Journal
ELECTRONICS
Volume 11, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/electronics11213614
Keywords
gait analysis; artificial neural network; abnormal gait pattern; smart shoes
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Funding
- Pukyong National University
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A smart insole device has been developed to analyze gait patterns in real time and evaluate exercise function. The device uses sensors and IoT technology to monitor human activity and classify gait patterns through a WiFi network.
Recently, as a wearable-sensor-based approach, a smart insole device has been used to analyze gait patterns. By adding a small low-power sensor and an IoT device to the smart insole, it is possible to monitor human activity, gait pattern, and plantar pressure in real time and evaluate exercise function in an uncontrolled environment. The sensor-embedded smart soles prevent any feeling of heterogeneity, and WiFi technology allows acquisition of data even when the user is not in a laboratory environment. In this study, we designed a sensor data-collection module that uses a miniaturized low-power accelerometer and gyro sensor, and then embedded it in a shoe to collect gait data. The gait data are sent to the gait-pattern classification module via a Wi-Fi network, and the ANN model classifies the gait into gait patterns such as in-toeing gait, normal gait, or out-toeing gait. Finally, the feasibility of our model was confirmed through several experiments.
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