This project presents an autonomous walker designed for individuals with mobility limitations. It utilizes a navigation stack built on Robot Operating System (ROS) for self-directed movement guided by user-specified goals through a multimodal interface combining LiDAR, sound source localization, and computer vision. Additionally, the walker offers a mechanism for sit-to-stand mobility assistance and provides active user guidance through an array of force-sensing resistors. We successfully implemented and configured the ROS stack, fabricated a functional prototype, and conducted structural analysis. Future work focuses on fine-tuned navigation, user studies, and clinical trials to evaluate the system’s potential for real-world application, promoting independence and mobility for users.

This project builds on creating a Remote Monitoring Robot for Gait Rehabilitation, adopting a patient-centered approach to robot-assisted gait training while empowering remote clinical oversight. Our system integrates two key elements: One being Rhythmic Auditory Stimulation, which employs music and rhythm to enhance gait patterns in individuals with gait disorders, aligning with physiotherapy objectives. The pther being Remote Gait Monitoring which utilizes wearable IMUs for real-time gait data transmission to medical professionals. This enables informed remote care delivery and reduces the necessity for frequent in-person consultations. Crucially, the system provides direct feedback to the user for gait improvement, enabling self-guided training even in the absence of external monitoring.

This work presents a Patient Assistive Mobile Robot equipped with robust summoning and navigation capabilities. Our system addresses the crucial need for assistive robots to respond reliably to patients’ requests, even in scenarios involving out-of-sight calls. To achieve this, we integrate three core technologies: 1) LiDAR-based SLAM for accurate localization and mapping, providing a foundational spatial understanding; 2) Sound source localization to determine the direction of calls from outside the robot’s line-of-sight; and 3) Stereo vision-based gesture recognition and depth estimation, enabling intuitive interaction when the patient is visible. This multifaceted approach ensures that our Patient Assistive Mobile Robot can navigate complex environments and respond effectively to a broader range of summoning methods, enhancing its utility and dependability in healthcare settings.

This novel microgripper design utilizes electrothermal actuators, specifically U-shaped and V-shaped configurations, to achieve controlled grasping and releasing actions. Traditional thermally actuated microgrippers often struggle with releasing grasped objects, prompting the incorporation of an active release mechanism. This mechanism enables controlled release without external forces, crucial for preserving the integrity of delicate samples.