Improving Walking Assistance Efficiency in Real-World Scenarios with Soft Exosuits Using Locomotion Mode Detection

Area of Science:

• Biomechanics
• Robotics
• Assistive Technology

Background:

• Wearable assistive devices enhance locomotion efficiency by reducing metabolic cost.
• Adaptive assistance is crucial for optimizing performance across various movement patterns.

Purpose of the Study:

• Develop a novel control strategy for an underactuated soft exosuit with a single actuator for hip flexion assistance.
• Utilize inertial measurement units (IMUs) to differentiate between level walking, stair ascent, and stair descent.
• Enable real-time adjustment of walking assistance to maximize user support.

Main Methods:

• Implemented a kinematics-based IMU classification strategy to identify locomotion modes.
• Integrated the control strategy into a soft exosuit designed for bilateral hip flexion assistance.
• Conducted preliminary testing with four healthy subjects comparing exosuit-disabled and exosuit-enabled conditions.

Main Results:

• The IMU classification strategy achieved over 95% accuracy in distinguishing between the three locomotion modes.
• Subjects experienced an average reduction of 10.1% in walking energy expenditure when using the exosuit.
• The system demonstrated real-time adaptability to different walking conditions.

Conclusions:

• The developed control strategy effectively adapts exosuit assistance to diverse locomotion modes.
• This technology offers a promising advancement in compact, high-performance lower limb assistive devices.
• The findings support the practical application of adaptive wearable robotics for improving mobility.