Automatic Detection of Temporal Gait Parameters in Poststroke Individuals

Abstract

Approximately one-third of people who recover from a stroke require some form of assistance to walk. Repetitive task-oriented rehabilitation interventions have been shown to improve motor control and function in people with stroke. Our long-term goal is to design and test an intensive task-oriented intervention that will utilize the two primary components of constrained-induced movement therapy: massed, task-oriented training and behavioral methods to increase use of the affected limb in the real world. The technological component of the intervention is based on a wearable footwear-based sensor system that monitors relative activity levels, functional utilization, and gait parameters of affected and unaffected lower extremities. The purpose of this study is to describe a methodology to automatically identify temporal gait parameters of poststroke individuals to be used in assessment of functional utilization of the affected lower extremity as a part of behavior enhancing feedback. An algorithm accounting for intersubject variability is capable of achieving estimation error in the range of 2.6–18.6% producing comparable results for healthy and poststroke subjects. The proposed methodology is based on inexpensive and user-friendly technology that will enable research and clinical applications for rehabilitation of people who have experienced a stroke.

Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

“…Foot switches such as force sensitive resistors (FSRs) provide contact timing information and are often used as the reference method in determining the accuracy of gait event detection in other systems [2], [12]- [14]. However, they do not provide any kinematic data or spatial information during swing phase, which are important aspects in pathological gait assessment [15].…”

“…An alternative is to analyze the magnitude of the resultant accelerometer signal instead which makes it invariant to individual axis alignment, as done in [4], [29]. While some methodologies instruct subjects to walk in a straight line or a given path at a selfselected pace [4], [13], [27], [29], others either pre-define a set of walking speeds or ask the subjects to walk slowly, normal and fast, in order to test the algorithmic robustness to different velocities [3], [14], [24]- [26], [28]. A number of algorithms apply thresholds either to filtered accelerometer signals or use them at some intermediate stage after signal transformation, to perform peak detection for identifying events [25], [28], [29].…”

Accelerometric assessment of different dimensions of natural walking during the first year after stroke: Recovery of amount, distribution, quality and speed of walking

“…Previous research has shown the potential relevance of 4 dimensions of walking behaviour: amount (6,13); distribution (14)(15)(16); quality of gait (17,18); and gait speed (19,20). The amount of activities is an increasingly used parameter in studies of people with chronic conditions (6).…”

A Comparative Review of Footwear-Based Wearable Systems

“…The work in [5] has used 18.75 Hz, however it has not yet been fully validated. In [10], it was shown that 25 Hz is sufficient for gait parameter extraction for walking at the speed of 2 km/h or less. However, our ongoing work suggests that for computation of parameters such as double support time, especially during walking at speeds more than 4 km/h or running, 25 Hz is insufficient.…”

“…From connectivity perspective, Bluetooth is quite commonly used in many of the footwear-based systems as done in [5,10,18] and others. A lower power consumption version of Bluetooth, named Bluetooth Low Energy (BLE), is coming to prominence in the recent years and there are a few footwear-based systems that have utilized BLE, [21,22], which will be discussed in the later sections.…”