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              <p>Children with cerebral palsy (CP) can have difficulties to walk, to move or to stand still. A rehabilitation device such as an exoskeleton may be useful to promote walking for these children. However, exoskeletons can also generate stress and discomfort to the children. Due to their condition, it is not possible for them to communicate on it. A stress detection model is therefore necessary. Three types of stress/discomfort due to exoskeletons are identified: fear of falling, fear of novelty and physical discomfort, each type being simulated by its own experiment in the protocol.  The contribution of this paper is first to propose a protocol adapted to children whose aim is to simulate the stress generated by the wearing of an exoskeleton, and also to develop a stress detection model for healthy children. This is a first step towards stress detection on CP children, next steps being to use and tune this model on more healthy children and then on CP children. Only the fear of falling experiment generates stress on the six children, this stress is detected using a machine learning model based on linear regression. The proposed model uses two heart rate features: the mean of the heart rate and the proportion of successive interbeat intervals differing by more than 50 milliseconds, and achieves a balanced accuracy of 91.74 ± 8.91 %.</p>
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