Optimal Foot Location for Placing Wearable IMU Sensors and Automatic Feature Extraction for Gait Analysis

• Published in: IEEE sensors journal Vol. 18; no. 6; pp. 2555 - 2567
• Main Authors: Anwary, Arif Reza, Hongnian Yu, Vassallo, Michael
• Format: Journal Article
• Language: English
• Published: IEEE 15.03.2018
• Subjects: accelerometer; Accelerometers; Feature extraction; Foot; gait analysis; gyroscope; Gyroscopes; Inertial measurement unit (IMU); optimal location; sensor placement; Smart phones; Wearable sensors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2017.2786587

Our aim is to maximize the interpretable information for gait analysis. To achieve this, it is important to find the optimal sensor placement and the parameters that influence the extraction of automatic gait features. We investigated the effect of different anatomical foot locations on inertial measurement unit (IMU) sensor output. We designed and developed an android app to collect real-time synchronous sensor output. We selected a set of five anatomical foot locations covering most of the foot regions to place wearable IMU sensors for data collection. Each participant performed a trial in a straight corridor comprising 25 strides of normal walking, a turn-around, and another 25 strides. We proposed an automatic gait features extraction method to analyze the data for stride number, distance, speed, length and period of stride, stance, and swing phases during walking. The highest accuracy for detecting stride number was in location 1 (first cuneiform) followed by location 5 (Achilles Tendon) and 4 (Talus). Location 1 was the closest to correlate estimate to the measured distance travelled. The accuracy of detecting number of strides on average is 95.47% from accelerometer data and 93.60% from gyroscope data and closest to the 60:40% split for average stance and swing for 15 subjects. To validate our results, we conducted trials using the Qualisys motion capture instrument and from our sensors concurrently. The average accuracy of the result is 97.77% with 95% confidence interval 0.767 for estimated and 99.01% with 95% confidence interval 0.266 for period.