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Personalizing exoskeleton assistance while walking in the real world.

Patrick Slade^1,2, Mykel J Kochenderfer³, Scott L Delp^1,2

¹Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.

|October 12, 2022

Summary

Optimizing personalized exoskeleton assistance outdoors using wearable sensors is faster and as effective as lab methods. This approach enhances walking speed and reduces energy consumption during naturalistic movement.

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Area of Science:

Biomechanics
Robotics
Human-Computer Interaction

Background:

Personalized exoskeleton assistance significantly improves walking speed and energy economy.
Current optimization methods require lengthy laboratory tests under unnatural conditions.

Purpose of the Study:

To develop and validate a rapid, real-world method for optimizing personalized ankle exoskeleton assistance.
To demonstrate that outdoor optimization using wearable sensors is effective and faster than traditional laboratory approaches.

Main Methods:

Designed a portable ankle exoskeleton informed by laboratory testbed insights.
Developed a data-driven, outdoor optimization method using wearable sensors and naturalistic walking data.
Collected data during short walking bouts at varying speeds in a public setting.

Main Results:

Outdoor optimization was equally effective as laboratory methods but identified optimal parameters four times faster.
Assistance optimized outdoors increased self-selected walking speed by 9% and reduced travel energy by 17%.
Optimized assistance reduced metabolic energy consumption by 23% during treadmill walking at 1.5 m/s.

Conclusions:

Exoskeleton optimization can be performed rapidly and effectively under real-world conditions.
Wearable sensors and data-driven methods enable efficient personalization of assistive devices.
Human movement data is key to enhancing performance with personalized assistive devices.