A. R. Voelker, A. Peer, and C. Eliasmith, “Human-inspired neurorobotic system for classifying surface textures by touch,” IEEE Robot. Autom. Lett., vol. 1, no. 1, pp. 516–523, Jan. 2016.
[17]T. Birkoben, H. Winterfeld, S. Fichtner, A. Petraru, and H. Kohlstedt, “A spiking and adapting tactile sensor for neuromorphic applications,” Sci. Rep., vol. 10, no. 1, pp. 1–11, Dec. 2020.
[18]H. Liu, D. Guo, and F. Sun, “Object recognition using tactile measurements: Kernel sparse coding methods,” IEEE Trans. Instrum. Meas., vol. 65, no. 3, pp. 656–665, Mar. 2016
[19]M. Pearson et al., “A hardware based implementation of a tactile sensory system for neuromorphic signal processing applications,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., vol. 4, May 2006, pp. 1–4.
[20]W. W. Lee, J. Cabibihan, and N. V. Thakor, “Bio-mimetic strategies for tactile sensing,” in Proc. IEEE SENSORS, Nov. 2013, pp. 1–4.
[21]N. Salimi-Nezhad, M. Amiri, E. Falotico, and C. Laschi, “A digital hardware realization for spiking model of cutaneous mechanoreceptor,” Frontiers Neurosci., vol. 12, p. 322, Jan. 2018.
[22]N. Salimi-Nezhad, E. Ilbeigi, M. Amiri, E. Falotico, and C. Laschi, “A digital hardware system for spiking network of tactile afferents,” Frontiers Neurosci., vol. 13, p. 1330, Jan. 2020.
[23]F. Semiconductor, “CMOS Schmitt trigger—A uniquely versatile design component,” Fairchild Semicond. Appl. Note, vol. 140, pp. 1–8, Jan. 1975.
[24]L. A. P. Melek, A. L. da Silva, M. C. Schneider, and C. Galup-Montoro, “Analysis and design of the classical CMOS Schmitt trigger in subthreshold operation,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 64, no. 4, pp. 869–878, Apr. 2017.
[25]U. B. Rongala, A. Spanne, A. Mazzoni, F. Bengtsson, C. M. Oddo, and H. Jörntell, “Intracellular dynamics in cuneate nucleus neurons support self-stabilizing learning of generalizable tactile representations,” Frontiers Cellular Neurosci., vol. 12, p. 210, Jul. 2018.
[26]H. Rezaee-Dehsorkh, N. Ravanshad, R. Lotfi, K. Mafinezhad, and A. M. Sodagar, “Analysis and design of tunable amplifiers for implantable neural recording applications,” IEEE J. Emerg. Sel. Topics Circuits Syst, vol. 1, no. 4, pp. 546–556, Dec. 2011.
[27]H. Kassiri, K. Abdelhalim, and R. Genov, “Low-distortion superGOhm subthreshold-MOS resistors for CMOS neural amplifiers,” in Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), Oct. 2013, pp. 270–273.
[28]B. D. Sahoo, “Ring oscillator based sub-1 V leaky integrate-andfire neuron circuit,” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), May 2017, pp. 1–4.
[29]T. P. Minka, “Automatic choice of dimensionality for PCA,” in Proc. Adv. Neural Inf. Process. Syst., 2001, pp. 598–604.
[30]A. Parvizi-Fard, N. Salimi-Nezhad, M. Amiri, E. Falotico, and C. Laschi, “Sharpness recognition based on synergy between bioinspired nociceptors and tactile mechanoreceptors,” Sci. Rep., vol. 11, no. 1, p. 2109, Dec. 2021