A Compact and Lightweight Rehabilitative Exoskeleton to Restore Grasping Functions for People with Hand Paralysis

Abstract

Millions of individuals suffer from upper extremity paralysis caused by neurological disorders including stroke, traumatic brain injury, or spinal cord injury. Robotic hand exoskeletons can substitute the missing motor control and help restore the functions in daily operations. However, most of the hand exoskeletons are bulky, stationary, and cumbersome to use. We have modified a recent existing design (Tenoexo) to prototype a motorized, lightweight, fully wearable rehabilitative hand exoskeleton by combining rigid parts with a soft mechanism capable of producing various grasps needed for the execution of daily tasks. Mechanical evaluation of our exoskeleton showed that it can produce fingertip force up to 8 N and can cover 91.5° of range of motion in just 3 s. We further tested the performance of the developed robotic exoskeleton in two quadriplegics with chronic hand paralysis and observed immediate success on independent grasping of different daily objects. The results suggested that our exoskeleton is a viable option for hand function assistance, allowing patients to regain lost finger control for everyday activities.

Soft robotics and functional electrical stimulation advances for restoring hand function in people with SCI: a narrative review, clinical guidelines and future directions

“…Two studies [64,65] presented devices based on a three-layered sliding spring mechanism, adapted from the concept originally presented in [79]. In TLSS, two spring blades are layered, with rigid elements connecting them.…”

“…In [64], the sliding springs are actuated by Bowdencables, that transmit the torque of electric motors storage in a backpack. In [65], the electric motors are directly attached to the TLSS. Both hand exoskeletons based on TLSS are mounted on the dorsal side of the hand, which is advantageous for somatosensation aspects.…”

“…Pressure (or force) sensors were used by other two devices [50,52]: in Zhou's work [52], a state machine controller determined the hand pose based on the signals of pressure sensors placed on the palm and fingertips; and in the SEM Glove [50] proportional pulling force in the flexion direction is applied according to what is sensed from fingertips' pressure sensors. Within the final SR collection, other user intent detection methods were identified: wrist extension, based on the signals of a bending sensor positioned on the dorsal side of the wrist [54]; myoelectric (EMG) signals, recorded from either the ipsilateral biceps or the upper trapezius muscle [67], or other muscles [64,65]; voice command, in which the user is able to combine keywords to determine the action to be performed, creating a customized dictionary [66]. In [40], Kim and colleagues used a first-person-view camera to record spatial/temporal information and used it to detect user intent.…”

“…Regarding finger assistance, the pneumatic gloves [51][52][53] and the devices based on TLSS mechanism [64,65], actuate all the four fingers and thumb, although the three-fingered grasp was the most frequent configuration observed in the final collection. The combination of thumb, index and middle fingers is used by four devices [40,54,66,67], while the configuration of thumb, middle and ring fingers is adopted by the SEM Glove, [50].…”

“…Most devices are prepared to produce a 3-point grasp type (with different finger configuration) a precision grasp according to Cutkosky's taxonomy [80]. In the pneumatic gloves [51][52][53] and in the devices based on TLSS mechanism [64,65] a power grasp (according to Cutkosky's taxonomy) can be generated too since all fingers are actuated. However, in [64] and [65], the fingers are unable to move independently.…”