A System for Measuring Canine Gait

Ladha C, O'Sullivan J, Belshaw Z, Asher L. Gaitkeeper: A system for measuring canine gait. Sensors 2017, 17(2), 309.

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DOI link to article: https://doi.org/10.3390/s17020309

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03/05/2017

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sensors

Article GaitKeeper: A System for Measuring Canine Gait

Cassim Ladha 1,*, Jack O’Sullivan 1, Zoe Belshaw 2 and Lucy Asher 1

1 Centre for Behaviour and Evolution, Henry Wellcome Building, Newcastle University, Newcastle NE2 4HH, UK; J.O’[email protected] (J.O.); [email protected] (L.A.) 2 School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, UK; [email protected] * Correspondence: [email protected]

Academic Editor: Thurmon E. Lockhart Received: 9 December 2016; Accepted: 31 January 2017; Published: 8 February 2017

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.

Keywords: gait; locomotion; stride segmentation; accelerometer; dog; exercise; movement science; inertial measurement unit (IMU); gyroscope

1. 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 [1–3].

Sensors 2017, 17, 309; doi:10.3390/s17020309 www.mdpi.com/journal/sensors Sensors 2017, 17, 309 2 of 17

The 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]. In either case, 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 (between IC-FC) or swing (FC-IC) and thus 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) [6–8]. In humans, these parameters have been shown to correlate with neurological and physiological conditions [1,9]. In recent years there has been an increase in interest in the application of similar measures of gait for use with companion animals. In the United Kingdom approximately 30% of households own a dog (Canis familiaris) [10]. Many of these animals either currently suffer from or are at risk of developing orthopaedic diseases; it is estimated 20% of dogs over 1 year old have osteoarthritis [11]. The potential impact of a tool for the prediction and diagnosis of such conditions, and the monitoring of recovery post-treatment, could be substantial. Currently the most prevalent form of gait analysis performed by veterinarians are forms of subjective visual gait assessment [12]. Numerical rating scales and visual analogue scales are two subjective, observation based methods which are used to evaluate the severity of lameness [13]. A wide range of clinical scales have been described [14] to assist the grading of canine lameness in clinical veterinary practice but few are validated, therefore their reliability and value is unknown. These two subjective measures have been shown, despite their ubiquity, to lack accuracy in the majority of instances except where lameness is severe [12,15]. Technologies for automatically measuring gait, in both humans and dogs, can be placed in two categories: worn and un-worn [16]. In the un-worn category are methods ranging from manual coding of videos to the use of force plates and camera based 3D motion analysis. Force plates are often used in research centres or specialist veterinary referral clinics to measure the ground reaction forces which are generated when dogs stand on or walk across the measurement region [15,17]. They are generally termed kinetic-based measurements and are currently considered the gold standard in animal movement science as they provide an objective and precise method of collecting foot-down, gait related measurements [7]. Unfortunately, while precise, this category of movement detection is limited to instrumented environments, which may be stressful for the dog, un-natural and dimensionally constrained [18]. In addition, the specialist nature of the equipment often means limited, expensive availability in sparse geographic locations. Worn sensors are widely researched in human movement science with focus on improving existing measures [19], developing new ones such as foot clearance [20] and characterising conditions by gait [21,22]. In human movement science, the objective outputs available from worn sensors are