Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique

Abstract

Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.

Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning

“…Figure 1 shows the block diagram of the proposed human-movement sensor system that was used in our previous work [ 27 , 46 ]. The FSR sensors (Flexiforce A301) [ 47 ] were located at six common points of pressure across the foot.…”

“…This paper aims to detect thirteen different human movements using the P2S2 and machine learning algorithms. The P2S2 was developed in our previous work in [ 46 ] and the details will be provided in Section 2 . It allows the system to acquire a more complete view of the user and makes it highly useful for injury prevention and health tracking.…”

Foot Position Measurement during Assistive Motion for Sit-to-Stand Using a Single Inertial Sensor and Shoe-Type Force Sensors

“…Moreover, machine learning can be used in applications using wearable sensors that require indirect estimations for joint movement, muscle activity, and position [ 28 ]. Recent studies have shown that the combination of wearable sensors and machine learning could be used to estimate kinematic values during lifting and squatting [ 25 , 26 , 29 ]. Antwi-Afari et al reported that several lifting postures could be identified using machine learning and that foot pressure distributions could be obtained from insole force sensors [ 25 ].…”

“…Matijevich et al found that lumbar load during manual lifting could be predicted by a machine learning-based regression model with an inertial sensor and shoe-type force sensors [ 26 ]. Choffin et al estimated foot angle while squatting using machine learning and insole force sensors [ 29 ]. These previous studies focused on tasks that are similar to our current research, and thereby provided insight into the potential usefulness of the combination of wearable sensors and machine learning for estimating foot position during manual lifting [ 25 , 26 , 29 ].…”

“…Choffin et al estimated foot angle while squatting using machine learning and insole force sensors [ 29 ]. These previous studies focused on tasks that are similar to our current research, and thereby provided insight into the potential usefulness of the combination of wearable sensors and machine learning for estimating foot position during manual lifting [ 25 , 26 , 29 ]. From these findings, we hypothesized that machine learning is the most suitable method for the indirect estimation of foot position.…”