Nonlinear analysis to quantify human movement variability from time-series data (& my research)

In this talk I will speak about (a) theoretical models for human movement variability and methods to quantify human movement variability, (b) use of nonlinear methods to measure real-world time series data (i.e., data affected by non-stationarity, non-linearity, data length, sensor source, noise, etc.), and (c) illustration of results for real-world time-series data from human-robot imitation activities. I will then comment on current and future challenges on this subject and how the above points might lead to develop tools to evaluate, for instance, the improvement of movement performances, to quantify and provide feedback of skill learning or to quantify movement adaptations and pathologies. Similarly, I will speak about my current role in a multidisciplinary project in the context of Ultrasound-Guidance Procedures where I am jumping between the areas of medical imaging, AI, physics and robotics