@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{wu_2023, title={Behavior Analysis for Potential Guide Dogs}, DOI={10.1145/3637882.3637900}, journal={TENTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2023}, author={Wu, Yifan}, year={2023} }
 @article{wu_holder_foster_williams_enomoto_lascelles_bozkurt_roberts_2022, title={Spatial and Temporal Analytic Pipeline for Evaluation of Potential Guide Dogs Using Location and Behavior Data}, url={https://doi.org/10.1145/3565995.3566033}, DOI={10.1145/3565995.3566033}, abstractNote={Training guide dogs for visually-impaired people is a resource-consuming task for guide dog schools. This task is further complicated by a dearth of capabilities to objectively measure and analyze candidate guide dogs’ temperaments as they are placed with volunteer raisers away from guide dog schools for months during the raising process. In this work, we demonstrate a preliminary data analysis workflow that is able to provide detailed information about candidate guide dogs’ day to day physical exercise levels and gait activities using objective environmental and behavioral data collected from a wearable collar-based Internet of Things device. We trained and tested machine learning models to analyze different gait types including walking, pacing, trotting and mixture of walk and trot. By analyzing data both spatially and temporally, a location and behavior summary for candidate dogs is generated to provide insight for guide dog training experts, so that they can more accurately and comprehensively evaluate the future success of the candidate. The preliminary analysis revealed movement patterns for different location types which reflected the behaviors of candidate guide dogs.}, journal={NINTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2022}, author={Wu, Yifan and Holder, Timothy and Foster, Marc and Williams, Evan and Enomoto, Masataka and Lascelles, B. Duncan X. and Bozkurt, Alper and Roberts, David L.}, year={2022} }