Illusory movement perception improves motor control for prosthetic hands

Abstract

To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement’s progress. This largely non-conscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. Here we report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands. Vibrating the muscles used for prosthetic control via a neural-machine interface produced the illusory perception of complex grip movements. Within minutes, three amputees integrated this kinesthetic feedback and improved movement control. Combining intent, kinesthesia, and vision instilled participants with a sense of agency over the robotic movements. This feedback approach for closed-loop control opens a pathway to seamless integration of minds and machines.

Task‐dependent responses to muscle vibration during reaching

“…As we did not assess conscious perception in our experiment, we use the term “illusory” to denote a difference between the estimated and actual limb state, ignoring whether or not the estimated state results in a conscious percept. Vibration of multiple muscles induces complex multijoint illusory movement (Thyrion & Roll, ) that can be used as feedback to functionally improve control of prostheses even in re‐innervated muscles (Marasco et al., ). During voluntary movement, sensitivity to vibration is reduced in shortening muscles, and information about limb position and movement comes primarily from the lengthening muscle (Capaday & Cooke, , ; Inglis & Frank, ; Inglis, Frank, & Inglis, ).…”

“…Analyses were performed with Python 3.6 (Python Software Foundation, RRID:SCR_008394, https://python.org), including packages h5py (The HDF Group, 1997-2017, numpy (RRID:SCR_008633, van der Walt, Colbert, & Varoquaux, 2011), pandas (McKinney, 2010), matplotlib (RRID:SCR_008624, Hunter, 2007), and scipy (RRID:SCR_008058, Jones, Oliphant, & Peterson, 2001-2018. Statistical tests were performed with R 3.4 (RRID:SCR_001905, R Core Team, 2017) via the rpy package, https://rpy2.bitbucket.io/.…”

Experimental Analysis of a Novel, Magnetic-Driven Tactile Feedback Device

“…The current methods that deliver tactile stimulation are limited, and most adopt the vibro-tactile techniques [10][11][12]. While they have proven to be quite successful in sending a stimulus to the mechanoreceptors, they are limited in stimulus variation and provide a stimulus that is not very common in real-world interactions, as they send signals through controlled frequencies [13].…”

Joint Speed Discrimination and Augmentation For Prosthesis Feedback

“…Several feedback methods have been proposed over the past decades, including vibrotactile [7][8][9][10][11] , electrotactile 8 , skin stretch 7 , audio [12][13][14] and visual 15 modalities 16,17 . More complex feedback modalities like peripheral nerve stimulation 18 and vibration-induced illusory kinesthesia 19 have also been introduced to great effect. Sensory feedback typically falls into two categories: tactile feedback of grasping force 9,11,18 , and proprioceptive feedback of limb movement 7,8,10,[12][13][14][15]19 .…”

“…More complex feedback modalities like peripheral nerve stimulation 18 and vibration-induced illusory kinesthesia 19 have also been introduced to great effect. Sensory feedback typically falls into two categories: tactile feedback of grasping force 9,11,18 , and proprioceptive feedback of limb movement 7,8,10,[12][13][14][15]19 . Vision is capable of estimating grasping force similarly to tactile feedback 20 , though several grasping force feedback studies have still shown significant benefit to prosthesis control with vision present.…”