Cover
Vol. 15 No. 2 (2024)

Published: November 15, 2024

Pages: 131-154

Review Paper

Smart Prosthetics Controller Types: Review

Abstract

Advanced prosthetics are a crucial aspect of rehabilitation technology and are receiving increased attention globally. Approximately 2 million people require prosthetic limbs, presenting opportunities for enhancing their quality of life. State-of-the-art technologies such as realistic arms and myoelectric prostheses are gaining popularity. Progress in sensor technology, artificial intelligence, and materials has driven the field forward. Various types of controllers, including direct, pattern recognition, and proportional-derivative, have been developed. Integration of material science, computer science, artificial intelligence, and neurology has facilitated controller advancements. Techniques like targeted muscle reinnervation and Osseo integrated prostheses offer improved surgical options. Gesture recognition technologies and intelligent sensors are enhancing hand control. Future advancements will involve machine learning, artificial intelligence, and sensing techniques, while ethical concerns must be addressed. Advanced myoelectric prostheses, also known as myocontrolled or lower-limb micromod investigative prostheses, have a patient acceptance rate of 75% to 80%. However, while these methods offer advantages, there are also drawbacks. Integrating different types of controllers for these smart prostheses and enhancing the overall device's strength and robustness will have a significant impact. This discussion focuses on various types of smart prosthetic controllers, dividing muscle activity into extracellular myoelectric potential and EEG signals

References

  1. Reinhard, J., Urban, P., Bell, S., Carpenter, D., & Sagoo, M. S. (2024). Automatic data-driven design and 3D printing of custom ocular prostheses. Nature Communications, 15(1), 1360.
  2. Spaulding, S. Kheng, S. Kapp, & C. Harte, "Education in prosthetic and orthotic training", Prosthetics and Orthotics International, vol. 44, no. 6, p. 416-426, 2020. https://doi.org/10.1177/0309364620968644
  3. Mereu, F. Leone, C. Gentile, F. Cordella, E. Gruppioni, & L. Zollo, "Control strategies and performance assessment of upper-limb tmr prostheses: a review", Sensors, vol. 21, no. 6, p. 1953, 2021. https://doi.org/10.3390/s21061953
  4. Karczewski, A. Dingle, & S. Poore, "The need to work arm in arm: calling for collaboration in delivering neuroprosthetic limb replacements", Frontiers in Neurorobotics, vol. 15, 2021. https://doi.org/10.3389/fnbot.2021.711028
  5. Yildiz, A. Shin, & K. Kaufman, "Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review", Journal of Neuroengineering and Rehabilitation, vol. 17, no. 1, 2020. https://doi.org/10.1186/s12984-020-00667-5
  6. Bates, "Technological advances in prosthesis design and rehabilitation following upper extremity limb loss", Current Reviews in Musculoskeletal Medicine, vol. 13, no. 4, p. 485-493, 2020. https://doi.org/10.1007/s12178-020-09656-6
  7. Zhu. Et.al. Intelligent Biosensors for Healthcare 5.0. In Federated Learning and AI for Healthcare 5.0 (pp. 61-77). IGI Global. 2024
  8. Nizamis, A. Athanasiou, S. Almpani, C. Dimitrousis, & A. Astaras, "Converging robotic technologies in targeted neural rehabilitation: a review of emerging solutions and challenges", Sensors, vol. 21, no. 6, p. 2084, 2021. https://doi.org/10.3390/s21062084
  9. Li, P. Shi, & H. Yu, "Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future", Frontiers in Neuroscience, vol. 15, 2021. https://doi.org/10.3389/fnins.2021.621885
  10. Tan, K. Liang, Z. Ngo, C. Dube, & C. Lim, "Application of 3d bioprinting technologies to the management and treatment of diabetic foot ulcers", Biomedicines, vol. 8, no. 10, p. 441, 2020. https://doi.org/10.3390/biomedicines8100441
  11. Farina, I. Vujaklija, R. Brånemark, A. Bull, H. Dietl, B. Gramlichet al., "Toward higher-performance bionic limbs for wider clinical use", Nature Biomedical Engineering, vol. 7, no. 4, p. 473-485, 2021. https://doi.org/10.1038/s41551-021-00732-x
  12. Li, K., Zhang, J., Wang, L., Zhang, M., Li, J., & Bao, S. (2020). A review of the key technologies for sEMG-based human-robot interaction systems. Biomedical Signal Processing and Control, 62, 102074.
