4.6 Article

Pressure-Sensitive Insoles for Real-Time Gait-Related Applications

Journal

SENSORS
Volume 20, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/s20051448

Keywords

optoelectronic sensors; wearable sensors; sensorized insole; plantar pressure distribution; real-time gait monitoring; robot control

Funding

  1. European Commission under the CYBERLEGs project within the Seventh Framework Program (FP7-ICT-2011-7) [287894]
  2. CYBERLEGs Plus Plus project within the H2020 framework (H2020-ICT-25-2016-2017) [731931]
  3. Slovenian Research Agency [P2-0228]

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Wearable robotic devices require sensors and algorithms that can recognize the user state in real-time, in order to provide synergistic action with the body. For devices intended for locomotion-related applications, shoe-embedded sensors are a common and convenient choice, potentially advantageous for performing gait assessment in real-world environments. In this work, we present the development of a pair of pressure-sensitive insoles based on optoelectronic sensors for the real-time estimation of temporal gait parameters. The new design makes use of a simplified sensor configuration that preserves the time accuracy of gait event detection relative to previous prototypes. The system has been assessed relatively to a commercial force plate recording the vertical component of the ground reaction force (vGRF) and the coordinate of the center of pressure along the so-called progression or antero-posterior plane (CoPAP) in ten healthy participants during ground-level walking at two speeds. The insoles showed overall median absolute errors (MAE) of 0.06 (0.02) s and 0.04 (0.02) s for heel-strike and toe-off recognition, respectively. Moreover, they enabled reasonably accurate estimations of the stance phase duration (2.02 (2.03) % error) and CoPAP profiles (Pearson correlation coefficient with force platform rho CoP = 0.96 (0.02)), whereas the correlation with vGRF measured by the force plate was lower than that obtained with the previous prototype (rho vGRF = 0.47 (0.20)). These results confirm the suitability of the insoles for online sensing purposes such as timely gait phase estimation and discrete event recognition.

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