Machine Learning based Physical Human-Robot Interaction for Walking Support of Frail People.

In the near future robots will permeate our daily life empowering human beings in several activities of daily living. Particular, service robots could actively support indoor mobility tasks thus to enhance the independent living of citizens. They should be able to provide tailored services to citizens to achieve higher physical human-robot interaction. Too often service robots were designed without taking into account end-users functional requirements, which can change with age and geriatric syndromes. In this paper, we present a robot smart control based on machine learning strategies and adaptable to different handgrip strengths. The smart control was implemented on ASTRO robot conceived to be a companion and to support indoor mobility, among other activities. Particularly, three smart controller strategies were implemented and tested with end users from technical and user point of view. The results show promising results that underline the proposed approach was suitable for the proposed application.

Utilization of murine peripheral blood lymphocytes for H-2 typing.

We adapted the NIH Standard Protocol for HLA-A, B, C typing to perform murine H-2 typing. The assay is direct, measuring the cytotoxicity of the antiserum/cell/complement reaction with a supravital dye. This method is advantageous because it: utilizes peripheral blood lymphocytes (PBL) obtained from the tail vein; uses microliter volumes of antiserum; is practical because the formalin fixed reactions need not be read immediately; involves standard and inexpensive cytotoxicity techniques; is easily interpreted and is readily reproducible.