GaitKeeper: A System for Measuring Canine Gait.

Cassim Ladha,Jack O’Sullivan,Lucy Asher,Zoe Belshaw

doi:10.3390/s17020309

Cassim Ladha, Jack O’Sullivan + Show 2 more

Open Access

https://doi.org/10.3390/s17020309

Copy DOI

Abstract

It is understood gait has the potential to be used as a window into neurodegenerative disorders, identify markers of subclinical pathology, inform diagnostic algorithms of disease progression and measure the efficacy of interventions. Dogs’ gaits are frequently assessed in a veterinary setting to detect signs of lameness. Despite this, a reliable, affordable and objective method to assess lameness in dogs is lacking. Most described canine lameness assessments are subjective, unvalidated and at high risk of bias. This means reliable, early detection of canine gait abnormalities is challenging, which may have detrimental implications for dogs’ welfare. In this paper, we draw from approaches and technologies used in human movement science and describe a system for objectively measuring temporal gait characteristics in dogs (step-time, swing-time, stance-time). Asymmetries and variabilities in these characteristics are of known clinical significance when assessing lameness but presently may only be assessed on coarse scales or under highly instrumented environments. The system consists an inertial measurement unit, containing a 3-axis accelerometer and gyroscope coupled with a standardized walking course. The measurement unit is attached to each leg of the dog under assessment before it is walked around the course. The data by the measurement unit is then processed to identify steps and subsequently, micro-gait characteristics. This method has been tested on a cohort of 19 healthy dogs of various breeds ranging in height from 34.2 cm to 84.9 cm. We report the system as capable of making precise step delineations with detections of initial and final contact times of foot-to-floor to a mean precision of 0.011 s and 0.048 s, respectively. Results are based on analysis of 12,678 foot falls and we report a sensitivity, positive predictive value and F-score of 0.81, 0.83 and 0.82 respectively. To investigate the effect of gait on system performance, the approach was tested in both walking and trotting with no significant performance deviation with 7249 steps reported for a walking gait and 4977 for a trotting gait. The number of steps reported for each leg were approximately equal and this consistency was true in both walking and trotting gaits. In the walking gait 1965, 1790, 1726 and 1768 steps were reported for the front left, front right, hind left and hind right legs respectively. 1361, 1250, 1176 and 1190 steps were reported for each of the four legs in the trotting gait. The proposed system is a pragmatic and precise solution for obtaining objective measurements of canine gait. With further development, it promises potential for a wide range of applications in both research and clinical practice.

Highlights

IntroductionDetailed analysis of the gait cycle in humans has demonstrated its utility as a window into many underlying neurodegenerative and medical diseases, as well as biomechanical conditions [1,2,3].Sensors 2017, 17, 309; doi:10.3390/s17020309 www.mdpi.com/journal/sensorsThe translation of such methodologies to non-human species can be problematic due to the need to account for their different physiological and cognitive abilities.Parameters (or micro-gait features) in the human gait cycle that have been identified as sensitive predictors of disease (in a medical sense) can be roughly characterized into spatial or temporal categories: temporal parameters pertaining to intra-leg based timing events (e.g., velocity, length, width), and spatial parameters being related to measurements between the environment and skeletal features (e.g., pitch, roll, joint flexions) [4,5]
Detailed analysis of the gait cycle in humans has demonstrated its utility as a window into many underlying neurodegenerative and medical diseases, as well as biomechanical conditions [1,2,3].Sensors 2017, 17, 309; doi:10.3390/s17020309 www.mdpi.com/journal/sensorsThe translation of such methodologies to non-human species can be problematic due to the need to account for their different physiological and cognitive abilities.Parameters in the human gait cycle that have been identified as sensitive predictors of disease can be roughly characterized into spatial or temporal categories: temporal parameters pertaining to intra-leg based timing events, and spatial parameters being related to measurements between the environment and skeletal features [4,5]
The processing of the data using Matlab scripts was a matter of seconds and the output was a data file that could be used for post-analysis and to generate a report

Summary

Introduction

Detailed analysis of the gait cycle in humans has demonstrated its utility as a window into many underlying neurodegenerative and medical diseases, as well as biomechanical conditions [1,2,3].Sensors 2017, 17, 309; doi:10.3390/s17020309 www.mdpi.com/journal/sensorsThe translation of such methodologies to non-human species can be problematic due to the need to account for their different physiological and cognitive abilities.Parameters (or micro-gait features) in the human gait cycle that have been identified as sensitive predictors of disease (in a medical sense) can be roughly characterized into spatial or temporal categories: temporal parameters pertaining to intra-leg based timing events (e.g., velocity, length, width), and spatial parameters being related to measurements between the environment and skeletal features (e.g., pitch, roll, joint flexions) [4,5]. The calculation of each micro-gait feature starts with the precise detection of final and initial contact times (FC and IC, respectively) of the foot with the floor These events can be directly used to determine when the foot is in stance IC-FC) or swing (FC-IC) and form the basis of other more complex calculations to determine both temporal and spatial micro-gait features in addition to gait asymmetry (difference inter-leg) and variabilities (difference intra-leg) [6,7,8]. In humans, these parameters have been shown to correlate with neurological and physiological conditions [1,9]

Methods

Results

Discussion

Conclusion