A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings

Abstract

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions. Graphical Abstract

Effects of multidisciplinary inpatient rehabilitation on everyday life physical activity and gait in patients with multiple sclerosis

“…The locomotion periods or walking bouts (WBs) were automatically extracted using a previously validated algorithm [ 48 ]. The algorithm is based on a peak enhancement filtering method using continuous wavelet transforms of the triaxial angular velocity norm recorded at the foot.…”

“…However, the step/activity count approach only partially addresses the multidimensional aspect of PA. Recently, robust algorithms for ambulatory locomotion detection have been developed and validated that take into account different PA dimensions such as activity type, duration, and intensity [ 43 , 48 , 59 ]. These different PA dimensions can be combined to obtain a symbolic sequence of PA states, also called “barcoding” [ 44 ].…”

“…The main challenge in measuring free-living gait speed is the need for validated algorithms for reliable and accurate feature extraction. In recent years, researchers have developed robust algorithms for locomotion detection in a free-living environment using trunk-mounted (chest/lower back) [ 30 , 43 , 59 ], or foot-mounted inertial measurement unit (IMU) [ 48 ]. The advantage of the foot-mounted IMU is the clinically acceptable accuracy of the gait spatio-temporal parameters which can be further extracted [ 31 ].…”