Vision Controlled Orthotic Hand Exoskeleton

This paper presents the design and implementation of an AI vision-controlled orthotic hand exoskeleton to enhance rehabilitation and assistive functionality for individuals with hand mobility impairments. The system leverages a Google Coral Dev Board Micro with an Edge TPU to enable real-time object detection using a customized MobileNet\_V2 model trained on a six-class dataset. The exoskeleton autonomously detects objects, estimates proximity, and triggers pneumatic actuation for grasp-and-release tasks, eliminating the need for user-specific calibration needed in traditional EMG-based systems. The design prioritizes compactness, featuring an internal battery. It achieves an 8-hour runtime with a 1300 mAh battery. Experimental results demonstrate a 51ms inference speed, a significant improvement over prior iterations, though challenges persist in model robustness under varying lighting conditions and object orientations. While the most recent YOLO model (YOLOv11) showed potential with 15.4 FPS performance, quantization issues hindered deployment. The prototype underscores the viability of vision-controlled exoskeletons for real-world assistive applications, balancing portability, efficiency, and real-time responsiveness, while highlighting future directions for model optimization and hardware miniaturization.

A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities

This paper introduces a novel AI vision-enabled pediatric prosthetic hand designed to assist children aged 10-12 with upper limb disabilities. The prosthesis features an anthropomorphic appearance, multi-articulating functionality, and a lightweight design that mimics a natural hand, making it both accessible and affordable for low-income families. Using 3D printing technology and integrating advanced machine vision, sensing, and embedded computing, the prosthetic hand offers a low-cost, customizable solution that addresses the limitations of current myoelectric prostheses. A micro camera is interfaced with a low-power FPGA for real-time object detection and assists with precise grasping. The onboard DL-based object detection and grasp classification models achieved accuracies of 96% and 100% respectively. In the force prediction, the mean absolute error was found to be 0.018. The features of the proposed prosthetic hand can thus be summarized as: a) a wrist-mounted micro camera for artificial sensing, enabling a wide range of hand-based tasks; b) real-time object detection and distance estimation for precise grasping; and c) ultra-low-power operation that delivers high performance within constrained power and resource limits.

Autonomous Navigation System from Simultaneous Localization and Mapping

This paper presents the development of a Simultaneous Localization and Mapping (SLAM) based Autonomous Navigation system. The motivation for this study was to find a solution for navigating interior spaces autonomously. Interior navigation is challenging as it can be forever evolving. Solving this issue is necessary for multitude of services, like cleaning, the health industry, and in manufacturing industries. The focus of this paper is the description of the SLAM-based software architecture developed for this proposed autonomous system. A potential application of this system, oriented to a smart wheelchair, was evaluated. Current interior navigation solutions require some sort of guiding line, like a black line on the floor. With this proposed solution, interiors do not require renovation to accommodate this solution. The source code of this application has been made open source so that it could be re-purposed for a similar application. Also, this open-source project is envisioned to be improved by the broad open-source community upon past its current state.