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International audience; As the world population continues to age, there is an increased need for efficient systems that can improve rehabilitation standards for the elderly and other motor-impaired subjects. A self-training system that takes into account subject-specific differences would contribute in a positive way to personalized home rehabilitation protocols. Since human balance depends largely on the control of the full body center of mass (CoM), fall risks can be assessed by observing the CoM displacement over the support polygon. The CoM position is often estimated using what are known as anthropometric tables. However, the parameters have been obtained from a standard population and will differ between subjects. Variability across subjects is increased for motor-impaired subjects as anthropometric values vary as a function of age, somatotype, and fitness level. This chapter develops a personalized measure of balance that considers subject-specific variations. This is a step towards the creation of versatile and portable self-balance assessment tools. We have expanded the range of application of the statically equivalent serial chain (SESC) mainly regarding the following points: (1) We have used the SESC method to estimate accurately CoM position with portable sensors like Kinect and Wii balance board. The system was validated by comparing its performance to high-end laboratory equipment: Vicon and force platform. We find that the CoM estimation obtained with SESC and the Kinect performed equivalently to the one based on anthropometric values and high-end equipment. (2) We developed an adaptive interface for self-identification of the SESC parameters by implementing a recursive identification method. This decreased the time necessary to establish the SESC by allowing the subject to perform a self-directed identification. (3) We assess a personalized balance based only on the identified CoM. This chapter also overviews home-based rehabilitation along with other state-of-the-art works.

International audience; Cardiac modelling aims at understanding cardiac diseases and predicting cardiac responses to therapies. By generating the elec-trical propagation, the contraction and the mechanical response, we are able to simulate cardiac motion from non-invasive imaging techniques. Four healthy canine clinical data (left ventricles) were provided by the STACOM'2014 challenge. Our study is based on Bestel-Clement-Sorine mechanical modelling, while the electrophysiological phenomena is driven by an Eikonal model. Our model has been calibrated by a quantitative sensitivity study as well as a personalized automatic calibration. Results and comparison with clinical measures are shown in terms of left ventricular volume, flow, pressure and ejection fraction.

International audience We present the fundamental principles of data assimilation underlying the Verdandi library, and how they are articulated with the modular architecture of the library. This translates -- in particular -- into the definition of standardized interfaces through which the data assimilation library interoperates with the model simulation software and the so-called observation manager. We also survey various examples of data assimilation applied to the personalization of biophysical models, in particular for cardiac modeling applications within the euHeart European project. This illustrates the power of data assimilation concepts in such novel applications, with tremendous potential in clinical diagnosis assistance.