Background ========== Our bodies rely on bi-directional communication between our brains and our limbs for coordinated movement. Descending motor commands and ascending proprioceptive feedback travel via the nerves in our extremities<sup>1</sup>. These proprioceptive signals guide the brain to make minute corrections during movements<sup>2</sup>. Concurrently, the brain develops and refines internal models of the motor commands required to incite desired limb movements<sup>3</sup>. Lack of either signal results in a drastic decrease in coordinated control over the limb<sup>4</sup>. It is no surprise, then, that the lack of proprioceptive feedback is a major limitation for robotic prosthetic arms<sup>5</sup>. A common method of restoring this missing branch of the communication loop is through sensory substitution. Proprioceptive information such as limb position or speed are communicated to the user indirectly using separate sensory channels including vibration<sup>6-10</sup> and audio<sup>11-13</sup> cues. However, because proprioceptive information can be estimated with vision, and because vision is often significantly more precise than the feedback modality, these redundant proprioceptive cues are often ignored in favor of vision. In a previous study, we used psychophysics techniques to test vision for conditions in which speed perception is poor, we showed that joint speed feedback integrates most effectively with vision. Furthermore, we developed an audio feedback paradigm capable of providing prosthetic limb joint speed more precisely than vision<sup>14</sup>. Although we have demonstrated the capacity to augment vision with audio feedback in observational tasks, this augmentation must still be tested during real-time reaching tasks. The purpose of this study is to evaluate joint speed feedback's ability to improve the rate of adaptation during reaching tasks. Subjects will perform center-out reaches requiring coordinated movement of a biological and myoelectric-controlled limb. We will measure trial-by-trial adaptation to self-generated errors during steady-state reaches to determine the strength of the generated internal model. We will also measure the adaptation rate across several trials immediately post-perturbation to understand the speed at which internal models update to changing system parameters. ---------- 1. Jones, L. A. Kinesthetic sensing. Hum. Mach. Haptics, 1–10 (2000). 2. Bilodeau, E. & Bilodeau, I. Motor-skills learning. Annu. Rev. Psychol. 12, 243–280 (1961). 3. Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. An internal model for sensorimotor integration. Sci. Pap. Ed. 269, 1880–1882 (1995). 4. Sainburg, R. L., Ghilardi, M. F., Poizner, H. & Ghez, C. Control of limb dynamics in normal subjects and patients without proprioception. J. Neurophysiol. 73, 820–835 (1995). 5. Cordella, F. et al. Literature review on needs of upper limb prosthesis users. Front. Neurosci. 10, 1–14 (2016). 6. Stanley, A. A. & Kuchenbecker, K. J. Evaluation of tactile feedback methods for wrist rotation guidance. IEEE Trans. Haptics 5, 240–251 (2012). 7. Witteveen, H. J. B., Droog, E. A., Rietman, J. S. & Veltink, P. H. Vibro- and electrotactile user feedback on hand opening for myoelectric forearm prostheses. IEEE Trans. Biomed. Eng. 59, 2219–2226 (2012). 8. Cipriani, C., Segil, J. L., Clemente, F., Richard, R. F. & Edin, B. Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand. Exp. Brain Res. 232, 3421–3429 (2014). 9. Krueger, A. R., Giannoni, P., Shah, V., Casadio, M. & Scheidt, R. A. Supplemental vibrotactile feedback control of stabilization and reaching actions of the arm using limb state and position error encodings. J. Neuroeng. Rehabil. 14, 1–23 (2017). 10. De Nunzio, A. M. et al. Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels. Exp. Brain Res. 235, 2547–2559 (2017). 11. Mirelman, A. et al. Audio-biofeedback training for posture and balance in patients with Parkinson’s disease. J. Neuroeng. Rehabil. 8, 35 (2011). 12. Shehata, A. W., Scheme, E. J. & Sensinger, J. W. Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 1046–1055 (2018). 13. Shehata, A. W., Scheme, E. J. & Sensinger, J. W. Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control. Sci. Rep. 8, 8541 (2018). 14. Earley, E. J., Johnson, R. E., Hargrove, L. J., Sensinger, J. W. Joint Speed Discrimination and Augmentation for Prosthesis Feedback. Sci. Rep. 8, 17752 (2018).