Sensor systems for lower limb functional electrical stimulation (FES) control

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Wearable Sensors

Background:

• Gait impairment significantly affects individuals with paraplegia, necessitating advanced assistive technologies.
• Accurate real-time monitoring of joint kinematics is crucial for developing effective control strategies for functional electrical stimulation (FES).
• Existing sensor systems often face challenges with size, cabling, and real-time processing capabilities.

Purpose of the Study:

• To design and evaluate novel wearable sensor systems for monitoring gait and joint angles in individuals with paraplegia.
• To assess the systems' ability to predict incipient knee buckles and measure knee and hip flexion during sit-to-stand movements.
• To determine the feasibility of using these sensor systems for real-time FES control.

Main Methods:

• Developed two sensor systems with integrated accelerometers, magnetic sensors, gyroscopes, and strain gauges, placed at the belt/AFO or AFO/thigh.
• Utilized a flexible wire bus to minimize cabling effects, with maximum cluster size of 14 cm³ and 75 g.
• Employed a threshold method for knee buckle prediction and analytical/neural network methods for joint angle measurement.

Main Results:

• Sensors detected five phases of normal gait with 40 ms resolution in able-bodied individuals.
• Successfully predicted incipient knee buckles in a paraplegic individual with a minimum lead time of 30 ms.
• Achieved accuracy of 3.2 degrees for knee flexion and 3.8 degrees for knee/hip flexion during sit-to-stand trials.

Conclusions:

• The designed sensor systems demonstrate high accuracy and responsiveness for gait analysis and joint angle measurement.
• The systems show potential for real-time prediction of gait deviations like knee buckles.
• Feasibility for real-time control of FES in paraplegic individuals is suggested, paving the way for improved mobility solutions.