  13. Xia, C. Chen, X. Sheng and X. Zhu, "On Detecting the Invariant Neural Drive to Muscles during Repeated Hand Motions: A Preliminary Study," 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Shanghai, China, 2021, pp. 192-196, doi: 10.1109/M2VIP49856.2021.9665089.
  14. Dantas. Et.al. Deep learning movement intent decoders trained with dataset aggregation for prosthetic limb control. IEEE Transactions on Biomedical Engineering, 66(11), 3192-3203. 2019.
  15. Lukyanenko. Stable, simultaneous and proportional 4-DoF prosthetic hand control via synergy-inspired linear interpolation: a case series. Journal of NeuroEngineering and Rehabilitation, 18, 1-15. 2019
  16. Lin. Et.qi. Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization. Journal of neural engineering, 15(2), 026017. 2018
  17. Hu, X., Zeng, H., Chen, D., Zhu, J., & Song, A. (2020, May). Real-time continuous hand motion myoelectric decoding by automated data labeling. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6951-6957). IEEE.
  18. Guo. Et.al. Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals. Journal of Neural Engineering, 18(2), 026027. 2021
  19. V. Godoy. On emg based dexterous robotic telemanipulation: Assessing machine learning techniques, feature extraction methods, and shared control schemes. IEEE Access, 10, 99661-99674. 2022
  20. R. Zangene. Et.al. An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running. Biomedical Signal Processing and Control, 86, 105103. 2023
  21. Piazza. Evaluation of a simultaneous myoelectric control strategy for a multi-DoF transradial prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(10), 2286-2295. 2020.
  22. Chen. Et.al. A review of myoelectric control for prosthetic hand manipulation. Biomimetics, 8(3), 328. 2023.
  23. Shin. et. al. A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. Journal of neurophysiology, 101(1), 387-401. 2009
  24. Stapornchaisit, et.al. Finger angle estimation from array EMG system using linear regression model with independent component analysis. Frontiers in neurorobotics, 13, 75. 2019
  25. L. Crouch, H. Huang, Lumped-parameter electromyogram-driven musculoskeletal hand model: A potential platform for real-time prosthesis control. Journal of biomechanics, 49(16), 3901-3907. 2016.
  26. Zhao. et.al. A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements. Journal of Neural Engineering, 19(1), 016027. 2022
  27. Mereu, F. Leone, C. Gentile, F. Cordella, E. Gruppioni, & L. Zollo, "Control strategies and performance assessment of upper-limb tmr prostheses: a review", Sensors, vol. 21, no. 6, p. 1953, 2021. https://doi.org/10.3390/s21061953
  28. Lee. Et.ql. Recent Advances in Smart Tactile Sensory Systems with Brain‐Inspired Neural Networks. Advanced Intelligent Systems, 2300631. 2024.
  29. Pyo, J. Lee, K. Bae, S. Sim, & J. Kim, "Recent progress in flexible tactile sensors for human‐interactive systems: from sensors to advanced applications", Advanced Materials, vol. 33, no. 47, 2021. https://doi.org/10.1002/adma.202005902
  30. Kunrath, S. Gupta, F. Lorusso, A. Scarano, & S. Noumbissi, "Oral tissue interactions and cellular response to zirconia implant-prosthetic components: a critical review", Materials, vol. 14, no. 11, p. 2825, 2021. https://doi.org/10.3390/ma14112825
  31. Nizamis, A. Athanasiou, S. Almpani, C. Dimitrousis, & A. Astaras, "Converging robotic technologies in targeted neural rehabilitation: a review of emerging solutions and challenges", Sensors, vol. 21, no. 6, p. 2084, 2021. https://doi.org/10.3390/s21062084
  32. Pradhan, D. Bharti, S. Chakravarty, S. Ray, V. Voinova, А. Бонарцевet al., "Internet of things and robotics in transforming current-day healthcare services", Journal of Healthcare Engineering, vol. 2021, p. 1-15, 2021. https://doi.org/10.1155/2021/9999504
  33. Cervino, M. Cicciù, A. Herford, A. Germanà, & L. Fiorillo, "Biological and chemo-physical features of denture resins", Materials, vol. 13, no. 15, p. 3350, 2020. https://doi.org/10.3390/ma13153350
