This paper presents a camera-based device for monitoring walking gait speed. The walking gait speed data will be used for performance assessment of elderly patients with cancer and calibrating wearable walking gait speed monitoring devices. This standalone device has a Raspberry Pi computer, three cameras (two cameras for finding the trajectory and gait speed of the subject and one camera for tracking the subject), and two stepper motors. The stepper motors turn the camera platform left and right and tilt it up and down by using video footage from the center camera. The left and right cameras are used to record videos of the person walking. The algorithm for operating the proposed system is developed in Python. The measured data and calculated outputs of the system consist of times for frames, distances from the center camera, horizontal angles, distances moved, instantaneous gait speed (frame-byframe), total distance walked, and average speed. This system covers a large Lab area of 134.3 m 2 and has achieved errors of less than 5% for gait speed calculation.Clinical Relevance-This project will help specialists to adjust the chemo dosage for elderly patients with cancer. The results will be used to analyze the human walking movements for estimating frailty and rehabilitation applications, too.
There is promising potential in the application of algorithmic facial landmark estimation to the early prediction, in infants, of pediatric developmental disorders and other conditions. However, the performance of these deep learning algorithms is severely hampered by the scarcity of infant data. To spur the development of facial landmarking systems for infants, we introduce InfAnFace, a diverse, richly-annotated dataset of infant faces. We use InfAnFace to benchmark the performance of existing facial landmark estimation algorithms that are trained on adult faces and demonstrate there is a significant domain gap between the representations learned by these algorithms when applied on infant vs. adult faces. Finally, we put forward the next potential steps to bridge that gap 1 .
This paper presents a novel integrated stage converter configuration being applied as a LED driver. In order to prove the betterment of the topology, its performance is compared in terms of power factor, output voltage and settling time with three other randomly selected topologies: A single switch AC-DC LED driver based on boost fly back power factor correction converter with a lossless snubber, Integrated stage double buck- boost converter LED driver, Integrated stage LED driver based on buck-boost and class E resonant converter. As a consequence of relevant simulations of all the chosen topologies with resistance as the load, it is understood that Integrated buck boost fly back converter based LED Driver is beneficial as a compromise between all circuits. Hence the same is translated into an experimental prototype which is capable of a yield rated 45 V, 84.6 W and 1.88 A for a LED string load.
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