Physical exercise may produce significant systemic benefits including reducing the risk of cardiovascular diseases. For individuals with spinal cord injury (SCI), muscles that are not volitionally contracted can be activated using functional electrical stimulation (FES), a technology where lowenergy electric impulses are applied to restore motor function. In this article, we share our experience in developing a system, the Empowering Mobility and Autonomy (EMA) tricycle, to enable persons with paraplegia to perform FES-assisted cycling. Both technological development and preparation protocols are presented, along with information regarding our team's participation at Cybathlon 2016.

Control systems for human movement based on Functional Electrical Stimulation (FES) have shown to provide excellent performance in different experimental setups. Nevertheless, there is still a limited number of such applications available today on worldwide markets, indicating poor performance in real settings, particularly for upper limb rehabilitation and assistance. Based on these premises, in this paper we explore the use of an alternative control strategy based on co-activation of antagonist muscles using FES. Although co-contraction may accelerate fatigue when compared to single-muscle activation, knowledge from motor control indicate it may be useful for some applications. We have performed a simulation and experimental study designed to evaluate whether controllers that integrate such features can modulate joint impedance and, by doing so, improving performance with respect to disturbance rejection. The simulation results, obtained using a novel model including proprioceptive feedback and anatomical data, indicate that both stiffness and damping components of joint impedance may be modulated by using FES-induced co-activation of antagonist muscles. Preliminary experimental trials were conducted on four healthy subjects using surface electrodes. While the simulation investigation predicted a maximum 494% increase in joint stiffness for wrist flexion/extension, experiments provided an average elbow stiffness increase of 138% using lower stimulation intensity. Closed-loop experiments in which disturbances were applied have demonstrated that improved behavior may be obtained, but increased joint stiffness and other issues related to simultaneous stimulation of antagonist muscles may indeed produce greater errors.

Individuals who sustained a spinal cord injury often lose important motor skills, and cannot perform basic daily living activities. Several assistive technologies, including robotic assistance and functional electrical stimulation, have been developed to restore lost functions. However, designing reliable interfaces to control assistive devices for individuals with C4–C8 complete tetraplegia remains challenging. Although with limited grasping ability, they can often control upper arm movements via residual muscle contraction. In this article, we explore the feasibility of drawing upon these residual functions to pilot two devices, a robotic hand and an electrical stimulator. We studied two modalities, supra-lesional electromyography (EMG), and upper arm inertial sensors (IMU). We interpreted the muscle activity or arm movements of subjects with tetraplegia attempting to control the opening/closing of a robotic hand, and the extension/flexion of their own contralateral hand muscles activated by electrical stimulation. Two groups were recruited: eight subjects issued EMG-based commands; nine other subjects issued IMU-based commands. For each participant, we selected at least two muscles or gestures detectable by our algorithms. Despite little training, all participants could control the robot’s gestures or electrical stimulation of their own arm via muscle contraction or limb motion.

Functional Electrical Stimulation assisted cycling (FES-Cycling) is increasingly becoming an alternative option recommended to people with spinal cord injury struggling with paraplegia and interested in practicing sports. In order to propose preconditions to guide FES-Cycling recommendation, we aimed to investigate some features and their potential relationships with responsiveness to Neuromuscular Electrical Stimulation (NMES). Fourteen volunteers attended a public recruitment forum to be assessed about their responsiveness through the 16-sessions of NMES. Volunteers were separated in two groups (responsive and non-responsive to NMES) which were investigated in the light of some personal, clinical, structural and functional features. Fifty seven percent of the initial sample responded to electrical stimulation with a visual contraction. This responsive group was predominantly composed by subjects presenting traumatic spinal cord injuries above T12 vertebral level. Only two subjects became responsive at the 3rd and 16th sessions. Among the observed features, the etiology and level of injuries seems to be more associated to responsiveness. Our observations seem to indicate that subjects with traumatic spinal cord injury above T12 level were the best potential candidates for FES-cycling.

Working towards the development of robust motion recognition systems for assistive technology control, the widespread approach has been to use a plethora of, often times, multi-modal sensors. In this paper, we develop single-sensor motion recognition systems. Utilising the peripheral nature of surface electromyography (sEMG) data acquisition, we optimise the information extracted from sEMG sensors. This allows the reduction in sEMG sensors or provision of contingencies in a system with redundancies. In particular, we process the sEMG readings captured at the trapezius descendens and platysma muscles. We demonstrate that sEMG readings captured at one muscle contain distinct information on movements or contractions of other agonists. We used the trapezius and platysma muscle sEMG data captured in able-bodied participants and participants with tetraplegia to classify shoulder movements and platysma contractions using white-box supervised learning algorithms. Using the trapezius sensor, shoulder raise is classified with an accuracy of 99%. Implementing subject-specific multi-class classification, shoulder raise, shoulder forward and shoulder backward are classified with a 94% accuracy amongst object raise and shoulder raise-and-hold data in able bodied adults. A three-way classification of the platysma sensor data captured with participants with tetraplegia achieves a 95% accuracy on platysma contraction and shoulder raise detection.