BirdLab

🦿 Enhancing Human Mobility Through Intelligent Robotic Assistance 🤖

We’re proud to share another achievement from our BiRDLab team! 🎉
Our members Luís Moreira, Joana Figueiredo, and our PI Cristina Santos have published their latest work in IEEE Transactions on Medical Robotics and Bionics:
📘 “Real-Time Control of Ankle Orthosis Assistance Using a Locomotion Mode Prediction Tool.”

This study presents an innovative 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗹𝗼𝗰𝗼𝗺𝗼𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲 (𝗟𝗠) 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹 designed to enhance robotic assistive devices - enabling them to anticipate user movements and adapt assistance accordingly, even at the slow walking speeds typical of neurologically impaired users.

Using 𝘁𝗵𝗶𝗴𝗵 𝗮𝗻𝗱 𝘀𝗵𝗮𝗻𝗸 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 𝗮𝗻𝗴𝗹𝗲𝘀 as inputs to a 𝗟𝗼𝗻𝗴 𝗦𝗵𝗼𝗿𝘁-𝗧𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗟𝗦𝗧𝗠) model, the system predicts four locomotion modes — standing, level-ground walking, stair descent, and stair ascent — with impressive 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝗶𝗲𝘀 𝗼𝗳 𝟵𝟴.𝟲% for able-bodied and 𝟵𝟳.𝟮% for stroke participants.

Integrated into the 𝗦𝗺𝗮𝗿𝘁𝗢𝘀 𝘀𝘆𝘀𝘁𝗲𝗺, it adjusts 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗮𝗯𝗼𝘂𝘁 𝟯𝟬𝟬 𝗺𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 a new movement begins, offering smoother, safer, and natural support.

This work represents a significant step toward adaptive, user-centered robotic assistance for stroke rehabilitation and mobility enhancement. 👏

Congratulations to the authors for this outstanding contribution to biomedical engineering and human-robot interaction!

🔗 Read the full article here: https://ieeexplore.ieee.org/document/11145177

Real-Time Control of Ankle Orthosis Assistance Using a Locomotion Mode Prediction Tool | IEEE Journals & Magazine | IEEE Xplore Robotic assistive devices have been equipped with locomotion mode (LM) decoding tools to adapt their assistance according to the users locomotion needs. However