Seven phases of gait detected in real-time using shank attached gyroscopes

Abstract

A new gyroscope-based gait phase detection system (GPDS) with ability to detect all seven phases of gait was proposed in this study. Gyroscopes were attached to each shank. Shank angular velocity, about the medio-lateral axis, was streamed to a PC and a rule-based algorithm was used to identify characteristics of the signals. Five subjects were asked to walk on treadmill at their self-selected speed while using this system. All 7 phases of gait: LR, MSt, TSt, PSw, ISw, MSw, and TSw were detected in real-time using only shank angular velocities. To quantify system performance, sensor data was compared to simultaneously collected motion capture data. Average gait phase detection delays of the system were less than 40ms except TSw (74ms). The present system, consisting of minimal sensors and decreased processing, is precise, cosmetic, economical, and a good alternative for portable stand-alone applications.

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

“…This minimal sensor set up facilitates implementation (similar to FVA), is of modest cost, and computationally efficient in wearable applications. Unlike CBTA, minimal processing is required to condition the sensor signals for the SK detection algorithm providing improved processing capacity and enabling implementation of more sophisticated control algorithms in the system [ 50 ]. The ample amount of data on reliability and performance of the SK GEDM evaluated in patient populations such as amputees [ 51 ], spinal cord injuries [ 49 ], and post-stroke survivors [ 52 ] demonstrate its robustness to variations in gait.…”

“…Previous work reported real-time GED performance using SK against force plates in adults, typically developing children, and children with CP during treadmill walking [ 5 , 50 ]. Gait detection reliability was higher when SK was applied to sensor signals (AD: 99.8% [ 50 ], TD: 99.9%, CP: 99.6% [ 5 ]) than to motion capture signals (AD: 96.7% TD: 96.3% CP: 95.2%). Comparisons of SK GEDM accuracies between the sensor system (gyroscope signal) and motion capture system are outlined in Table 5 .…”

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

“…These computation methods are categorized into two main domains. Firstly, the domain based on the threshold method [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], time-frequency analysis [ 18 , 19 , 20 , 21 ], and peak heuristic algorithms [ 16 , 19 , 22 , 23 , 24 , 25 ], which are also variations of the threshold method. Secondly, Machine Learning (ML) approaches are now among the most popular techniques to detect phases and events with various models such as Hidden Markov Models (HMM) [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], or several of the latest studies published based on the Artificial Neural Network technique (ANN) [ 35 , 36 , 37 , 38 ], Deep Learning Neural Network (DLNN) [ 39 , 40 , 41 , 42 , 43 ], a Convolutional Neural Network (CNN) [ 44 , 45 , 46 ], or [ 28 ] proposed a hybrid method that combined HMM and Fully connected Neural Networks (FNN).…”

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

“…[3]. Rule-based algorithm methods for defining gait classifiers have been implemented using a wearable three-axis inertial measurement unit (IMU) sensor attached to both shanks [13] or the thigh, shank, and foot [14]. Other discrete gait phase detection algorithms try to reduce classification errors by introducing more sensors, such as using force sensitive resistors (FSRs) under the shoe insole to measure ground contact forces [15]–[17].…”