@article{majikes_brugarolas_winters_yuschak_mealin_walker_yang_sherman_bozkurt_roberts_2017, title={Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system}, volume={98}, ISSN={["1095-9300"]}, DOI={10.1016/j.ijhcs.2016.04.010}, abstractNote={This paper describes a canine posture detection system composed of wearable sensors and instrumented devices that detect the postures sit, stand, and eat. The system consists of a customized harness outfitted with wearable Inertial Measurement Units (IMUs) and a base station for processing IMU data to classify canine postures. Research in operant conditioning, the science of behavior change, indicates that successful animal training requires consistent and accurate feedback on behavior. Properly designed computer systems excel at timeliness and accuracy, which are two characteristics most amateur trainers struggle with and professionals strive for. Therefore, in addition to the system being ergonomically designed to ensure the dog׳s comfort and well-being, it is engineered to provide posture detection with timing and accuracy on par with a professional trainer. We contend that providing a system with these characteristics will one day aid dogs in learning from humans by overcoming poor or ineffective timing during training. We present the initial steps in the development and validation of a computer-assisted training system designed to work outside of laboratory environments. The main contributions of this work are (a) to explore the trade-off between low-latency responses to changes in time-series IMU data representative of posture changes while maintaining accuracy and timing similar to a professional trainer, and (b) to provide a model for future ACI technologies by documenting the user-centered approach we followed to create a computer-assisted training system that met the criteria identified in (a). Accordingly, in addition to describing our system, we present the results of three experiments to characterize the performance of the system at capturing sit postures of dogs and providing timely reinforcement. These trade-offs are illustrated through the comparison of two algorithms. The first is Random Forest classification and the second is an algorithm which uses a Variance-based Threshold for classification of postures. Results indicate that with proper parameter tuning, our system can successfully capture and reinforce postures to provide computer-assisted training of dogs.}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES}, author={Majikes, John and Brugarolas, Rita and Winters, Michael and Yuschak, Sherrie and Mealin, Sean and Walker, Katherine and Yang, Pu and Sherman, Barbara and Bozkurt, Alper and Roberts, David L.}, year={2017}, month={Feb}, pages={179–195} }
 @article{bozkurt_roberts_sherman_brugarolas_mealin_majikes_yang_loftin_2014, title={Toward Cyber-Enhanced Working Dogs for Search and Rescue}, volume={29}, ISSN={["1941-1294"]}, DOI={10.1109/mis.2014.77}, abstractNote={The authors introduce the fundamental building blocks for a cyber-enabled, computer-mediated communication platform to connect human and canine intelligence to achieve a new generation of Cyber-Enhanced Working Dog (CEWD). The use of monitoring technologies provides handlers with real-time information about the behavior and emotional state of their CEWDs and the environments they're working in for a more intelligent canine-human collaboration. From handler to dog, haptic feedback and auditory cues are integrated to provide remote command and feedback delivery. From dog to handler, multiple inertial measurement units strategically located on a harness are used to accurately detect posture and behavior, and concurrent noninvasive photoplethysmogram and electrocardiogram for physiological monitoring. The authors also discuss how CEWDs would be incorporated with a variety of other robotic and autonomous technologies to create next-generation intelligent emergency response systems. Using cyber-physical systems to supplement and augment the two-way information exchange between human handlers and dogs would amplify the remarkable sensory capacities of search and rescue dogs and help them save more lives.}, number={6}, journal={IEEE INTELLIGENT SYSTEMS}, author={Bozkurt, Alper and Roberts, David L. and Sherman, Barbara L. and Brugarolas, Rita and Mealin, Sean and Majikes, John and Yang, Pu and Loftin, Robert}, year={2014}, pages={32–39} }