Validating Commercial Wearable Sensors for Running Gait Parameters Estimation

Abstract

A growing number of people all over the world are running. Gathering in-field data with wearable sensors is attractive for runners, clinicians and coaches to improve running performance, avoid injury or return to running after an injury. However, it is yet to be proven that commercially available wearable sensors provide valid data. The objective of this study was to assess the validity of five wearable sensors (Moov Now TM , MilestonePod, RunScribe TM , TgForce and Zoi) to measure ground reaction force related metrics, step rate, foot strike pattern, and vertical displacement of the center of mass during running. Concurrent/criterion validity was assessed against a laboratory-based system using Pearson's correlation coefficients and ANOVAs. Step rate measurement provided by all wearable sensors was valid (all r > 0.96 and p < 0.001). Only Zoi provided valid vertical displacement of the center of mass (r = 0.81, p < 0.001); only TgForce provided meaningful estimates of instantaneous vertical loading rate (r = 0.76, p < 0.001); only MilestonePod could discriminate between a rear-, mid-and fore-foot strike pattern during running (p < 0.001). None of the wearable sensors was valid for estimating peak braking force. In conclusion, only a few metrics provided by these commercially available wearable sensors are valid. Potential buyers should therefore be aware of such limitations when monitoring running gait variables.

The Effect of Footwear, Running Speed, and Location on the Validity of Two Commercially Available Inertial Measurement Units During Running

“…The relationship between AVLR and PPA from the shoemounted IMUs (IMeasureU-Shoe: r = 0.49 overall; RunScribe Impact: r = 0.55 overall; RunScribe Shock: r = 0.47 overall) was not only significant, but higher than in previous studies that fixed the IMU to the heel instead of the laces (Cheung et al, 2019;Pairot de Fontenay et al, 2020). This suggests that the lace-mount might be a better location to act as a proxy for GRF loading rates when compared with the heel.…”

“…Distally-placed accelerometers may also more closely represent the accelerations experienced by the foot/ankle (Sheerin et al, 2019 ). However, positive associations between impact loading (average vertical loading rate; AVLR) and PPA of shoe-mounted IMUs have been poor, especially when attached to the heel of the shoe (Cheung et al, 2019 ; Pairot de Fontenay et al, 2020 ).…”

“…When mounted on the shin adjacent to a research-grade accelerometer, the RunScribe sensor also demonstrated high positive associations (ICC 0.89–0.92) with the measurement of tibial PPA across a range of running speeds (Brayne et al, 2018 ). The RunScribe sensor mounted on the heel is not a valid surrogate for either the average or instantaneous vertical loading rates (Pairot de Fontenay et al, 2020 ), indicating that choice of sensor location can have important implications. However, current guidelines from RunScribe recommend placing it on the dorsum of the shoe, where it clips into a cradle that is securely mounted to the shoelaces.…”

Effects of gait retraining with focus on impact versus gait retraining with focus on cadence on pain, function and lower limb kinematics in runners with patellofemoral pain: Protocol of a randomized, blinded, parallel group trial with 6-month follow-up

“…The accelerometer Tgforce (v2.0.0.10) will be taped to the anteromedial aspect of the subject's distal tibia [20]. The study of Pairot de Fontenay and colleagues [41] assessed the…”

Hardware/Software Co-Design of Fractal Features Based Fall Detection System

“…Morbidoni et al [33] utilise electromyography signals for deep learning classification of stance, swing phases during natural walking. Pairot et al [34] determined the validity of commercially available, wearable sensors by monitoring gait during running and concluded that only few metrics measured by commercially available sensors are valid. Park et al [35] developed a real-time healthcare monitoring system for monitoring gait and vital signs, and utilised machine learning classification for health conditions, such as disordered gait and onset of stroke.…”