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Spinal cord injury (SCI) resulting in paralysis of lower limbs and trunk restricts daily upright activity, work capacity, and ambulation ability, putting persons with an injury at greater risk of developing a myriad of secondary medical issues. Time spent in the upright posture has been shown to decrease the risk of these complications in SCI. Unfortunately, the majority of ambulation assistive technologies are limited by inefficiencies such as high energy demand, lengthy donning and doffing time, and poor gait pattern precluding widespread use. These limitations spurred the development of bionic exoskeletons. These devices are currently being used in rehabilitation settings for gait retraining, and some have been approved for home use. This overview will address the current state of available devices and their utility.

Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21 year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain-computer interface within the home environment. The brain-computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-week laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement intent information could be decoded consistently throughout the 29-week in-laboratory study with a mean accuracy of 89.0% (range 78–93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80–98.95%) and an accuracy of 88.3% (range 77.6–95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain-computer interface can be safely used to reliably decode movement intent from motor cortex, allowing for accurate volitional control of hand grasp.

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