  34. Boretti. A perspective on 3D printing in the medical field. Annals of 3D Printed Medicine, 13, 100138. 2024.
  35. Datta, , & R. Barua. 3D Printing in Modern Healthcare: An Overview of Materials, Methods, Applications, and Challenges. Emerging Technologies for Health Literacy and Medical Practice, 132-152. 2024
  36. Li and K. Kataoka, "Chemo-physical strategies to advance the in vivo functionality of targeted nanomedicine: the next generation", Journal of the American Chemical Society, vol. 143, no. 2, p. 538-559, 2020. https://doi.org/10.1021/jacs.0c09029
  37. Baumann, C. O'Neill, M. Owens, S. Weber, S. Sivan, R. D’Amicoet al., "Fda public workshop: orthopaedic sensing, measuring, and advanced reporting technology (smart) devices", Journal of Orthopaedic Research®, vol. 39, no. 1, p. 22-29, 2020. https://doi.org/10.1002/jor.24833
  38. Pradhan, D. Bharti, S. Chakravarty, S. Ray, V. Voinova, А. Бонарцевet al., "Internet of things and robotics in transforming current-day healthcare services", Journal of Healthcare Engineering, vol. 2021, p. 1-15, 2021. https://doi.org/10.1155/2021/9999504
  39. Mereu, F. Leone, C. Gentile, F. Cordella, E. Gruppioni, & L. Zollo, "Control strategies and performance assessment of upper-limb tmr prostheses: a review", Sensors, vol. 21, no. 6, p. 1953, 2021. https://doi.org/10.3390/s21061953
  40. Li, P. Shi, & H. Yu, "Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future", Frontiers in Neuroscience, vol. 15, 2021. https://doi.org/10.3389/fnins.2021.621885
  41. Pinteala, M. Abadie, & R. Rusu, "Smart supra- and macro-molecular tools for biomedical applications", Materials, vol. 13, no. 15, p. 3343, 2020. https://doi.org/10.3390/ma13153343
  42. Liu, J. Liu, X. Cui, X. Wang, L. Zhang, & P. Tang, "Recent advances on magnetic sensitive hydrogels in tissue engineering", Frontiers in Chemistry, vol. 8, 2020. https://doi.org/10.3389/fchem.2020.00124
  43. Nizamis, A. Athanasiou, S. Almpani, C. Dimitrousis, & A. Astaras, "Converging robotic technologies in targeted neural rehabilitation: a review of emerging solutions and challenges", Sensors, vol. 21, no. 6, p. 2084, 2021. https://doi.org/10.3390/s21062084
  44. Mereu, F. Leone, C. Gentile, F. Cordella, E. Gruppioni, & L. Zollo, "Control strategies and performance assessment of upper-limb tmr prostheses: a review", Sensors, vol. 21, no. 6, p. 1953, 2021. https://doi.org/10.3390/s21061953
  45. Belkacem, N. Jamil, J. Palmer, S. Ouhbi, & C. Chen, "Brain computer interfaces for improving the quality of life of older adults and elderly patients", Frontiers in Neuroscience, vol. 14, 2020. https://doi.org/10.3389/fnins.2020.00692
