Authors:
(1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA;
(2) 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;
(3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA;
(4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy;
(5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA;
(6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA;
(7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
Table of Links
V. Conclusion, Acknowledgment, and References
V. CONCLUSION
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. Evaluation of our model’s closed-loop performance versus typical sensors
(i.e., force plates, FSR footplates) highlights the system’s applicability across new users and several walking speeds and conditions. To address our model limitations, future work could include training datasets encompassing various walking speeds, overground walking and other activities (e.g. ramps, stairs). Furthermore, this work could be used to implement a state machine that utilizes an estimated stance interpolation factor, selecting impedance parameters in the mid-level controller for exoskeleton control and tailored assistance during walking. The proposed framework will be validated in future on additional healthy individuals as well as individuals with lower-limb impairments (e.g., stroke, spinal cord injury).
ACKNOWLEDGMENT
This work was supported by the National Science Foundation / National Robotics Initiative (Grant No: 2024488). We would like to thank Tim Haswell for his technical support on the hardware improvements of the ExoMotus-X2 exoskeleton.
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