Search results for: Tracy Hammond

Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: Time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition.

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Authors: Benjamin Gorry, Zezhi Chen, Kevin Hammond, Andy Wallace, Greg Michaelson

Abstract:

This paper describes new computer vision algorithms that have been developed to track moving objects as part of a long-term study into the design of (semi-)autonomous vehicles. We present the results of a study to exploit variable kernels for tracking in video sequences. The basis of our work is the mean shift object-tracking algorithm; for a moving target, it is usual to define a rectangular target window in an initial frame, and then process the data within that window to separate the tracked object from the background by the mean shift segmentation algorithm. Rather than use the standard, Epanechnikov kernel, we have used a kernel weighted by the Chamfer distance transform to improve the accuracy of target representation and localization, minimising the distance between the two distributions in RGB color space using the Bhattacharyya coefficient. Experimental results show the improved tracking capability and versatility of the algorithm in comparison with results using the standard kernel. These algorithms are incorporated as part of a robot test-bed architecture which has been used to demonstrate their effectiveness.

Keywords: Hume, functional programming, autonomous vehicle, pioneer robot, vision.

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Authors: Kudzai Ashley Tagwira, Michelle Marle Marais, Tracy Anne Ludwig, Rutendo Precious Chidziva, Mavis Nyaradzo Munodawafa, Wendy M. Wrench, Roman Tandlich

Abstract:

Risk of infectious disease outbreaks is related to the hygiene among the population. To assess the actual risks and modify the relevant emergency procedures if necessary, a hygiene survey was conducted among undergraduate students on the Rhodes University campus. Soap was available to 10.5% and only 26.8% of the study participants followed proper hygiene in relation to food consumption. This combination increases the risk of infectious disease outbreaks at the campus. Around 83.6% were willing to wash their hands if soap was provided. Procurement and availability of soap in undergraduate residences on campus should be improved, as the total cost is estimated at only 2000 USD per annum. Awareness campaigns about food-related hygiene and the need for regular handwashing with soap should be run among Rhodes University students. If successful, rates of respiratory and hygiene-related diseases will be decreased and emergency health management simplified.

Keywords: Awareness, Food hygiene, Infectious disease spread, Undergraduate students.

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