Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation

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ID: 113998
2020
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Abstract
Falls are among the main causes of injuries in elderly individuals. Balance and mobility impairment are major indicators of fall risk in this group. The objective of this research was to develop a fall risk feedback system that operates in real time using an inertial sensor-based instrumented cane. Based on inertial sensor data, the proposed system estimates the kinematics (contact phase and orientation) of the cane. First, the contact phase of the cane was estimated by a convolutional neural network. Next, various algorithms for the cane orientation estimation were compared and validated using an optical motion capture system. The proposed cane contact phase prediction model achieved higher accuracy than the previous models. In the cane orientation estimation, the Madgwick filter yielded the best results overall. Finally, the proposed system was able to estimate both the contact phase and orientation in real time in a single-board computer.
Reference Key
fernandez2020sensorsinertial Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ibai Gorordo Fernandez;Siti Anom Ahmad;Chikamune Wada;Fernandez, Ibai Gorordo;Ahmad, Siti Anom;Wada, Chikamune;
Journal sensors
Year 2020
DOI 10.3390/s20174675
URL
https://www.mdpi.com/1424-8220/20/17/4675
Keywords
Deep learning gait edge computing inertial measurement unit falls instrumented cane

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