Real-time gait event detection for lower limb amputees using a single wearable sensor.

Area of Science:

• Biomechanics
• Rehabilitation Engineering
• Wearable Technology

Background:

• Accurate real-time gait event detection is crucial for lower limb amputee rehabilitation and prosthetic control.
• Existing methods may have limitations in accuracy or real-time applicability.

Purpose of the Study:

• To develop and validate a rule-based real-time gait event/phase detection system (R-GEDS).
• To utilize a shank-mounted inertial measurement unit (IMU) for detecting gait events during level ground walking in lower limb amputees.

Main Methods:

• Algorithm development based on shank angular velocity and linear acceleration.
• System evaluation with control subjects (CS) and a transfemoral amputee (TFA).
• Validation using FlexiForce footswitches (FSW) for comparison.

Main Results:

• High detection accuracy (99.78%) for all gait events.
• Data latency for initial contact (IC) and toe off (TO) within ± 40 ms.
• Varied detection times for foot-flat start (FFS) and heel-off (HO) between CS and TFA, particularly on the prosthetic side.

Conclusions:

• The R-GEDS system demonstrates high accuracy and real-time capabilities for gait event detection.
• The system shows potential for application in gait analysis and lower limb prosthesis control.
• Kinematic differences between amputees and control subjects influence detection timing variations.