@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} }
 @inproceedings{majikes_mealin_rita_walker_yuschak_sherman_bozkurt_roberts_2016, title={Smart connected canines: IoT design considerations for the lab, home, and mission-critical environments (invited paper)}, DOI={10.1109/sarnof.2016.7846739}, abstractNote={The canine-human relationship continues to grow as dogs become an increasingly critical part of our society. As reliance on dogs has increased from simple companionship, to service dogs, urban security, and national defense, the opportunities for enhanced communications between the working canine and their handler increase. Wireless sensor networks and the Internet of Things (IoT) can extend traditional canine-human communication to integrate canines into the cyber-enabled world. This is what we call the Smart Connected Canine (SCC). Canine-computer interaction is sufficiently different from human-computer interaction so as to present some challenging research and design problems. There are physical and performance limits to what a dog will naturally tolerate. There are communications requirements for monitoring dogs, monitoring the environment, and for canine-human communications. Depending on the working environment there are different performance, security, and ergonomic considerations. This paper summarizes three example canine-human systems we presented earlier along with their Ion data characteristics and design criteria in order to explore how smart connected canines can improve our lives, the future of smart connected canines, and the requirements on IoT technologies to facilitate this future.}, booktitle={2016 ieee 37th sarnoff symposium}, author={Majikes, J. J. and Mealin, S. and Rita, B. and Walker, K. and Yuschak, S. and Sherman, B. and Bozkurt, A. and Roberts, D. L.}, year={2016}, pages={118–123} }
 @article{winters_brugarolas_majikes_mealin_yuschak_sherman_bozkurt_roberts_2015, title={Knowledge Engineering for Unsupervised Canine Posture Detection from IMU Data}, DOI={10.1145/2832932.2837015}, abstractNote={Training animals is a process that requires a significant investment of time and energy on the part of the trainer. One of the most basic training tasks is to train dogs to perform postures on cue. While it might be easy for a human trainer to see when an animal has performed the desired posture, it is much more difficult for a computer to determine this. Most work in this area uses accelerometer and/or gyroscopic data to produce data from an animal's current state, but this has limitations. Take for example a normal standing posture. From an accelerometer's perspective, it closely resembles the "laying down" posture, but the posture can look very different if the animal is standing still, versus walking, versus running, and might look completely different from a "standing on incline" posture. A human trainer can instantly tell the difference between these postures and behaviors, but the process is much more difficult for a computer. This paper demonstrates several algorithms for recognizing canine postures, as well as a system for building a computational model of a canine's potential postures, based solely on skeletal measurements. Existing techniques use labeled data, which can be difficult to acquire. We contribute a new technique for unsupervised posture detection, and compare the supervised technique to our new, unsupervised technique. Results indicate that the supervised technique performs with a mean 82.06% accuracy, while our unsupervised approach achieves a mean 74.25% accuracy, indicating that in some cases, our new unsupervised technique is capable of achieving comparable performance.}, journal={12TH ADVANCES IN COMPUTER ENTERTAINMENT TECHNOLOGY CONFERENCE (ACE15)}, author={Winters, Michael and Brugarolas, Rita and Majikes, John and Mealin, Sean and Yuschak, Sherrie and Sherman, Barbara L. and Bozkurt, Alper and Roberts, David}, year={2015} }
 @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} }