Can a digital twin of the residuum improve bionic solutions for individuals suffering from limb loss?

Abstract

Introduction: Understanding the influence of whole-body activity (e.g., physical training) on the biophysics at organ-, tissue-, and sub-tissue-levels is important for designing optimal physical training/rehabilitation and effective control of assistive devices. The Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE) has developed mature rehabilitation and assistive device technologies underpinned by the Personalized Digital Human (PDH). Method framework: The PDH is a physics-based digital twin of the human (and devices), which includes representation of an individual’s bones, articulations, muscles, and other soft tissues. Coupled with peripheral muscle excitation (e.g., electromyograms), PDH encodes muscle activations specific to the individual (e.g., sensitive to training and disease) and task (e.g. sensitive to control task)1. Examples: To analyze human performance, PDH quantifies mechanical parameters such as net loading to the body, organ (e.g., whole bone; Fig. 2), joint, tissue (e.g., ligament; Fig. 1)2, and sub-tissue (e.g., local strain in tendon; Fig. 1)1. Within a control system3, PDH predicts muscle activations that produce target mechanics (e.g., torque on rehabilitation ergometer), compliment an assistive device (e.g., torque above motor-driven assistance), sensory afferents that augment/simulate sensory experience (e.g., haptics), and safety margins (e.g., critical stresses) (Fig. 2).Fig. 2The PDH used within a control system to interpret sensor data, model the internal states of the human, provide augmented sensory afferents, and perform safety checks on the loading of musculoskeletal tissues3.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Discussion/future directions: Currently, we are developing direct fusion of PDH with prosthesis design to optimize amputee residuum health4. Further, PDH could readily be extended to become the personalized digital soldier, with extensive scope for use in monitoring training loads, improving rehabilitation efficacy, and designing equipment to better interface with humans. Acknowledgements: The PDH is the result of numerous nationally-competitive and industry grants for over 2 decades. References 1Pizzolato C, Shim VB, Lloyd DG, et al. Targeted achilles tendon training and rehabilitation using personalized and real-time multi-scale models of the neuromusculoskeletal system. Front Bioeng Biotechnol 2020; 8:878. https://doi.org/10.3389/fbioe.2020.00878 2Nasseri A, Khataee H, Bryant AL, et al. Modelling the loading mechanics of anterior cruciate ligament. Comp Meth Prog Biomed 2020; 184:105098. https://doi.org/10.1016/j.cmpb.2019.105098 3Pizzolato C, Gunduz MA, Palipana D, et al. Non-invasive approaches to functional recovery after spinal cord injury: therapeutic targets and multimodal device interventions. Exp Neurol 2021; 330:113612. https://doi.org/10.1016/j.expneurol.2021.113612 4Frossard LA, Lloyd DG. The future of bionic limbs. Res Feat 2021; 134:74-77. https://doi.org/10.26904/RF-134-7477