Measurement agreement between a newly developed sensing insole and traditional laboratory-based method for footstrike pattern detection in runners

Abstract

This study introduced a novel but simple method to continuously measure footstrike patterns in runners using inexpensive force sensors. Two force sensing resistors were firmly affixed at the heel and second toe of both insoles to collect the time signal of foot contact. A total of 109 healthy young adults (42 males and 67 females) were recruited in this study. They ran on an instrumented treadmill at 0°, +10°, and -10° inclinations and attempted rearfoot, midfoot, and forefoot landings using real time visual biofeedback. Intra-step strike index and onset time difference between two force sensors were measured and analyzed with univariate linear regression. We analyzed 25,655 footfalls and found that onset time difference between two sensors explained 80–84% of variation in the prediction model of strike index (R-squared = 0.799–0.836, p<0.001). However, the time windows to detect footstrike patterns on different surface inclinations were not consistent. These findings may allow laboratory-based gait retraining to be implemented in natural running environments to aid in both injury prevention and performance enhancement.

Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

“…This lack of dichotomy may be a result of speed or flight time inconsistencies during MF strike pattern performance, which is supported by the fact that the MF condition was the most difficult condition for participants to perform [37]. However, the apparent stratification of the independent variables for each strike condition thus confirms the applicability of the fore/aft Loadsol TM sensors to estimate FSA and FSP [26,27]. Supporting this, the MR and FRST PRED models developed for the prediction of FSA were both evidently good fits (MR = 91.4% and FRST PRED = 95.42% of variance explained) and the classification accuracy of FSP for all statistical techniques was greater than 90% (Table 4B).…”

“…The linear approach of the MR as suggested by Fritz and colleagues [ 27 ] appears to be appropriate to generally explain the variance of the FSA (R 2 = 0.914). In a similar application, a univariate linear regression to determine strike index via the onset time difference of a fore and aft pressure sensor resulted in a lower coefficient of determination (R 2 = 0.836) [ 26 ]. Although participants were asked to perform RF, MF, and FF foot strikes, Cheung and colleagues [ 26 ] did not carry out further analyses to confirm the performance of the FSPs or if there was a stratified model fit.…”

“…In a similar application, a univariate linear regression to determine strike index via the onset time difference of a fore and aft pressure sensor resulted in a lower coefficient of determination (R 2 = 0.836) [ 26 ]. Although participants were asked to perform RF, MF, and FF foot strikes, Cheung and colleagues [ 26 ] did not carry out further analyses to confirm the performance of the FSPs or if there was a stratified model fit. Importantly, a strong linear relationship between the strike index and 3D FSA kinematics is supported in literature, however the relationship appears to be driven primarily by FF and RF strike types [ 14 , 16 ].…”

“…The wearable application of pressure insoles already extends to temporal gait events [24,25], therefore they may be a plausible alternative to IMUs to facilitate ecological kinematic estimation while also enabling a valid measurement of vertical force (Fong et al, 2008). Importantly, Loadsol TM wearable insoles can measure vertical force in the rear and fore foot separately; thus the time and force relationships of the fore and aft sensors may provide enough information for FSA prediction and FSP classification [26,27]. Further, the separation of the insole into multiple components is encouraged by the assumption that the foot is not a singularly rigid segment as was traditionally considered [28,29].…”

Use of Wearables: Tracking and Retraining in Endurance Runners

“…Dorschky and colleagues 37 As such, very few kinematics that can be readily determined using wearable sensors have strong relationships with running economy or injury. For instance, advancements have made it possible to assess the foot strike pattern used by a runner through an instrumented insole (38). Yet, a recent review recommended that altering foot strike is unlikely to reduce metabolic cost (28), and in fact rearfoot strikers who adopt a forefoot strike could experience an increase in metabolic cost (39).…”