Spinal Cord Injury (SCI) may impair an individual's gait. For these cases, a rehabilitation technique that has become more popular is Functional Electrical Stimulation (FES). On traditional FES-assisted gait, the stimulation control is performed with manual triggering, a fact that helps make it distant from healthy gait. This work proposes a system that monitors gait variables -knee joint angles, and ground reaction forces (rearfoot and forefoot) -and uses them as inputs for an Artificial Neural Network (ANN), in order to be able to automatically control gait FES. The transducers used for angle measurement were electrogoniometers, mounted on the individual's lower limbs using Velcro straps. For force measurement, the transducers used were load cells built with strain gages, mounted on instrumented sandals. The methods for building the data acquisition hardware (transducers and interface) and software are described, along with the transducer calibration methods. All transducers presented linear behavior. Initial tests were performed, using first a healthy individual, and then a couple of patients that normally undergo suspended gait training (FES-assisted or not). The results showed that the monitoring module allows recording the data collected, and making comparison between different individuals' gait patterns, as well as different rehabilitation stages for the same individual. The ANN training for the healthy individual presented an accuracy rate close to 90%, and for the SCI patients the rate was about 80%. The control module showed promising results on practical tests performed, with quick and accurate responses for the healthy individual. Suggestions for future works were given, so that practical control tests can be performed using SCI patients.

Spinal Cord Injury (SCI) may impair an individual's gait. For these cases, a rehabilitation technique that has become more popular is Functional Electrical Stimulation (FES). Gait analysis is an important technique to evaluate rehabilitation of patients undergoing FES-assisted therapy. This work proposes a system that monitors gait variables -knee joint angles, and ground reaction forces (heel and metatarsal) -and uses them as inputs for gait analysis of paraplegic patients. The methods for building the data acquisition hardware (transducers and interface) and software are described, along with the transducer calibration methods. The results show the final prototype for the gait analysis system, which allows comparison between different individuals' gaits, as well as different rehabilitation stages for the same individual. The software has a recording feature, as well as digital control outputs, which may be used in the future for training an Artificial Neural Network (ANN) and controlling the individual's FES stimulator. In the near future, the system may be of great applicability for suspended FES-assisted gait analysis and control.