Quantitative Indicators for Behaviour Drift Detection from Home Automation Data.

Smart Homes diffusion provides an opportunity to implement elderly monitoring, extending seniors' independence and avoiding unnecessary assistance costs. Information concerning the inhabitant behaviour is contained in home automation data, and can be extracted by means of quantitative indicators. The application of such approach proves it can evidence behaviour changes.

SHARON: a Simulator of Human Activities, ROutines and Needs.

In a few decades, requests for assistance to the elderly will increase the already high health care costs. Within this context, a possible solution is represented by smart environments where services help dwellers' life. The development of smart technologies requires large datasets for training, validation or testing. Since the data collection from real smart homes has high costs the authors developed SHARON - a Simulator of Human Activity, ROutines and Needs. This software aims to support such projects, virtually reproducing environments and behaviors of the dwellers. This work proposes and validates a behavioral model able reproduce decisions and human habits, starting from an available data set or an interview. Physiological parameters and habits are merged with a probabilistic approach, choosing the most likely activity. With respect to other behavioral simulators available in the literature, SHARON is focused on routines and activities generation, based on user defined high level parameters. Within the evaluation phase it was applied a cross-validation approach, by simulating 300 days starting from a training set of 23 days and testing with the remaining 7 days validation set. As expected, results prove the simulated data correctly reproduce the activities routine distributions, in particular the more regular ones.

Quantitative characterization and identification of lymph nodes and nasopharingeal carcinoma by coregistered magnetic resonance images.

In this study we developed a technique to improve the identification of carcinoma and pathological lymph nodes in cases of Nasopharingeal Carcinoma (NPC), through a quantitative characterization of the tissues based on MR images: 3D VIBE (Volumetric Interpolated Breath-hold Examination) T1-CE (Contrast Enhanced), T1, T2 and Diffusion Weighted Imaging (DWI) for b-values 0,300,500,700,1000. The procedure included two phases: 1) coregistration of volumes and 2) tissue characterization. Concerning the first phase, the DICOM images were reassembled spatially and resampled with isotropic 0.5mm resolution. Coregistration was performed by two multiresolution rigid transformations, merging head and neck volumes, plus a final multiresolution non rigid transformation. The anatomical 3D CE-VIBE volume was taken as reference. The procedure for tissue characterization is semi automated and it required a radiologist to identify an example of tissue from the primary tumor and a metastatic lymph node. We generated a 8-dimensional membership function to perform a fuzzy-like identification of these tissues. The result of this procedure was the generation of two maps, which showed complementary characterization of lymph nodes and carcinoma. A few example will be shown to evidence the potentiality of this method in identification and characterization of NPC lesions.