Weight Distribution Estimation in Lower-Limb Exoskeletons via Deep Learning: Conclusion

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Explore deep learning for real-time weight distribution estimation in lower-limb exoskeletons, enhancing control without additional sensors.

Authors: Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna,...

This work was supported by the National Science Foundation / National Robotics Initiative . We would like to thank Tim Haswell for his technical support on the hardware improvements of the ExoMotus-X2 exoskeleton. In this study, we demonstrated the feasibility of employing deep learning in gait state detection for exoskeleton control, eliminating the necessity of ground reaction force sensors. Despite challenges such as a limited dataset, realtime constraints, and issues related to error propagation, these obstacles were successfully addressed.

 

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