Strike index estimation using a convolutional neural network with a single, shoe-mounted inertial sensor.

Strike index is a measurement of the center of pressure position relative to the foot length, and it is regarded as a gold standard in classifying strike pattern in runners. However, strike index requires sophisticated laboratory equipment, e.g., force plates and optical motion capture. We present a method of estimating strike index using data from a shoe-mounted inertial measurement unit (IMU) analyzed by a participant-independent convolutional neural network (CNN), which consists of convolutional, max-pooling, and fully-connected layers. To promote data variability, 16 participants were required to land with three strike patterns (rearfoot, midfoot, and forefoot strike) while running on an instrumented treadmill in four conditions i.e., two footwear types and two running speeds. Using the proposed approach, strike index was estimated with a root mean square error of 6.9% and a R2 of 0.89. Training and testing the model with different variations of the data collected showed that the model was robust to changes in speed. The proposed approach enables accurate estimation of strike index outside of traditional gait laboratories. This solution potentially improves running performance and reduces injury risk in distance runners.

Accurate Impact Loading Rate Estimation During Running via a Subject-Independent Convolutional Neural Network Model and Optimal IMU Placement.

OBJECTIVE: Enable accurate estimation of vertical average loading rate (VALR) in runners with one or more wearable inertial measurement units (IMUs). METHODS: A subject-independent convolutional neural network (CNN) model was developed to estimate VALR from wearable IMUs. Fifteen runners wore IMUs at the trunk, pelvis, thigh, shank, and foot and ran on an instrumented treadmill for combinations of the following conditions: foot-strike (forefoot, mid-foot, rear-foot), step rate (90% to 110% of baseline), running speed (2.4 m/s and 2.8 m/s) and footwear (standard and minimalist running shoes). Thirty-one IMU placement configurations with combinations of one to five IMUs were evaluated. VALR estimations from the wearable IMUs were compared with force-plate VALR measurements. RESULTS: VALR estimations via the subject-independent CNN model with a single shank-worn IMU were highly correlated (ρ = 0.94) with force-plate VALR measurements and were substantially higher than previously reported peak tibial acceleration correlations with force-plate VALR measurements from shank-worn accelerometers (ρ = 0.44-0.66). Correlation results from the CNN model for a single IMU placed at the foot, pelvis, trunk, and thigh were ρ = 0.91, 0.76, 0.69, and 0.65, respectively. There was no improvement in accuracy from the shank-worn IMU when adding 1-4 additional IMUs from the trunk, pelvis, thigh, or foot. CONCLUSION: The proposed subject-independent CNN model with a single shank-worn IMU provides more accurate estimation of VALR than previous wearable sensing approaches. SIGNIFICANCE: This could enable runners to more accurately assess impact loading rates and potentially provide insights into running-related injury risk and prevention.

Magnetometer-Free, IMU-Based Foot Progression Angle Estimation for Real-Life Walking Conditions.

Foot progression angle (FPA) is vital in many disease assessment and rehabilitation applications, however previous magneto-IMU-based FPA estimation algorithms can be prone to magnetic distortion and inaccuracies after walking starts and turns. This paper presents a foot-worn IMU-based FPA estimation algorithm comprised of three key components: orientation estimation, acceleration transformation, and FPA estimation via peak foot deceleration. Twelve healthy subjects performed two walking experiments to evaluation IMU algorithm performance. The first experiment aimed to validate the proposed algorithm in continuous straight walking tasks across seven FPA gait patterns (large toe-in, medium toe-in, small toe-in, normal, small toe-out, medium toe-out, and large toe-out). The second experiment was performed to evaluate the proposed FPA algorithm for steps after walking starts and turns. Results showed that FPA estimations from the IMU-based algorithm closely followed marker-based system measurements with an overall mean absolute error of 3.1±1.3 deg, and the estimation results were valid for all steps immediately after walking starts and turns. This work could enable FPA assessment in environments where magnetic distortion is present due to ferrous metal structures and electrical equipment, or in real-life walking conditions when walking starts, stops, and turns commonly occur.