An easy-to-use wearable step counting device for slow walking using ankle force myography

Abstract

Step counting is a practical way for evaluating the activity level of people in daily life. However, the widely used accelerometer-based step-counters are not able to accurately detect low-speed steps (<0.6 m/s). Our earlier study used supervised machine learning to achieved a very high performance (error rate <1.5%) in low speed step detection based on the force myography (FMG) signals recorded at the ankle. However, the supervised machine learning approach requires a training process using carefully labelled data. The present study explores the feasibility of using unsupervised learning technique to improve the usability of the ankle force sensing resisters (FSR) band in step detection. An unsupervised K-Means algorithm was employed to train and test with the FMG data recorded from an array of 8 FSRs worn on the ankle position. Eight young healthy volunteers participated in the study by walking on a treadmill at 3 different speeds (0.28 m/s, 0.42 m/s, and 0.56 m/s) while FMG signals were recorded. Results showed a low error rate in the step detection (2.2%) at all 3 walking speeds using the unlabelled data for training.