Flexible circuits for bionic limbs: a high impedance multiplexing front-end for myoelectric control
Source abstract
Abstract
Abstract Bionic limbs require reliable, low-noise and high-comfort interfaces between electrodes and the prosthetic system. This work presents the first fully flexible, wearable myoelectric control system compatible with both dry and wet electrodes. It features a low-noise front-end circuit on foil using amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) Thin-Film Transistors, optimized for multi-electrode sensing. The design includes an autozeroed pre-charging buffer to minimize offset and 1/f noise while maintaining high input impedance (841 MΩ at 50 Hz). The front-end achieves 22 µVrms input noise, < −90 dBc crosstalk, and a 4.6 mV input offset consuming 55.3 µW per channel. EMG signals measured by this AFE were used to drive an elbow musculoskeletal model and predict the resulting human elbow flexion-extension moments, which in turn were used to realize a closed-loop real-time control in a simulated bionic elbow joint, using both dry and wet electrodes. Experiments done with a series of movements show a 20°rms error in angular control.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Muscle activation and electromyography studies · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (31)
Show source-provided references
1. McDonald, C. L., Westcott-McCoy, S., Weaver, M. R., Haagsma, J. & Kartin, D. Global prevalence of traumatic non-fatal limb amputation. Prosthet. Orthot. Int 45, 105–114 (2021).10.1177/0309364620972258 ↗
2. Sartori, M., Durandau, G., Došen, S. & Farina, D. Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling. J. Neural Eng. 15, 066026 (2018).10.1088/1741-2552/aae26b ↗
3. Farina, D. et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nat. Biomed. Eng. 1, 0025 (2017).10.1038/s41551-016-0025 ↗
4. Song, H. et al. Continuous neural control of a bionic limb restores biomimetic gait after amputation. Nat. Med 30, 2010–2019 (2024).10.1038/s41591-024-02994-9 ↗
5. Yadav, D. & Veer, K. Recent trends and challenges of surface electromyography in prosthetic applications. Biomed. Eng. Lett. 13, 353–373 (2023).10.1007/s13534-023-00281-z ↗
6. Londoño-Ramírez, H. et al. Multiplexed Surface Electrode Arrays Based on Metal Oxide Thin-Film Electronics for High-Resolution Cortical Mapping. Adv. Sci. https://doi.org/10.1002/advs.202308507. (2023)10.1002/advs.202308507 ↗
7. Garripoli, C., Abdinia, S., van der Steen, J.-L. J. P., Gelinck, G. H. & Cantatore, E. A Fully Integrated 11.2-mm 2 a-IGZO EMG Front-End Circuit on Flexible Substrate Achieving Up to 41-dB SNR and 29-M$\Omega$ Input Impedance. IEEE Solid State Circuits Lett. 1, 142–145 (2018).10.1109/LSSC.2018.2878184 ↗
8. Genco, E. et al. An EMG Interface Comprising a Flexible a-IGZO Active Electrode Matrix and a 65-nm CMOS IC. IEEE J. Solid-State Circuits https://doi.org/10.1109/JSSC.2023.3274709. (2023)10.1109/JSSC.2023.3274709 ↗
9. van Oosterhout, K., Timmermans, M., Fattori, M. & Cantatore, E. A 250MΩ Input Impedance a-IGZO Front-End for Biosignal Acquisition from Non-Contact Electrodes. In 2024 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE. https://doi.org/10.1109/ISCAS58744.2024.10558081. 2024)10.1109/ISCAS58744.2024.10558081 ↗
10. Raynauld, J.-P. & Laviolette, J. R. The silver-silver chloride electrode: a possible generator of offset voltages and currents. J. Neurosci. Methods 19, 249–255 (1987).10.1016/S0165-0270(87)80009-2 ↗
