Real-Time continuous gait phase and speed estimation from a single sensor

Abstract

Human gait involves a repetitive cycle of movements, and the phase of gait represents the location in this cycle. Gait phase is measured across many areas of study (e.g., for analyzing gait and controlling powered lower-limb prosthetic and orthotic devices). Current gait phase detection methods measure discrete gait events (e.g., heel strike, flat foot, toe off, etc.) by placing multiple sensors on the subject’s lower-limbs. Using multiple sensors can create difficulty in experimental setup and real-time data processing. In addition, detecting only discrete events during the gait cycle limits the amount of information available during locomotion. In this paper we propose a real-time and continuous measurement of gait phase parameterized by a mechanical variable (i.e., phase variable) from a single sensor measuring the human thigh motion. Human subject experiments demonstrate the ability of the phase variable to accurately parameterize gait progression for different walking/running speeds (1 to 9 miles/hour). Our results show that this real-time method can also estimate gait speed from the same sensor.

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

“…We found 87 articles that concerned the gait phase or event detection using wearable sensors. Figure 1 indicates the number of published papers based on wearable sensors that measure the signals and methods applied: Wearable sensors : IMU sensors were the most commonly used (67 articles), much more than force sensors (12 articles) and EMGs (7 articles); Methods : The number of published threshold-based method articles (44) were nearly equal machine learning-based method articles (40), and other methods of 3 papers [ 53 , 63 , 64 ]. Though applications of Machine Learning-based have been increasing over the years, thresholding-based research studies were used more than other methods.…”

Variable Impedance Control of Powered Knee Prostheses Using Human-Inspired Algebraic Curves

“…The second representation in [16], which unifies the stance and swing phases of walking, is a velocity-based (nonholonomic) gait representation using the thigh phase portrait. This second representation method was later experimentally verified in [18] via a single sensor. As another unified walking gait cycle representation, the authors in [12,13] proposed a thigh angle integralbased representation of walking.…”

ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

“…For example, they reported approximately s for the TO and s for the IC. Similar to our work, the recent study of Quintero et al [ 40 ] worked on estimating the continuous progression of the gait cycle by extracting the relationship between the thigh angle and velocity extracted from one IMU. They transformed the angle–velocity relationship to polar coordinates in order to predict the gait percentage.…”