Monitoring Knee Contact Force with Force-Sensing Insoles

Abstract

Numerous applications exist for monitoring knee contact force (KCF) throughout activities of daily living. However, the ability to estimate these forces is restricted to a laboratory setting. The purposes of this study are to develop KCF metric estimation models and explore the feasibility of monitoring KCF metrics via surrogate measures derived from force-sensing insole data. Nine healthy subjects (3F, age 27 ± 5 years, mass 74.8 ± 11.8 kg, height 1.7 ± 0.08 m) walked at multiple speeds (0.8-1.6 m/s) on an instrumented treadmill. Thirteen insole force features were calculated as potential predictors of peak KCF and KCF impulse per step, estimated with musculoskeletal modeling. The error was calculated with median symmetric accuracy. Pearson product-moment correlation coefficients defined the relationship between variables. Models develop per-limb demonstrated lower prediction error than those developed per-subject (KCF impulse: 2.2% vs 3.4%; peak KCF: 3.50% vs. 6.5%, respectively). Many insole features are moderately to strongly associated with peak KCF, but not KCF impulse across the group. We present methods to directly estimate and monitor changes in KCF using instrumented insoles. Our results carry promising implications for internal tissue loads monitoring outside of a laboratory with wearable sensors.

Keywords: knee contact force; musculoskeletal modeling; statistical modeling; tissue loading; wearable.

Figure A1

Correlation coefficients of traditional time domain features with (a) peak KCF and (b) KCF impulse per subject per limb.

Figure A2

Correlation coefficients of other time domain features with (a) Peak KCF and (b) KCF Impulse per subject per limb.

Figure A2

Correlation coefficients of other time domain features with (a) Peak KCF and (b) KCF Impulse per subject per limb.

Figure A3

Correlation coefficients of frequency domain features with (a) Peak KCF and (b) KCF Impulse per subject per limb.

Figure 1

Overall method of KCF prediction with force-sensing insole data. Dual force traces in Insole-3D Sync represent data from left & right foot insoles.

Figure 2

Comparison of two subjects’ KCF evolution as speed increases. Subject A develops a much larger first peak (*) in KCF than Subject B as speed increases, possibly driving the opposite direction of the association between insole and KCF metrics. LR = loading rate.