Dietary choices are the primary determinants of prominent dis- eases such as diabetes, heart disease, and obesity. Human health care providers, such as dietitians, cannot be at the side of every user at all times to manually guide them towards optimal choices. Automated adaptive guidance fused with expert knowledge can use multimedia data to technologically scale health guidance without human intervention. Addressing the correct granularity of recommendations (in this case meal dishes) is essential for effortless decision making. Thus we make a decision support system using multi-modal data relying on timely, contextually aware, personalized data to find local restaurant dishes to satisfy a user's needs. Algorithms in this system take nutritional facts regarding products, efficiently calculate which items are healthiest, then re-rank and filter results to users based on their personalized health data streams and environmental context. Our recommendation engine is driven by the primary goal of lowering the barriers to a personalized healthy choice when eating out, by distilling dish suggestions to a single contextually aware and easily understood score.
Using Kinect sensors to monitor and provide feedback to patients performing intervention or rehabilitation exercises is an upcoming trend in healthcare. However, the joint positions measured by the Kinect sensor are often unreliable, especially for joints that are occluded by other parts of the body. Motion capture (MOCAP) systems using multiple cameras from different view angles can accurately track marker positions on the patient. But such systems are costly and inconvenient to patients. In this work, we simultaneously capture the joint positions using both a Kinect sensor and a MOCAP system during a training stage and train a Gaussian Process regression model to map the noisy Kinect measurements to the more accurate MOCAP measurements. To deal with the inherent variations in limb lengths and body postures among different people, we further propose a joint standardization method, which translates the raw joint positions of different people into a standard coordinate, where the distance between each pair of adjacent joints is kept at a reference distance. Our experiments show that the denoised Kinect measurements by the proposed method are more accurate than several benchmark methods.
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