Chicaiza Claudio, Fernando
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Chicaiza Claudio, Fernando
Main Affiliation
Ambato

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Automated Mouth State Recognition for Robotic Feeding Assistance

2025 , Samuel Peña , Viviana Moya , Chicaiza Claudio, Fernando , Danilo Chávez , Juan Pablo Vásconez , Andrea Pilco

Feeding poses a significant challenge for patients with limited upper limb mobility. A robotic assistant equipped with the capability to detect a patient's mouth and deposit food can effectively support the user during feeding. Since each patient exhibits unique physical characteristics, it is essential to develop an automated system capable of accurately identifying the mouth's location. This allows the definition of a precise three-dimensional target point where a robotic manipulator can deliver food. Although various techniques are available for facial feature detection, some demonstrate notable advantages in specific applications. Considering the constraints of limited processing capacity, we propose the use of Facemesh to identify the patient's point of interest, specifically the mouth. This technique enables the determination of the precise location for food delivery. To ensure the robot can reach both the patient's mouth and the food container, an inverse kinematics-based controller is implemented. The system's performance is evaluated, demonstrating the effectiveness of integrating the patient into the control loop for seamless operation.

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Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment

2025 , Viviana Moya , Michael Guerra , Karina Pazmiño , Faruk Abedrabbo , Chicaiza Claudio, Fernando , David Pozo-Espín

This study presents the design and implementation of an automated system for sorting and measuring kidney tomatoes using a YOLOv8 model with a size estimation algorithm. The proposed system integrates computer vision and deep learning with a physical sorting mechanism to categorize tomatoes into three classes: green, red, and damaged, while also determining their size. The classification model was trained on a dataset of 2,145 images of tomatoes taken from different sources and lighting conditions to enhance performance during training. The implemented prototype consists of a conveyor belt equipped with sensors and a high-resolution camera to capture and analyse tomato characteristics in real-time. A servo-driven sorting mechanism then directs the classified tomatoes into their respective bins. Experimental validation and testing show that the model achieves a classification accuracy of 99.6% and a size estimation accuracy of 97.1%, aiding in a reliable and efficient post-harvest sorting process. The proposed system not only reduces the probability of human error but also improves the precision of tomato classification. Future developments will focus on refining and adapting existing AI methodologies to improve their effectiveness in agricultural environments. This includes enhancing model robustness, improving classification accuracy under real-world conditions, and tailoring AI tools to better meet the demands of industrial tomato sorting

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Bilateral Rate/Position Delayed Teleoperation Control for UAVs: A Performance Evaluation

2025 , Chicaiza Claudio, Fernando , Emanuel Slawiñski , Viviana Moya , Carvajal, Christian , Varela Aldas, José , Ayala-Chauvin, Manuel Ignacio

This paper introduces a bilateral teleoperation system for UAVs that employs a hybrid control scheme combining rate and non-linear position modes. By continuously switching between these modes, the system achieves both agile manoeuvring and precise positioning under communication delays. Validation is carried out using a dynamic model for the master robot with a Novint Falcon haptic device and a simplified model for the slave robot in Gazebo-ROS2. Performance metrics including task completion time, mean squared error, and force feedback demonstrate enhanced stability and efficiency, suggesting promising applications in inspection, environmental monitoring and search and rescue

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