Login / Signup

Accuracy and precision of loadsol® insole force-sensors for the quantification of ground reaction force-based biomechanical running parameters.

Wolfgang SeiberlElisabeth JensenJosephine MerkerMarco LeitelAnsgar Schwirtz
Published in: European journal of sport science (2018)
Force plates represent the "gold standard" in measuring running kinetics to predict performance or to identify the sources of running-related injuries. As these measurements are generally limited to laboratory analyses, wireless high-quality sensors for measuring in the field are needed. This work analysed the accuracy and precision of a new wireless insole forcesensor for quantifying running-related kinetic parameters. Vertical ground reaction force (GRF) was simultaneously measured with pit-mounted force plates (1 kHz) and loadsol® sensors (100 Hz) under unshod forefoot and rearfoot running-step conditions. GRF data collections were repeated four times, each separated by 30 min treadmill running, to test influence of extended use. A repeated-measures ANOVA was used to identify differences between measurement devices. Additionally, mean bias and Bland-Altman limits of agreement (LoA) were calculated. We found a significant difference (p < .05) in ground contact time, peak force, and force rate, while there was no difference in parameters impulse, time to peak, and negative force rate. There was no influence of time point of measurement. The mean bias of ground contact time, impulse, peak force, and time to peak ranged between 0.6% and 3.4%, demonstrating high accuracy of loadsol® devices for these parameters. For these same parameters, the LoA analysis showed that 95% of all measurement differences between insole and force plate measurements were less than 12%, demonstrating high precision of the sensors. However, highly dynamic behaviour of GRF, such as force rate, is not yet sufficiently resolved by the insole devices, which is likely explained by the low sampling rate.
Keyphrases
  • drinking water
  • single molecule
  • high intensity
  • low cost
  • machine learning
  • high frequency
  • finite element analysis