• Gait Event Detection
• Exoskeleton
• Wavelet Transform
• Genetic Algorithm

Over the last decade, the employment of robotic exoskeletons in rehabilitation and gait assistance has considerably grown due to their capability to enhance mobility in individuals affected by lower limb impairments. This work provides a methodology for detecting heel strike (HS) timing from hip joint angle signal. The main aim is to enable, in an Active Pelvis Orthosis (APO), the quantitative assessment of the pathological gait, allowing to improve the assistive process and to evaluate the device effectiveness in terms of rehabilitative action. The proposed solution consists of a strategy based on wavelet analysis and is driven by a nature-inspired optimization process. An algorithm performs the Discrete Wavelet Transform, allowing to identify the HS occurrence from the frequency content of the hip joint angle acquired by the sensory system of the APO. Furthermore a Genetic Algorithm is implemented in order to select the optimal wavelet algorithm parameters, guaranteeing an accurate and robust event detection as the gait patterns change. The design and development of the strategy are presented along with its experimental validation with transfemoral amputees performing ground level walking at preferred speeds. The method is benchmarked against the gait event detector currently implemented in the control strategy of the APO.