In this paper, we propose an online scene reconstruction algorithm with monocular camera since there are many advantages on modeling and visualization of an environment with physical scene reconstruction instead of resorting to sparse 3D points. The goal of this algorithm is to simultaneously track the camera position and map the 3D environment, which is close to the spirit of visual SLAM. There're plenty of visual SLAM algorithms in the current literature which can provide a high accuracy performance, but many of them rely on stereo cameras. It's true that we'll face many more challenges to accomplish this task with monocular camera. However, the advantages of cheaper and easier deployable hardware setting have made monocular approach more attractive. Specifically, we apply a maximum a posteriori Bayesian approach with optimization technique to simultaneously track the camera and build a dense point cloud. We also propose a feature expansion method to expand the density of points, and then online reconstruct the scene with a delayed approach. Furthermore, we utilize the reconstructed model to accomplish visual localization task without extracting the features. Finally, a number of experiments have been conducted to validate our proposed approach, and promising performance can be observed.

This paper introduces a context-aware assisted active robotic walker for Parkinson's disease (PD) patients. Most of PD patients suffer from not only loss of balance but also abnormal gaits. These symptoms tend to make PD patients fall down more easily and result in low quality of life. We use Hidden Markov Model (HMM) to analyze the gait of PD patients, and then use our walker to help patients adjust their gait to become normal while applying auditory cues when abnormal gaits are recognized. To prevent user from leaning forward before falling down, the walker locks the motors when sudden forward pushing by the user is detected. Moreover, the walker can record the statics of gait from the user, making the therapists monitor the rehabilitation process relatively easier. Finally, the road conditions in front of the walker will be automatically analyzed, making user able to adjust his/her walking pace dynamically. To our best knowledge, the hereby proposed active robotic walker should be the first system which can provide walking aid to PD patients. In our experiments, the feasibility and performance of this system are evaluated by PD patients at two actual senior care units.