The performance of any Human Activity Recognition (HAR) model trained from a general population (subject-independent), will often decreases when tested on subjects unseen during training. This is due to inter-subject variability such as differences in activity patterns, emotional status, gait or posture between users. To solve this problem, we propose a novel auto-supervision formalism based on the theory of a Particle Filter (PF) or Sequential Monte Carlo (SMC) method to automatically build a personalized HAR model. The suggested approach uses different steps based on the SMC filter method to automatically and iteratively approximate the target distribution as a set of temporal samples in order to personalize the HAR model towards a target user. These temporal samples are selected from the target user based on their importance score, which indicates the likelihood of the temporal samples belong to the target distribution. We validated our framework on several public HAR datasets, and prediction accuracy was consistently improved for new subjects in the test set.

Subdividing surfaces into cylinders is a significant question in various applications. Even if specific approaches have been described in several domains, most of the time topological properties are not explicitly handled, and the segmentation remains mainly driven by geometry. We present here an original approach to describe the topological and combinatorial nature of a tiling with cylinders. We first introduce m-cellular complexes, a framework allowing the flexible description of cuttings and tilings. Then we describe n-loops, an extension of the loops for producing tilings with cylinders. Computational issues of n-loops are then addressed, using both topological and geometrical properties of the surface. Finally, we propose two applications, first tiling a surface with large quadrangles patches, and then segmenting surfaces with possible protrusions.

Electric chronic stimulation of the human motor cortex (ECSM) has been reported to alleviate chronic severe pain. However the mechanism of action of ECSM is still hypothetical. This is due mainly to the poor knowledge of 1) the electric diffusion through the multiple structures beneath the epidural contacts (i.e. dura matter, cerebrospinal fluid space, arachnoid membrane, grey and white matter layers, pie mere and vascular tree), 2) the absence of consensus concerning the stimulation parameters (mono versus bipolar stimulation, cathodic or anodic current) and 3) the detailed cortical topography of the contacts. In this study we focused on the precise identification of the cortical areas covered by the electric contacts in a series of twelve patients operated on for ECSM. We propose a new automatic tool for topographic analysis able to compute 2D maps from the 3D anatomic MRI with bijective transformation (point- to-point correspondance). Anatomical regions of interest (AROIs) were visually identified, manually outlined and extracted (Iplan, BrainLab, Germany) for further analysis: 1) for the anatomic structures, on pre operative Tl-weigthed magnetic resonance imaging (MRI), the frontal (superior or Fl, intermediate or F2 and inferior or F3), the pre central and the post central gyrus; 2) for the electrode contacts (Resume, Medtronic, USA), on post operative computerized tomography (CT). After getting white and gray matter membership maps by automatic segmentation, we produced a cortical mask to build a triangular mesh. We defined a homeomorphism between the 3D mesh and a subset of R2 and could apply in consequence the circle packing algorithm. We built depth maps (distance to the skull), distance- to-contact maps (distance to a given electrode contact) and anatomic structure maps. Results showed that it was easier to accurately define the location of the contact projection on the cortex allowing physicians to correlate the benefit with the topography. In particular, because of the unfolding, it was easier to integrate the cytoarchitectonics (i.e. the manually identified AROIs) knowledge in the analysis. Beyond the better understanding of ECSM and indirectly of the pathophysiologic process of chronic pain, this new tool might be used in the future for image guided electrode positioning.