Extraction of Stride Events From Gait Accelerometry During Treadmill Walking

Abstract

Objective Evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method We proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson’s disease and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results Our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe contact events from both feet. We used t-tests, ANOVA and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be identified similarly using measures with the two methods. Conclusions A simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical Impact: The proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step towards the assessment of stride events using tri-axial accelerometers in real-life settings.

Gait Event Detection From Accelerometry Using the Teager–Kaiser Energy Operator

“…The performance of TKGED was compared with four established GED methods [3], [11]- [13], each of which has previously demonstrated high GED accuracy in comparative assessments [12], [18], [19]. All methods were implemented in MatLab 2018a (Mathworks, USA) as outlined in the source literature [3], [11]- [13], and summarised as follows.…”

“…Acceleration in the vertical and anterio-posterior directions from a sensor located at the waist were preprocessed for artefact removal by subtracting the mean, 5th order median filtering, and normalizing the maximum amplitude to 1. A refined peak finding approach within localised windows was used to determine the temporal occurrence of IC and FC events [13].…”

“…The computational approach most commonly adopted for GED involves digital filtering in conjunction with zero-crossing and/or peak detection [13]- [17], [20], [21]. These methods, although straightforward to implement, often rely on the signal having a specific profile and very precise sensor placement to perform reliably [12].…”

“…The performance of the proposed TKO method for gait event detection, henceforth referred to as TKGED, was compared with four other algorithms -two designed for waist mounted sensors [11], [13], and two designed for shank mounted sensors [3], [12]. For each sensor position, one chosen method used digital filtering with peak detection [3], [13], and the other used the CWT [11], [12]. TKGED demonstrated high accuracy for IC and FC detection across a variety of walking conditions typical of tasks in daily living.…”

Parkinson's disease and wearable devices, new perspectives for a public health issue: an integrative literature review

“…Currently, such monitoring can be done from devices that utilize accelerometers, gyroscopes, magnetometers, and electromyography sensors, with possible uses such as the clinical observation of falls, tremors, bradykinesia, gait disorders, and mobility fluctuations 15 . The most appropriate way to measure the motor performance of patients seems to be the use of wearable devices based on inertial sensors, which can acquire data with a high sampling rate 13,[16][17][18] . This has been developed for the assessment of several motor symptoms using a single or multiple systems.…”