approaching a maturity whereby they are capable of informing clinical decision making process for movement related conditions [23]. These sensors measure the movement of the supporting limb or body rather than forces and are generally termed kinematic measurements. While some research has been done using flex sensors and/or pressure transducers [24,25], the most common worn sensor systems combine accelerometers and gyroscopes; this combination is termed an inertial measurement unit (IMU). The popularity of the device in the clinical setting, both among humans and animals, has been accompanied by the rise in commercial products for humans (e.g., FitBit), or tailored alternatives for dogs (e.g., Whistle) [26,27]. Unfortunately, these devices, although low cost, employ black box methodologies obfuscating their strengths and weaknesses which therefore prevent the in-depth analysis of their potential for application in a research or clinical setting [26–28]. More recently, Sensors 2017, 17, 309 3 of 17 the research community has recognised these shortcomings and has begun to embrace open-source, transparent capture systems [29]. In human movement science, it is common for patients to wear an IMU and to perform a structured movement protocol laid out by the clinician. Protocols are varied but a very common one is the continuous walk test (CWT) [30] which is designed to assess the physical capacity of the subject. Variants of the test (such as asking the patient to count while walking) are also common and designed to stress neurological pathways; accentuating disturbances in the gait [31]. As the tests are common practice and standardized, bench-marks exist where bounds for age-related norms are in place. This makes clinical measurements easy and un-complex. In animal movement science, researchers have experimented with IMUs since they became ubiquitous during the 2000 s [32], however, to date the widest adopters of them in clinical practice is the equine industry. These equine systems are presently being used in clinical practice for lameness detection and interoperability between systems is reported to be improving [33]. Of the equine-based systems in use, there seem to be no standardised protocols (equivalent to the CWT) and subsequently a lack of openly available and reliable bench marking (although some work has been done with hoof-mounted impact sensors [34]). To the authors’ knowledge, no systems are available for measurements in dogs. It is clear that IMU-based gait measurement could result in valuable insights to normal and pathological animal movements. The relatively low cost and ability to deploy worn sensors over both short and extended periods of time could make IMU gait measurement an enabling technology to detect and monitor gait abnormalities in dogs, in companion animals [35–37]. If this led to affected animals receiving appropriate veterinary treatment at an earlier stage, this could have a significant positive impact on their welfare. Initial studies have demonstrated IMUs’ benefit as a clinical tool in equine science [38–42]. However, to make such measurements necessitates precise and well-characterized instruments that are validated in the intended use environment. This paper describes the signal-processing approach used to detect final and initial contact times of dog paws. Particular attention is paid to the tailoring the development to deal with the idiosyncrasies of the canine gait cycle. Results are given from an experiment involving the comparison of the system’s step detection to the manually coded steps identified within high-speed video by two trained observers. Both step prediction and step annotation were performed on the same walking bouts for each dog allowing for a one to one comparison. The cohort used consisted of 19 dogs of a variety of breeds. Performance of the step detection methods were calculated as the Positive Predictive Value, Sensitivity and the F-measure. Potential sources of error within dogs were investigated through the use of a mixed linear model. The system was evaluated independently for both walking and trotting gaits.