  46. Tariq, & S. B. Ismail. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. Plos one, 19(3), e0294289. 2024.
  47. Ko, F. Asplund, & B. Zeybek, "A scoping review of pressure measurements in prosthetic sockets of transfemoral amputees during ambulation: key considerations for sensor design", Sensors, vol. 21, no. 15, p. 5016, 2021. https://doi.org/10.3390/s21155016
  48. Li, P. Shi, & H. Yu, "Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future", Frontiers in Neuroscience, vol. 15, 2021. https://doi.org/10.3389/fnins.2021.621885
  49. Baumann, C. O'Neill, M. Owens, S. Weber, S. Sivan, R. D’Amicoet al., "Fda public workshop: orthopaedic sensing, measuring, and advanced reporting technology (smart) devices", Journal of Orthopaedic Research®, vol. 39, no. 1, p. 22-29, 2020. https://doi.org/10.1002/jor.24833
  50. Cruz, M. Ross, S. Powell, & M. Woodruff, "Advancements in soft-tissue prosthetics part a: the art of imitating life", Frontiers in Bioengineering and Biotechnology, vol. 8, 2020. https://doi.org/10.3389/fbioe.2020.00121
  51. Vu, D. Dong, H. Cao, T. Verstraten, D. Lefeber, B. Vanderborghtet al., "A review of gait phase detection algorithms for lower limb prostheses", Sensors, vol. 20, no. 14, p. 3972, 2020. https://doi.org/10.3390/s20143972
  52. Kunrath, S. Gupta, F. Lorusso, A. Scarano, & S. Noumbissi, "Oral tissue interactions and cellular response to zirconia implant-prosthetic components: a critical review", Materials, vol. 14, no. 11, p. 2825, 2021. https://doi.org/10.3390/ma14112825
  53. Panchal, N. Kent, A. Knox, & L. Harris, "Microfluidics in haemostasis: a review", Molecules, vol. 25, no. 4, p. 833, 2020. https://doi.org/10.3390/molecules25040833
  54. Tagde, S. Tagde, T. Bhattacharya, P. Tagde, H. Chopra, R. Akteret al., "Blockchain and artificial intelligence technology in e-health", Environmental Science and Pollution Research, vol. 28, no. 38, p. 52810-52831, 2021. https://doi.org/10.1007/s11356-021-16223-0
  55. Pradhan, D. Bharti, S. Chakravarty, S. Ray, V. Voinova, А. Бонарцевet al., "Internet of things and robotics in transforming current-day healthcare services", Journal of Healthcare Engineering, vol. 2021, p. 1-15, 2021. https://doi.org/10.1155/2021/9999504
  56. Powell, R. Cruz, M. Ross, & M. Woodruff, "Past, present, and future of soft‐tissue prosthetics: advanced polymers and advanced manufacturing", Advanced Materials, vol. 32, no. 42, 2020. https://doi.org/10.1002/adma.202001122
  57. Safari, "Lower limb prosthetic interfaces", Prosthetics and Orthotics International, vol. 44, no. 6, p. 384-401, 2020. https://doi.org/10.1177/0309364620969226
  58. Liao, C. Cheng, C. Chen, Y. Wang, H. Chiu, C. Penget al., "Systematic review of diagnostic sensors for intra-abdominal pressure monitoring", Sensors, vol. 21, no. 14, p. 4824, 2021. https://doi.org/10.3390/s21144824
  59. Khan, M. Mehran, Z. Haq, Z. Ullah, S. Naqvi, M. Ihsanet al., "Applications of artificial intelligence in covid-19 pandemic: a comprehensive review", Expert Systems With Applications, vol. 185, p. 115695, 2021. https://doi.org/10.1016/j.eswa.2021.115695
  60. Iqbal, Z. Javed, H. Sadia, I. Qureshi, A. Irshad, R. Ahmedet al., "Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future", Cancer Cell International, vol. 21, no. 1, 2021. https://doi.org/10.1186/s12935-021-01981-1
  61. Nizamis, A. Athanasiou, S. Almpani, C. Dimitrousis, & A. Astaras, "Converging robotic technologies in targeted neural rehabilitation: a review of emerging solutions and challenges", Sensors, vol. 21, no. 6, p. 2084, 2021. https://doi.org/10.3390/s21062084
  62. Ciolacu, R. Nicu, & F. Ciolacu, "Cellulose-based hydrogels as sustained drug-delivery systems", Materials, vol. 13, no. 22, p. 5270, 2020. https://doi.org/10.3390/ma13225270
  63. Kapadia, M. Desai, & R. Parikh, "Fractures in the framework: limitations of classification systems inpsychiatry", Dialogues in Clinical Neuroscience, vol. 22, no. 1, p. 17-26, 2020.
