Gait analysis and falls estimation using the slope's variation of synchronous statistics

The walking of human, from childhood to elderly is difficult to interpret and analyze because of the variability of displacements of individuals. The age, weight, sex, and other parameters affect the walking conditions and therefore the results. Two approaches exist to study the gait. The first approach generalized to biomechanics is based on the observations of variables which are easily measured and most of the time associated to a descriptive model. This allows the analysis of motion to measure performance as well to characterize pathology or be used to view the progress of reeducation. The second approach is the neuroscience and is considered to be more explanatory. It focuses on the supposed causes of displacement, in order to improve knowledge on the functioning of the central nervous system and sensory motor system. There have been numerous studies involving research and development, for estimating falls. Prevention of a falling elderly is complex to estimate, small research has been done. In fact research is often strictly limited resourceful medical organizations that have specialized clinical tools. Human locomotion is defined by sequences of cyclic and repeated gestures. The variability of such sequences can reveal information about drive failure and motor / motor-neuron disorders. Studying and exploiting the Cyclostationary (CS) properties of such sequences, offers a complementary way to quantify human locomotion and its changes with progressing aging and the development of diseases. As part of the collaboration between LASPI and CHU Saint Etienne, we decided to focus on certain advanced signal processing theory and methods, to study very complex phenomena of human walking, which is often subject to numerous motor and / or motor-neurons malfunctions, such as in the case of the falling elderly population, that often has serious and severe consequences. Furthermore, this paper also examined the effects on walking in elderly with history of falling or not. Results show that the new indicator based on synchronous statistics helping to detect fallers' signals from non-fallers.