@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{brugarolas_valero-sarmiento_bozkurt_essick_2016, title={Auto-adjusting mandibular repositioning device for in-home use}, DOI={10.1109/embc.2016.7591677}, abstractNote={Obstructive Sleep Apnea (OSA) is a form of respiratory dysfunction that affects 20% of adults in the world. Among the first-line treatments that are used to mitigate the effects of OSA are continuous positive airway pressure (CPAP) and mandibular repositioning devices (MRD). Although CPAP provides a more efficacious therapy than MRDs, recent studies suggest that both are comparable in overall effectiveness due to greater patient preference and adherence to MRD therapy. In this paper, we present the Auto-Positioner, a novel add-on for MRDs that adjusts the extent to which the mandible (lower jaw) is advanced in response to respiratory signals indicating labored breathing during sleep, and to changes in sleeping position known to affect individual patient's airway patency.}, booktitle={2016 38th annual international conference of the ieee engineering in medicine and biology society (embc)}, author={Brugarolas, R. and Valero-Sarmiento, J. M. and Bozkurt, A. and Essick, G. K.}, year={2016}, pages={4296–4299} }
 @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} }
 @inproceedings{brugarolas_agcayazi_yuschak_roberts_sherman_bozkurt_2016, title={Towards a wearable system for continuous monitoring of sniffing and panting in dogs}, DOI={10.1109/bsn.2016.7516276}, abstractNote={Although numerous advances have been made in instrumental odor detection systems, these still cannot match the efficient sampling, odor discrimination, agile mobility and the olfactory acuity of odor detection dogs. A limiting step in using dogs to detect odors is the subjectivity of the translation of odor information processed by the dog to its handler. We present our preliminary efforts towards a wireless wearable system for continuous auscultation of respiratory behavior by recording internal sounds at the neck and chest by means of a commercially available electronic stethoscope to provide objective decision support for handlers. We have identified discrete features of sniffing and panting in the time domain and utilize event duration, event rate, event mean energy, and the number of consecutive events in a row to build a decision tree classifier. Since feature extraction requires segmentation of individual sniffing and panting events, we developed an adaptive method using short-time energy contour and an adaptive threshold. The performance of the system was evaluated on recordings from a Greyhound and a Labrador Retriever and achieved high classification accuracies.}, booktitle={International conference on wearable and implantable body sensor}, author={Brugarolas, R. and Agcayazi, T. and Yuschak, S. and Roberts, D. L. and Sherman, B. L. and Bozkurt, A.}, year={2016}, pages={292–295} }
 @article{brugarolas_latif_dieffenderfer_walker_yuschak_sherman_roberts_bozkurt_2016, title={Wearable Heart Rate Sensor Systems for Wireless Canine Health Monitoring}, volume={16}, ISSN={["1558-1748"]}, DOI={10.1109/jsen.2015.2485210}, abstractNote={There is an increasing interest from dog handlers and veterinarians in an ability to continuously monitor dogs' vital signs (heart rate, heart rate variability, and respiratory rate) outside laboratory environments, with the aim of identifying physiological correlations to stress, distress, excitement, and other emotional states. We present a non-invasive wearable sensor system combining electrocardiogram (ECG), photoplethysmogram (PPG), and inertial measurement units (IMU) to remotely and continuously monitor the vital signs of dogs. To overcome the limitations imposed by the efficiently insulated skin and dense hair layers of dogs, we investigated the use of various styles of ECG electrodes and the enhancements of these by conductive polymer coatings. We also studied the incorporation of light guides and optical fibers for an efficient optical coupling of PPG sensors to the skin. Combined with our parallel efforts to use IMUs to identify dog behaviors, these physiological sensors will contribute to a canine-body area network to wirelessly and continuously collect data during canine activities with a long-term goal of effectively capturing and interpreting dogs' behavioral responses to environmental stimuli that may yield measurable benefits to handlers' interactions with their dogs.}, number={10}, journal={IEEE SENSORS JOURNAL}, author={Brugarolas, Rita and Latif, Tahmid and Dieffenderfer, James and Walker, Katherine and Yuschak, Sherrie and Sherman, Barbara L. and Roberts, David L. and Bozkurt, Alper}, year={2016}, month={May}, pages={3454–3464} }
 @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} }
 @inproceedings{brugarolas_valero-sarmiento_brna_2015, title={Wearable SpO(2) and sleep posture monitoring system for obstructive sleep apnea patients}, booktitle={2015 Virtual Conference on Application of Commercial Sensors}, author={Brugarolas, R. and Valero-Sarmiento, J. M. and Brna, A.}, 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} }
 @inproceedings{brugarolas_loftin_yang_roberts_sherman_bozkurt_2013, title={Behavior recognition based on machine learning algorithms for a wireless canine machine interface}, DOI={10.1109/bsn.2013.6575505}, abstractNote={Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.}, booktitle={2013 IEEE International Conference on Body Sensor Networks (BSN)}, author={Brugarolas, R. and Loftin, R. T. and Yang, P. and Roberts, D. L. and Sherman, B. and Bozkurt, A.}, year={2013} }
 @inproceedings{brugarolas_roberts_sherman_bozkurt_2013, title={Machine learning based posture estimation for a wireless canine machine interface}, DOI={10.1109/biowireless.2013.6613658}, abstractNote={Effective training and accurate interpretation of canine behaviors are essential for dog welfare and to obtain the maximum benefits provided by working dogs. We are developing a canine body area network based interface to incorporate electronic sensing and computational behavior modeling into canine training, where computers will be able to provide real time feedback to trainers about canine behavior. In this study, we investigated the accuracy of machine learning algorithms in identifying canine posture through wireless inertial sensing with 3-axis accelerometers and 3-axis gyroscopes. Data was collected from two dogs performing a sequence of 5 postures (sit, stand, lie, stand on two legs, and eat off the ground). A two-stage cascade learning technique was used: one for differentiating samples of behaviors of interest from transitions between behaviors, and one for posture classification of the behaviors. The algorithms achieved high posture classification accuracies demonstrating potential to enable a real time canine computer interface.}, booktitle={Ieee topical conference on biomedical wireless technologies networks and}, author={Brugarolas, R. and Roberts, D. and Sherman, B. and Bozkurt, A.}, year={2013}, pages={10–12} }
 @inproceedings{brugarolas_roberts_sherman_bozkurt_2012, title={Posture estimation for a canine machine interface based training system}, DOI={10.1109/embc.2012.6346964}, abstractNote={Dogs and humans have worked in partnership throughout history thanks to dogs' unique capability of detecting signals in human voices or gestures and learning from human inputs. Traditional canine training methods rely solely on subjective visual observations made by trainers. We propose a canine body-area-network (cBAN) to incorporate context-aware sensing with objective detection algorithms to augment the sensitivity and specificity of human trainer's awareness of the dogs they are training. As an initial effort, we developed a Bluetooth-based wireless infrastructure and tested inertial measurement units as cBAN sensor nodes to electronically assess the posture of the dogs. As a result, we were able to optimize the sensor locations and distinguish different postures using the distinct patterns in the measured angles.}, booktitle={2012 annual international conference of the ieee engineering in medicine and biology society (embc)}, author={Brugarolas, R. and Roberts, D. and Sherman, B. and Bozkurt, A.}, year={2012}, pages={4489–4492} }