Jun Ye

Most individuals with tetraplegia retain some myoelectric function in their forearms, which offers the possibility of using surface electromyographic (sEMG) control for human-computer interaction (HCI). We demonstrate the potential of this approach by showing that people with motor-complete (n=5) and motor-incomplete (n=2) tetraplegia can accurately control myoelectric activity in their forearm to perform discrete button-click and continuous positioning tasks. These control inputs were mapped to the firing rate of motor units detected by a wireless wristband sensor designed for everyday use. Participants completed four testing sessions to assess their speed and accuracy. Motor units that displayed a wide dynamic range in their firing rate performed best during tasks requiring continuous, single-axis control. Interestingly, the level of impairment did not affect performance on the clicking and 1D cursor control tasks. However, those with motor-incomplete injuries showed greater independent control over two motor units than participants with motor-complete injuries, who exhibited stronger coupling between units. Participants also confirmed the practical utility of the device, successfully placing and removing the sEMG wristband on their own and consistently rating it as comfortable and easy to manage. These findings are significant because they offer the first demonstration of motor unit-based control in individuals with cervical spinal cord injury (SCI) using a fully wearable wristband interface, highlighting the feasibility of moving these systems out of the lab and into daily life.

Studies have shown that most individuals with motor complete spinal cord injuries (SCIs) can activate motor units (MUs) below the injury level, potentially enabling the use of myoelectric controllers for computer tasks. We present a novel wearable neuromotor interface driven by surface electromyography (sEMG). This wearable sEMG interface detects motor unit action potentials (MUAPs) from an array of sensors on the forearm. The MU event rate is translated (i.e. decoded) into discrete or continuous inputs for emulating button presses or joystick inputs. Our study demonstrates real-time detection and decoding of MUAPs in two individuals with tetraplegia, enabling computer task control via a non-invasive interface. Participants calibrated a spike sorting model through periods of rest and tonic activation, mapping MUAP firing rates to various degrees of freedom (DOF) for cursor or character control in tasks and 2D games. Each MUAP was mapped to a separate DOF axis, which we utilized in a Fitts’ Law target acquisition task to evaluate device throughput rate and other performance metrics such as completion rate, initiation time, time to target acquisition, and dial-in time. Our findings highlight MU firing as an effective control input, enabling gaming and social interaction for individuals with tetraplegia. This device’s wearability and ease of use offer an innovative human-computer interaction solution that may enable people with SCI to interact freely with computers and other digital devices.