  64. Li, H., Hang, Z., Song, K., Han, F., Liu, Z., & Tian, Q. (2024). Flexible and stretchable implantable devices for peripheral neuromuscular electrophysiology. Nanoscale.
  65. Ko, F. Asplund, & B. Zeybek, "A scoping review of pressure measurements in prosthetic sockets of transfemoral amputees during ambulation: key considerations for sensor design", Sensors, vol. 21, no. 15, p. 5016, 2021.
  66. Fleming, N. Stafford, S. Huang, X. Hu, D. Ferris, & H. Huang, "Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions", Journal of Neural Engineering, vol. 18, no. 4, p. 041004, 2021.
  67. Wu, G. Yang, K. Zhu, S. Liu, W. Guo, Z. Jianget al., "Materials, devices, and systems of on‐skin electrodes for electrophysiological monitoring and human–machine interfaces", Advanced Science, vol. 8, no. 2, 2020. https://doi.org/10.1002/advs.202001938
  68. Hewitt, D. Smith, J. Heckman, & P. Pasquina, "Covid‐19: a catalyst for change in virtual health care utilization for persons with limb loss", Pm&r, vol. 13, no. 6, p. 637-646, 2021.
  69. Zhang. et.al. Machine learning‐reinforced noninvasive biosensors for healthcare. Advanced Healthcare Materials, 10(17), 2100734. 2021.
  70. Amirthalingam, G. Paidi, K. Alshowaikh, A. Jayarathna, D. Salibindla, K. Karpinska-Leydieret al., "Virtual reality intervention to help improve motor function in patients undergoing rehabilitation for cerebral palsy, parkinson’s disease, or stroke: a systematic review of randomized controlled trials", Cureus, 2021.
  71. Sensinger and S. Došen, "A review of sensory feedback in upper-limb prostheses from the perspective of human motor control", Frontiers in Neuroscience, vol. 14, 2020.
  72. Ko, F. Asplund, & B. Zeybek, "A scoping review of pressure measurements in prosthetic sockets of transfemoral amputees during ambulation: key considerations for sensor design", Sensors, vol. 21, no. 15, p. 5016, 2021.
  73. Zhan, K. Yin, J. Xiong, Z. He, & S. Wu, "Augmented reality and virtual reality displays: perspectives and challenges", Iscience, vol. 23, no. 8, p. 101397, 2020.
  74. Mohamad, M. Ahmad, Y. Benferdia, A. Shapi'i, & M. Bajuri, "An overview of ontologies in virtual reality-based training for healthcare domain", Frontiers in Medicine, vol. 8, 2021.
  75. Ellery, "Tutorial review of bio-inspired approaches to robotic manipulation for space debris salvage", Biomimetics, vol. 5, no. 2, p. 19, 2020.
  76. Baud, A. Manzoori, A. Ijspeert, & M. Bouri, "Review of control strategies for lower-limb exoskeletons to assist gait", Journal of Neuroengineering and Rehabilitation, vol. 18, no. 1, 2021.
  77. Zhou, L. Ren, C. You, S. Niu, Z. Han, & L. Ren, "Bio‐inspired soft grippers based on impactive gripping", Advanced Science, vol. 8, no. 9, 2021.
  78. Moro, J. Birt, Z. Štromberga, C. Phelps, J. Clark, P. Glasziouet al., "Virtual and augmented reality enhancements to medical and science student physiology and anatomy test performance: a systematic review and meta‐analysis", Anatomical Sciences Education, vol. 14, no. 3, p. 368-376, 2021.
