Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models.

Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer's disease.

Preparation of water-soluble chitosan oligosaccharides by oxidative hydrolysis of chitosan powder with hydrogen peroxide.

Chitosan (CS) is only soluble in weak acid medium, thereby limiting its wide utilisation in the field of biomedicine, food, and agriculture. In this report, we present a method for preparing water-soluble CS oligosaccharides (COSs) at high concentration (∼10%, w/v) via the oxidative hydrolysis of CS powder with molecular weight (Mw) ∼90,000 g/mol) in 2% H2O2 solution at ambient temperature by a two-step process, namely, the heterogeneous hydrolysis step and homogeneous hydrolysis step. The resultant COSs were characterised by gel permeation chromatography (GPC), fourier transforms infrared spectroscopy (FT-IR), ultraviolet-visible spectroscopy (UV-Vis), proton nuclear magnetic resonance spectroscopy (1H NMR) and X-ray diffraction (XRD) spectroscopy. The resulting products were composed of COSs (Mw of 2000-6600 g/mol) that were completely soluble in water. The results also indicated that the structure of COSs was almost unchanged compared with the original CS unless Mw was low. Accordingly, COSs with low Mw (∼2000 g/mol) and high concentration (10%, w/v) could be effectively prepared by the oxidative hydrolysis of CS powder using hydrogen peroxide under ambient conditions.

Contactless abnormal gait detection.

We present a new method to detect abnormal gait based on the symmetry verification of the two-leg movement. Unlike other methods requiring special motion captors, the proposed method uses image processing techniques to correctly track leg movement. Our method first divides each leg into upper and lower parts using anatomical knowledge. Then each part is characterised by two straight lines approximating its two borders. Finally, leg movement is represented by the angle evolution of these lines. In this process, we propose a new line approximation algorithm which is robust to the outliers caused by incorrect separation of leg into upper / lower parts. In our experiment, the proposed method got very encouraging results. With 281 normal / abnormal gait videos of 9 people, this method achieved a classification accuracy of 91%.