@article{wu_nichols_foster_martin_dieffenderfer_enomoto_lascelles_russenberger_brenninkmeyer_bozkurt_et al._2023, title={An Exploration of Machine Learning Methods for Gait Analysis of Potential Guide Dogs}, url={https://doi.org/10.1145/3637882.3637883}, DOI={10.1145/3637882.3637883}, abstractNote={Gait analysis is important for guide dog organizations, as ideal guide dogs have a smooth and efficient gait, where they can also easily shift between and maintain various gaits. Gait quality and natural traveling speed are two of the multiple factors important in matching a guide dog to its visually impaired handler. Gait evaluation typically includes subjective visual observation of the dog or objective assessments obtained from special-designed equipment. Guide dog organizations need a method to easily collect and analyze objective data of gait information. In this work, we explored how various machine learning models could learn and analyze gait patterns from inertial measurements data that were collected during two different data collection experiments using a wearable sensor device. We also evaluated how well each machine learning model could generalize behavior patterns from various dogs under different environments. Additionally, we compared how sensor placement locations could affect gait prediction performance by attaching the sensor device to the dog’s neck and back area respectively. The tested machine learning models were able to classify different gaits in the range of 42% to 91% in terms of accuracy, and predict various gait parameters with an error rate ranging from 14% to 29% depending on the setup. Furthermore, we also observed that using behavior data collected from the neck region contains more movement information than the back area. By performing a cross-dataset generalization test on the machine learning models, we found that even with performance drop, the models were able to learn gait-specific behavior patterns that are generalizable for different dogs. Although the results were preliminary, the proposed gait analysis exploration still showed promising potential for studying behavior patterns of candidate guide dogs.}, journal={TENTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2023}, author={Wu, Yifan and Nichols, Colt and Foster, Marc and Martin, Devon and Dieffenderfer, James and Enomoto, Masataka and Lascelles, B. Duncan X. and Russenberger, Jane and Brenninkmeyer, Gerald and Bozkurt, Alper and et al.}, year={2023} }
 @article{martin_roberts_bozkurt_2023, title={Toward In-the-Field Canine Manifold Learning: Data Fusion for Evaluation of Potential Guide Dogs}, DOI={10.1145/3637882.3637898}, journal={TENTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2023}, author={Martin, Devon and Roberts, David and Bozkurt, Alper}, year={2023} }
 @article{reynolds_williams_martin_readling_ahmmed_huseth_bozkurt_2022, title={A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments}, volume={22}, ISSN={["1424-8220"]}, url={https://doi.org/10.3390/s22155582}, DOI={10.3390/s22155582}, abstractNote={Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.}, number={15}, journal={SENSORS}, author={Reynolds, James and Williams, Evan and Martin, Devon and Readling, Caleb and Ahmmed, Parvez and Huseth, Anders and Bozkurt, Alper}, year={2022}, month={Aug} }
 @article{pearson_stewart-ginsburg_malone_manns_martin_sturdivant_2022, title={Best FACES Forward: Outcomes of an Advocacy Intervention for Black Parents Raising Autistic Youth}, ISSN={["1532-7035"]}, url={https://doi.org/10.1080/09362835.2022.2100392}, DOI={10.1080/09362835.2022.2100392}, abstractNote={ABSTRACT Despite increased diagnostic prevalence, Black parents raising autistic youth still experience additional and unique barriers to accessing and using autism-related services compared to their non-Black peers. Increasing parent advocacy capacity may be one way to reduce these disparities. This efficacy study examined the effects of the FACES advocacy program on advocacy capacity for Black parents raising autistic youth. Authors used a quasi-experimental research design that compared pretest and posttest measures for 16 Black parents raising autistic youth. Intervention participants demonstrated increases in family empowerment, school communication, and perceptions of advocacy ability. Findings offer emergent evidence of advocacy programs for Black families raising autistic youth.}, journal={EXCEPTIONALITY}, author={Pearson, Jamie N. and Stewart-Ginsburg, Jared H. and Malone, Kayla and Manns, Lonnie and Martin, DeVoshia Mason and Sturdivant, Danyale}, year={2022}, month={Jul} }
 @article{martin_holder_nichols_park_roberts_bozkurt_2022, title={Comparing Accelerometry and Depth Sensing-Based Computer Vision for Canine Tail Wagging Interpretation}, DOI={10.1145/3565995.3566025}, abstractNote={This paper presents a preliminary effort to evaluate alternative sensing modalities for automated, high-resolution tracking of dog tail position and movement as a behavioral communication tool. We compare two different methods: (1) inertial measurement devices placed on dog outfits, and (2) remotely positioned cameras supported with custom vision-based tail wag detection algorithms. The small size and non-invasiveness of the inertial sensors and the non-contact and remote nature of the camera system both promote subject comfort and continuous signal acquisition while not affecting the mechanics of dog tail movement. The preliminary findings support that the higher-resolution and continuous interpretations on the dog tail movements and positions can pave the way for assessing their emotional states and designing more appropriate training and play environments.}, journal={NINTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2022}, author={Martin, Devon and Holder, Timothy and Nichols, Colt and Park, Jeremy and Roberts, David and Bozkurt, Alper}, year={2022} }
 @article{martin_reynolds_daniele_lobaton_bozkurt_2021, title={Towards Continuous Plant Bioimpedance Fitting and Parameter Estimation}, ISSN={["1930-0395"]}, url={http://dx.doi.org/10.1109/sensors47087.2021.9639492}, DOI={10.1109/SENSORS47087.2021.9639492}, abstractNote={The push to advance artificial intelligence, internet of things, and big data analysis all pave the way to automated and systematic optimization in precision agriculture and smart farming applications. These advancements lead to many benefits, including the optimization of primary production, prevention of spoilage via supply chain management, and detection of crop failure risk. Noninvasive impedance sensors serve as a promising candidate for monitoring plant health wirelessly and play a major role in this optimization problem. In this study, we developed a software pipeline to support impedance sensing applications and, as a proof of concept, applied this to track longitudinal consistent bioimpedance data from the V4 leaf midrib in maize plants. The script uses the single-shell equivalent circuit model to represent the extracellular fluid, cellular membrane, and intracellular fluid as a simplified resistive-capacitive circuit, where these elements’ parameters are estimated with complex nonlinear least squares. The double-shell model extends the single-shell model to account for the effects of the relatively large plant cell vacuole. Limit cases for impedance are utilized for specific parameters as an alternative method of estimation. We investigated a complex analysis-based modification to the objective function and model optimization for the data pipeline automation. Various weighing functions are applied and checked against one another. Additionally, a custom graphical user interface was developed to assist with parameter initialization for correcting potential convergence issues and understating the influence of each parameter on the dataset. We demonstrated that the analysis of an example longitudinal dataset was able to reveal a time series for parameter fitting.}, journal={2021 IEEE SENSORS}, publisher={IEEE}, author={Martin, Devon and Reynolds, James and Daniele, Michael and Lobaton, Edgar and Bozkurt, Alper}, year={2021} }