Tibial bone forces can be monitored using shoe-worn wearable sensors during running

Abstract

Tibial bone stress injury is a common overuse injury experienced by runners, which results from repetitive tissue forces. Wearable sensor systems (wearables) that monitor tibial forces could help understand and reduce injury incidence. However, there are currently no validated wearables that monitor tibial bone forces. Previous work using simulated wearables demonstrated accurate tibial force estimates by combining a shoe-worn inertial measurement unit (IMU) and pressure insole with a trained algorithm. This study aimed assessed how accurately tibial bone forces could be estimated with existing wearables. Nine recreational runners ran at a series of different speeds and slopes, and with various stride patterns. Shoe-worn IMU and insole data were input into a trained algorithm to estimate peak tibial force. We found an average error of 5.7% in peak tibial force estimates compared with lab-based estimates calculated using motion capture and a force instrumented treadmill. Insole calibration procedures were essential to achieving accurate tibial force estimates. We concluded that a shoe-worn, multi-sensor system is a promising approach to monitoring tibial bone forces in running. This study adds to the literature demonstrating the potential of wearables to monitor musculoskeletal forces, which could positively impact injury prevention, and scientific understanding.

Keywords: (five): calibration; bone stress injury; inertial measurement units; pressure sensing insoles; remote monitoring.

Figure 1:

Signal flow chart. This shows how signals were processed to compute tibial bone force estimates for the Advanced Wearable condition. Yellow represents data from the shoe-mounted IMU and blue represents data from the pressure insole.

Figure 2:

Peak sagittal plane foot orientation angle determined from Standard Wearable shoe-mounted IMU vs. Lab-based 3D motion capture. These IMU signals were identical when input to the Advanced Wearable algorithm. Each color represents a different participant, and each data point a different running trial. The black line is a unity line representing perfect angle estimates. The Simulated Wearable data are not shown here because they are the same as the Lab-based 3D motion capture data.

Figure 3:

Peak forces computed from Simulated Wearables (A), Standard Wearable pressure insoles (B), and Advanced Wearable pressure insoles (C), each vs. Lab-based peak GRF from an instrumented treadmill. Each color represents a different participant, and each data point a different running trial. The black line is a unity line representing perfect estimates of GRF.

Figure 4:

Peak tibial bone force determined from Simulated Wearables (A), Standard Wearables (B), and Advanced Wearables (C), each vs. Lab-based estimates. Each color represents a different participant, and each data point a different running trial. The black line is a unity line representing perfect estimates of peak tibial bone force.