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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-03, Vol.20 (5), p.1448
• Main Authors: Martini, Elena, Fiumalbi, Tommaso, Dell'Agnello, Filippo, Ivanić, Zoran, Munih, Marko, Vitiello, Nicola, Crea, Simona
• Format: Article
• Language: English
• Subjects: Algorithms; Biomechanical Phenomena; Computer Systems; Electricity; Foot - physiology; Gait - physiology; Humans; optoelectronic sensors; plantar pressure distribution; Pressure; real-time gait monitoring; robot control; sensorized insole; wearable sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20051448

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 (CoP ) 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 CoP profiles (Pearson correlation coefficient with force platform ρCoP = 0.96 (0.02)), whereas the correlation with vGRF measured by the force plate was lower than that obtained with the previous prototype (ρvGRF = 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.