MS, University of Cincinnati, 2022, Engineering and Applied Science: Mechanical Engineering

This project is based on a request from the Kroger grocery store to automate its fresh meat department. The project is divided into three parts: first, Catia modeling the fresh meat department and designing two robots based on UR10 robotic arms and four-wheeled electric vehicles. In the second part, the reinforcement learning algorithm, Q-learning, is applied in the path planning of the robot path calculation. In the third part, the human-robot interaction system uses an interface developed by OpenCV and Mediapipe's hand recognition package. Simulation and practical operation are performed in this project for these three parts. The simulation results and calculation results verify the feasibility of the design. The robot can pick, pack, label, and replenish tasks. The human-robot interaction interface can clearly distinguish the customer's needs. The robustness of the Q-learning algorithm for path planning meets the expected standard and can complete the path planning in a short time. The paper concludes with a market price analysis and a comparative analysis of the profitability of this project.

Committee: Janet Jiaxiang Dong Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member); Xiaodong Jia Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy (PhD), Wright State University, 2022, Computer Science and Engineering PhD

Activities of Daily Living (ADL's) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL's has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing 's based on video collected from a wearable camera. Most in-home activities are naturally defined by interaction with objects. We leverage these object-centric activity definitions to develop a set of rules for a Fuzzy Inference System (FIS) that uses video features and the identification of objects to identify and classify activities. Further, we demonstrate that the use of FIS enhances the reliability of the system and provides enhanced explainability and interpretability of results over popular machine-learning classifiers due to the linguistic nature of fuzzy systems.

Committee: Tanvi Banerjee Ph.D. (Advisor); Yong Pei Ph.D. (Committee Member); Michael Riley Ph.D. (Committee Member); Mateen Rizki Ph.D. (Committee Member); Thomas Wischgoll Ph.D. (Committee Member) Subjects: Computer Science

Master of Science, University of Toledo, 2020, Engineering (Computer Science)

The objective of this research is to develop a fully functional semi-automated monitoring and verification system to improve the quality standards in compounding sterile preparations (CSP). To avoid the errors made in the CSP process, a material selection graphical user interface (MSGUI) is integrated with a video processing system (VPS) that provide in-process feedback to the pharmacist preparing a medication order (MO) in the work surface of a laminar airflow workbench (LAFW). A hand gesture-based monitoring and verification (HGMV) system is developed on deep learning technology that helps in monitoring as well as verification of the process using different types of hand-gestures. A barcode enabled product verification (BEPV) technique is also developed and integrated with a compounding database which helps in selecting correct products to be used in CSP. The complete model also includes some other important verification and monitoring features such as a video recording process (VRP) that is used to track all the steps performed in completing a MO, image capturing in between the process, and electronic documentation of all the products used in the process as well as important events that occurred while doing CSP. The developed system was tested for different scenarios that a pharmacist can face in CSP, and the final version of the model was found to be of the highest accuracy. The BEPV and HGMV were modified based on the results from the initial phase of testing, and the final version was highly robust and efficient. Mistakes were made deliberately at the testing phase, and the results matched the expected output. The compounding sterile preparations monitoring and verification system (CSPTVS) provides a cost-effective solution that is capable of improving the quality standards in the field of pharmacy by complete monitoring of the process and providing real-time in-process feedback to the pharmacist while reducing wastage of wrongly-selected products.

Committee: Vijay Devabhaktuni (Committee Chair); Jerry Nesamony (Committee Co-Chair); Ahmad Javaid (Committee Member); Weiqing Sun (Committee Member) Subjects: Artificial Intelligence; Computer Engineering; Computer Science; Pharmaceuticals

Master of Science, The Ohio State University, 2016, Electrical and Computer Engineering

Imitation and evaluation procedure is important for ASL learning and teaching. However, the current online ASL learning resources do not provide affordable and convenient imitation-evaluation function. To solve this problem, we propose an Automatic American Sign Language Imitation Evaluator (AASLIE) to evaluate the hand movement in the imitation. The proposed AASLIE system extracts 3D trajectory of the centroid of the hand by first applying a two-stage algorithm for 2D hand detection and tracking allowing possible hand-face overlaps. The 3D trajectory is extracted using a Structure from Motion algorithm with the point correspondences calculated from minimizing an affine transformation. The evaluation contains two parts, recognition and quantitative evaluation, for giving more sensitive feedback than the current sign language recognition systems. The recognition is achieved by a classification algorithm. The quantitative evaluation score, which indicates the goodness of imitation, is given by a weighted sum of point-wise distance between the imitation trajectory and the standard trajectory. Experiments were conducted for testing the recognition and quantitative evaluation functionality proposed in the system. The results show that the AASLIE system recognizes the trajectories with an average accuracy 0.8581 (±0.05) and the score accurately captures the different levels of goodness of imitation.

Committee: Aleix Martinez PhD (Advisor); Yuejie Chi PhD (Committee Member) Subjects: Computer Science; Electrical Engineering