Soft-Material-Based Smart Insoles for a Gait Monitoring System

Abstract

Spatiotemporal analysis of gait pattern is meaningful in diagnosing and prognosing foot and lower extremity musculoskeletal pathologies. Wearable smart sensors enable continuous real-time monitoring of gait, during daily life, without visiting clinics and the use of costly equipment. The purpose of this study was to develop a light-weight, durable, wireless, soft-material-based smart insole (SMSI) and examine its range of feasibility for real-time gait pattern analysis. A total of fifteen healthy adults (male: 10, female: 5, age 25.1 ± 2.64) were recruited for this study. Performance evaluation of the developed insole sensor was first executed by comparing the signal accuracy level between the SMSI and an F-scan. Gait data were simultaneously collected by two sensors for 3 min, on a treadmill, at a fixed speed. Each participant walked for four times, randomly, at the speed of 1.5 km/h (C1), 2.5 km/h (C2), 3.5 km/h (C3), and 4.5 km/h (C4). Step count from the two sensors resulted in 100% correlation in all four gait speed conditions (C1: 89 ± 7.4, C2: 113 ± 6.24, C3: 141 ± 9.74, and C4: 163 ± 7.38 steps). Stride-time was concurrently determined and R2 values showed a high correlation between the two sensors, in both feet (R² ≥ 0.90, p < 0.05). Bilateral gait coordination analysis using phase coordination index (PCI) was performed to test clinical feasibility. PCI values of the SMSI resulted in 1.75 ± 0.80% (C1), 1.72 ± 0.81% (C2), 1.72 ± 0.79% (C3), and 1.73 ± 0.80% (C4), and those of the F-scan resulted in 1.66 ± 0.66%, 1.70 ± 0.66%, 1.67 ± 0.62%, and 1.70 ± 0.62%, respectively, showing the presence of a high correlation (R² ≥ 0.94, p < 0.05). The insole developed in this study was found to have an equivalent performance to commercial sensors, and thus, can be used not only for future sensor-based monitoring device development studies but also in clinical setting for patient gait evaluations.

Keywords: capacitive pressure sensor; conductive textile; gait; monitoring; phase coordination index.

Figure 1

The structure of a parallel capacitor.

Figure 2

The structure of the proposed sensor—the soft-material-based smart insole (SMSI).

Figure 3

The sensor location of SMSI.

Figure 4

Block diagram of the proposed gait measurement and monitoring system.

Figure 5

Schematic of the gait measurement system.

Figure 6

The structure of the capacitance-measuring printed circuit board (PCB).

Figure 7

Gait monitoring system.

Figure 8

The ten channel data from the SMSI.

Figure 9

The procedure of signal processing: (a) low pass filtered gait signal; (b) derivative filtered gait signal; (c) squared gait signal; (d) the result of peak detection (heel strike and toe off).

Figure 9

The procedure of signal processing: (a) low pass filtered gait signal; (b) derivative filtered gait signal; (c) squared gait signal; (d) the result of peak detection (heel strike and toe off).

Figure 10

Left -Right stepping phase (φ) in a gait cycle.

Figure 11

Experimental method, (a) experimental environment and (b) real-time raw data of the SMSI and the F-scan.

Figure 11

Experimental method, (a) experimental environment and (b) real-time raw data of the SMSI and the F-scan.

Figure 12

The Bland-Altman plot showing the difference between the SMSI and F-scan for the left foot stride-time, in each gait speed condition. The dashed line in the middle is the mean value of the differences, the lines above and below denote the standard deviation (95% CI).