@article{foster_brugarolas_walker_mealin_cleghern_yuschak_clark_adin_russenberger_gruen_et al._2020, title={Preliminary Evaluation of a Wearable Sensor System for Heart Rate Assessment in Guide Dog Puppies}, volume={20}, ISSN={["1558-1748"]}, url={https://doi.org/10.1109/JSEN.2020.2986159}, DOI={10.1109/JSEN.2020.2986159}, abstractNote={This paper details the development of a novel wireless heart rate sensing system for puppies in training as guide dogs. The system includes a harness with on-board electrocardiography (ECG) front-end circuit, inertial measurement unit and a micro-computer with wireless capability where the major research focus of this paper was on the ergonomic design and evaluation of the system on puppies. The first phase of our evaluation was performed on a Labrador Retriever between 12 to 26 weeks in age as a pilot study. The longitudinal weekly data collected revealed the expected trend of a decreasing average heart rate and increased heart rate variability as the age increased. In the second phase, we improved the system ergonomics for a larger scale deployment in a guide dog school (Guiding Eyes for the Blind (Guiding Eyes)) on seventy 7.5-week-old puppies (heart rate coverage average of 86.7%). The acquired ECG based heart rate data was used to predict the performance of puppies in Guiding Eyes’s temperament test. We used the data as an input to a machine learning model which predicted two Behavior Checklist (BCL) scores as determined by expert Guiding Eyes puppy evaluators with an accuracy above 90%.}, number={16}, journal={IEEE SENSORS JOURNAL}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Foster, Marc and Brugarolas, Rita and Walker, Katherine and Mealin, Sean and Cleghern, Zach and Yuschak, Sherrie and Clark, Julia Condit and Adin, Darcy and Russenberger, Jane and Gruen, Margaret and et al.}, year={2020}, pages={9449–9459} }
 @article{brugarolas_yuschak_adin_roberts_sherman_bozkurt_2019, title={Simultaneous Monitoring of Canine Heart Rate and Respiratory Patterns During Scent Detection Tasks}, volume={19}, ISSN={["1558-1748"]}, DOI={10.1109/JSEN.2018.2883066}, abstractNote={Man-made technological odor detection systems continue to improve but still cannot match the tracking efficiency, mobility, and selectivity in the presence of interfering odors achieved by detector dogs. The reliability of dogs as olfactory detectors does not depend solely on their performance but also on the handler’s skill in interpreting the behavior of the dog that signals the detection of the target odor. We present our efforts on a wireless wearable system combining electrocardiogram (ECG) and an electronic stethoscope for direct monitoring of cardiopulmonary events in dogs toward enabling cybernetic dog–machine interfaces. This is, to our knowledge, the first cyber-physical attempt to simultaneously record heart rate, heart rate variability, and continuous auscultation of respiratory behavior in a wearable form factor during scent-detection tasks. In this paper, we present: 1) a method to automate the detection of sniffing episodes based on power spectral density of the respiratory sounds; 2) proof-of-concept extraction and quantification of the ECG and respiratory sounds features that would enable the discovery of physiological patterns associated to scent-detection tasks; and 3) proof-of-concept correlation of such patterns with the presence/absence of target odors. These contributions pave the way for a novel real-time cybernetic olfactory detection monitoring system to provide decision support for handlers in the field in addition to enabling future computer-sniffing dog interfaces.}, number={4}, journal={IEEE SENSORS JOURNAL}, author={Brugarolas, Rita and Yuschak, Sherrie and Adin, Darcy and Roberts, David L. and Sherman, Barbara L. and Bozkurt, Alper}, year={2019}, month={Feb}, pages={1454–1462} }
 @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} }
 @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} }