Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors.

Area of Science:

• Biomechanics
• Wearable Technology
• Sensor Fusion

Background:

• Accurate estimation of walking distance is crucial for health monitoring and rehabilitation.
• Existing methods often rely on GPS or complex algorithms, which can be inaccurate or require external devices.

Purpose of the Study:

• To develop and validate a novel method for precise walking distance estimation using insole-based stride counting.
• To assess the accuracy of this method across different walking distances.

Main Methods:

• Utilized insole sensors, including a triaxial accelerometer and eight pressure sensors, to capture detailed movement data.
• Developed algorithms to filter noise and identify stance and swing phases from sensor data.
• Counted walking strides based on phase information to estimate total distance traveled.

Main Results:

• Achieved low mean error rates of 4.8% for short distances (16 m) and 3.1% for long distances (89 m).
• Demonstrated high precision in determining swing phase timings, with maximum differences of 0.08 s and 0.06 s for start and stop points, respectively.
• Validated the method's accuracy with healthy participants across two distinct walking databases.

Conclusions:

• The proposed insole sensor-based stride counting method provides a highly accurate and precise approach to estimating walking distance.
• This technology holds potential for reliable, unobtrusive personal mobility monitoring.