  This paper presents a control interface to translate the residual body
motions of individuals living with severe disabilities, into control commands
for body-machine interaction. A custom, wireless, wearable multi-sensor network
is used to collect motion data from multiple points on the body in real-time.
The solution proposed successfully leverage electromyography gesture
recognition techniques for the recognition of inertial measurement units-based
commands (IMU), without the need for cumbersome and noisy surface electrodes.
Motion pattern recognition is performed using a computationally inexpensive
classifier (Linear Discriminant Analysis) so that the solution can be deployed
onto lightweight embedded platforms. Five participants (three able-bodied and
two living with upper-body disabilities) presenting different motion
limitations (e.g. spasms, reduced motion range) were recruited. They were asked
to perform up to 9 different motion classes, including head, shoulder, finger,
and foot motions, with respect to their residual functional capacities. The
measured prediction performances show an average accuracy of 99.96% for
able-bodied individuals and 91.66% for participants with upper-body
disabilities. The recorded dataset has also been made available online to the
research community. Proof of concept for the real-time use of the system is
given through an assembly task replicating activities of daily living using the
JACO arm from Kinova Robotics.