11. REFA & TMSi - an Artinis Company, the N. www.tmsi.artinis.com.
12. Chi, Y. M., Jung, T. P. & Cauwenberghs, G. Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Rev. Biomed. Eng. 3, 106–119 (2010).10.1109/RBME.2010.2084078 ↗
13. van Oosterhout, K. et al. Brain-Computer Interfaces Using Flexible Electronics: An a-IGZO Front-End for Active ECoG Electrodes. Adv. Sci. https://doi.org/10.1002/advs.202408576. (2024)10.1002/advs.202408576 ↗
14. Hall, D. A., Makinwa, K. A. A. & Jang, T. Quantifying Biomedical Amplifier Efficiency: The noise efficiency factor. IEEE Solid-State Circuits Mag. 15, 28–33 (2023).10.1109/MSSC.2023.3256353 ↗
15. Chandrakumar, H. & Markovic, D. A High Dynamic-Range Neural Recording Chopper Amplifier for Simultaneous Neural Recording and Stimulation. IEEE J. Solid-State Circuits 52, 645–656 (2017).10.1109/JSSC.2016.2645611 ↗
16. Raval, H. N., Tiwari, S. P., Navan, R. R., Mhaisalkar, S. G. & Rao, V. R. Solution-Processed Bootstrapped Organic Inverters Based on P3HT With a High- $k$ Gate Dielectric Material. IEEE Electron Device Lett. 30, 484–486 (2009).10.1109/LED.2009.2016679 ↗
17. Pragmatic Semiconductor. https://www.pragmaticsemi.com/.
18. van Oosterhout, K., Timmermans, M., Fattori, M. & Cantatore, E. Design Trade-offs for Noise Reduction Techniques in Flexible Electronics. In accepted for publication at IFETC 2025.
19. Pau, J. W. L., Xie, S. S. Q. & Pullan, A. J. Neuromuscular Interfacing: Establishing an EMG-Driven Model for the Human Elbow Joint. IEEE Trans. Biomed. Eng. 59, 2586–2593 (2012).10.1109/TBME.2012.2206389 ↗
20. Cavallaro, E. E., Rosen, J., Perry, J. C. & Burns, S. Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm. IEEE Trans. Biomed. Eng. 53, 2387–2396 (2006).10.1109/TBME.2006.880883 ↗
21. Pau, J. W. L., Saini, H., Xie, S. S. Q., Pullan, A. J. & Mallinson, G. An EMG-driven neuromuscular interface for human elbow joint. In 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics 156–161 (IEEE, https://doi.org/10.1109/BIOROB.2010.5627758. 2010).10.1109/BIOROB.2010.5627758 ↗
22. Thelen, D. G. Adjustment of Muscle Mechanics Model Parameters to Simulate Dynamic Contractions in Older Adults. J. Biomech. Eng. 125, 70–77 (2003).10.1115/1.1531112 ↗
23. Malzahn, J., Roozing, W. & Tsagarakis, N. The Compliant Joint Toolbox for MATLAB: An Introduction With Examples. IEEE Robot Autom. Mag. 26, 52–63 (2019).10.1109/MRA.2019.2896360 ↗
24. Albu-Schäffer, A., Ott, C. & Hirzinger, G. A Unified Passivity-based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots. Int J. Rob. Res 26, 23–39 (2007).10.1177/0278364907073776 ↗
25. MedCaT BV. https://medcat.nl/.
26. Moy, T. et al. An EEG Acquisition and Biomarker-Extraction System Using Low-Noise-Amplifier and Compressive-Sensing Circuits Based on Flexible, Thin-Film Electronics. IEEE J. Solid-State Circuits 52, 309–321 (2017).10.1109/JSSC.2016.2598295 ↗
27. Zulqarnain, M. et al. A flexible ECG patch compatible with NFC RF communication. npj Flex. Electron. 4, 13 (2020).10.1038/s41528-020-0077-x ↗
28. Koo, T. K. K. & Mak, A. F. T. Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow. J. Electromyogr. Kinesiol. 15, 12–26 (2005).10.1016/j.jelekin.2004.06.007 ↗
29. Pang, M., Guo, S., Huang, Q., Ishihara, H. & Hirata, H. Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane. J. Med Biol. Eng. 35, 165–177 (2015).10.1007/s40846-015-0033-8 ↗
30. Han, J., Ding, Q., Xiong, A. & Zhao, X. A State-Space EMG Model for the Estimation of Continuous Joint Movements. IEEE Trans. Ind. Electron. 62, 4267–4275 (2015).10.1109/TIE.2014.2387337 ↗
31. Li, K., Zhang, J., Liu, X. & Zhang, M. Estimation of continuous elbow joint movement based on human physiological structure. Biomed. Eng. Online 18, 31 (2019).10.1186/s12938-019-0653-2 ↗