Abstract
Background: FES-Cycling is an exciting recreational activity, which allows certain individuals after spinal cord injury or stroke to exercise their paralyzed muscles. The key for a successful application is to activate the right muscles at the right time. Methods: While a stimulation pattern is usually determined empirically, we propose an approach using the torque feedback provided by a commercially available crank power-meter installed on a standard trike modified for FES-Cycling. By analysing the difference between active (with stimulation) and passive (without stimulation) torques along a full pedalling cycle, it is possible to differentiate between contributing and resisting phases for a particular muscle group. In this article we present an algorithm for the detection of optimal stimulation intervals and demonstrate its functionality, bilaterally for the quadriceps and hamstring muscles, in one subject with complete SCI on a home trainer. Stimulation patterns were automatically determined for two sensor input modalities: the crank-angle and a normalized thigh-angle (i.e. cycling phase, measured via inertial measurement units). In contrast to previous studies detecting automatic stimulation intervals on motorised ergo-cycles, our approach does not rely on a constant angular velocity provided by a motor, thus being applicable to the domain of mobile FES-Cycling. Results: The algorithm was successfully able to identify stimulation intervals, individually for the subject's left and right quadriceps and hamstring muscles. Smooth cycling was achieved without further adaptation, for both input signals (i.e. crank-angle and normalized thigh-angle). Conclusion: The automatic determination of stimulation patterns, on basis of the positive net-torque generated during electrical stimulation, can help to reduce the duration of the initial fitting phase and to improve the quality of pedalling during a FES-Cycling session. In contrast to previous works, the presented algorithm does not rely on a constant angular velocity and thus can be effectively implemented into mobile FES-Cycling systems. As each muscle or muscle group is assessed individually, our algorithm can be used to evaluate the efficiency of novel electrode configurations and thus could promote increased performances during FES-Cycling.