  79. Barteit, L. Lanfermann, T. Bärnighausen, F. Neuhann, & C. Beiersmann, "Augmented, mixed, and virtual reality-based head-mounted devices for medical education: systematic review", Jmir Serious Games, vol. 9, no. 3, p. e29080, 2021. https://doi.org/10.2196/29080
  80. Jain, R. Carneiro, A. Vasilica, W. Chia, A. Souza, J. Wellingtonet al., "The impact of the covid-19 pandemic on global neurosurgical education: a systematic review", Neurosurgical Review, vol. 45, no. 2, p. 1101-1110, 2021. https://doi.org/10.1007/s10143-021-01664-5
  81. Georgiev, I. Georgieva, Z. Gong, V. Nanjappan, & G. Georgiev, "Virtual reality for neurorehabilitation and cognitive enhancement", Brain Sciences, vol. 11, no. 2, p. 221, 2021. https://doi.org/10.3390/brainsci11020221
  82. Simon, D. Bolton, N. Kennedy, S. Soekadar, & K. Ruddy, "Challenges and opportunities for the future of brain-computer interface in neurorehabilitation", Frontiers in Neuroscience, vol. 15, 2021. https://doi.org/10.3389/fnins.2021.699428
  83. Yang, R. Li, H. Li, K. Xu, Y. Shi, Q. Wanget al., "Exploring the use of brain-computer interfaces in stroke neurorehabilitation", Biomed Research International, vol. 2021, p. 1-11, 2021. https://doi.org/10.1155/2021/9967348
  84. Zhang, J. Wang, T. Liu, Y. Luo, X. Loh, & X. Chen, "Machine learning‐reinforced noninvasive biosensors for healthcare", Advanced Healthcare Materials, vol. 10, no. 17, 2021. https://doi.org/10.1002/adhm.202100734
  85. Sarker, "Machine learning: algorithms, real-world applications and research directions", Sn Computer Science, vol. 2, no. 3, 2021. https://doi.org/10.1007/s42979-021-00592-x
  86. Nurcahyani and J. Lee, "Role of machine learning in resource allocation strategy over vehicular networks: a survey", Sensors, vol. 21, no. 19, p. 6542, 2021. https://doi.org/10.3390/s21196542
  87. Handelzalts, G. Ballardini, C. Avraham, M. Pagano, M. Casadio, & I. Nisky, "Integrating tactile feedback technologies into home-based telerehabilitation: opportunities and challenges in light of covid-19 pandemic", Frontiers in Neurorobotics, vol. 15, 2021. https://doi.org/10.3389/fnbot.2021.617636
  88. Halperin, S. Israeli‐Korn, S. Yakubovich, S. Hassin‐Baer, & A. Zaidel, "Self‐motion perception in parkinson's disease", European Journal of Neuroscience, vol. 53, no. 7, p. 2376-2387, 2020. https://doi.org/10.1111/ejn.14716
  89. Rivas. Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs. Electronics, 13(6), 1013. 2024
  90. A. Ramadan. Deciphering the Mind: Advanced Neuroimaging Techniques and Cognitive State Decoding in Brain-Computer Interfaces. PLOMS Review Journal, 1(1). 2024.
  91. Fitzgerald. Et. Al. Moving a missing hand: children born with below elbow deficiency can enact hand grasp patterns with their residual muscles. Journal of NeuroEngineering and Rehabilitation, 21(1), 13. 2024
  92. Segura. Et. L. Upper Limb Prostheses by the Level of Amputation: A Systematic Review. Prosthesis, 6(2), 277-300. 2024.
  93. Yang, e.al. Muscle redistribution technique for expressing motion intention in patients with wrist-level amputation. Journal of Hand Surgery (European Volume), 49(1), 100-102. 2024
  94. G. Kulkarni, et. al. Overcoming challenges and innovations in orthopedic prosthesis design: an interdisciplinary perspective. Biomedical Materials & Devices, 2(1), 58-69. 2024
  95. Khang, Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry. IGI Global. 2024
  96. Mennella, U. Maniscalco, Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon. 2024
  97. J. Park, L. Lee, Soft Sensors and Actuators for Wearable Human–Machine Interfaces. Chemical Reviews. 2024.