2. Materials and Methods The experimental design described was approved by the Newcastle University ethics committee. Data analysis was performed using custom Matlab scripts developed by Cassim Ladha.

2.1. Data Collection A convenience sample of 19 healthy adult dogs was recruited from the local area using posters and local advertising. The sample contained breeds and crossbreeds of Border Collie, Cavalier King Charles Spaniel, Cocker Spaniel, Corgi, Doberman, Irish Wolfhound, Jack Russel Terrier, Labrador, Lurcher (with various mixes of breed), Poodle, Rhodesian Ridgeback, and Springer Spaniel. All dogs were deemed not to have any major gait impediment though retrospective video review by a qualified veterinary general practitioner. Experiments took place in a disused University residence hall. A 15 m long linear track was marked out using tape on a hard, non-slip parquet wooden surface. Before data capture commenced, each dog was given the opportunity to familiarise itself with the room used for experiments. The dog Sensors 2017, 17, 309 4 of 17 was judged to be familiar with the surroundings and ready to proceed when investigation of the environment had ceased and their demeanor was judged to be both calm and comfortable by the Sensors 2017, 17, 309 4 of 17 researchers. The height of the dogs, measured in cm from the floor to the withers, was taken at this point.point. The The owners owners also also provided provided details details as as to to the the age age of of their their dog(s) dog(s) and and with with one one exception exception (whom (whom reportedreported mild mild wrist wrist arthritis), arthritis), confirmed confirmed that that the the dogs dogs had had no no known known medical medical issues. issues. AsAs a control,a control, dogs dogs were were first first walked walked un-instrumented un-instrumented with with a standardiseda standardised lead lead for for six six laps, laps, three three clockwiseclockwise and and three three anti-clockwise. anti-clockwise. The The walk walk was was videoed videoed with with a a high-speed high-speed hand hand held held camera camera at at 240240 frames frames per per second second (FPS); (FPS); iPhone iPhone 6s 6s (Apple (Apple Inc., Inc., Cupertino, Cupertino, CA, CA, USA). USA). Subsequently, Subsequently, each each dog dog waswas then then instrumented instrumented using using four four IMU IMU sensors sensors (Vetsens, (Vetsens, Newcastle-upon-Tyne, Newcastle-upon-Tyne, UK); UK); one one on on each each leg.leg. This This was was performed performed in in two two stages. stages. Firstly, Firstly, a cohesivea cohesive bandage bandage (Vetrap, (Vetrap, 3M, 3M, Bracknell, Bracknell, UK) UK) was was wrappedwrapped twice twice around around the the prospective prospective sensor sensor location location (above (above the the carpal carpal joints joints of of the the thoracic thoracic limbs limbs andand below below the the tarsal tarsal joints joints of of the the pelvic pelvic limbs) limbs) as as shown shown in in Figure Figure1. The1. The dogs dogs were were given given a short a short time time toto habituate habituate to to wearing wearing them them and and once once deemed deemed they they had had done done so, so, sensors sensors were were attached attached to to the the lateral lateral limblimb aspect aspect using using a seconda second layer layer of of cohesive cohesive bandage bandage (placed (placed directly directly over over the the first). first).

Figure 1. Sensors were attached in the positions shown and secured with coloured cohesive bandages. Figure 1. Sensors were attached in the positions shown and secured with coloured cohesive bandages.

Each IMU sensor contained a 3-axis gyroscope and accelerometer. Both were simultaneously sampledEach IMUat 100 sensor Hz and contained output wirelessly a 3-axis gyroscopeover Bluetooth and accelerometer.to a laptop (Apple Both Macbook were simultaneously Pro, Apple Inc., sampledCupertino, at 100 CA, Hz USA). and output Received wirelessly data was over aggregated Bluetooth into to aa laptoplog-file (Apple for post Macbook processing. Pro, To Apple provide Inc., a Cupertino,distinct start CA, point, USA). within Received both data video was and aggregated IMU data into the a log-file sensors for were post videoed processing. being To tapped provide five a distincttimes. startThese point, taps withinwere clearly both video visible and in IMU the video data the and sensors could werebe identified videoed in being the tappedaccelerometer five times. data Theseas short, taps distinct were clearly impulses; visible see in Figure the video 2. and could be identified in the accelerometer data as short, distinct impulses; see Figure2. These were repeated after the walk, prior to stopping the data and video collection. Once this had occurred the sensors were attached with a layer of cohesive bandage (over the one that previously applied). Sensors were attached in a consistent orientation; USB port facing downwards and sited on the lateral aspect of the limb. The bandages of the left and right limb pairs were coloured differently (Left: green, Right: red) to assist in their differentiation during the subsequent annotation of the high speed videos. They were judged to be fully habituated when researchers observed a return to the gait pattern displayed prior to bandage attachment. Once the researchers conducting the experiment were satisfied that initial loss of proprioception (caused due to the leg bandages) had subsided and the dogs were walking with a consistent gait, video and data capture was started. At this stage the dogs were made to repeat the same walk protocol as used when not instrumented.

Figure 2. Sensors and video were synchronized on short successive taps, which appear as sharp spike in the accelerometer data stream.

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point. The owners also provided details as to the age of their dog(s) and with one exception (whom reported mild wrist arthritis), confirmed that the dogs had no known medical issues. As a control, dogs were first walked un-instrumented with a standardised lead for six laps, three clockwise and three anti-clockwise. The walk was videoed with a high-speed hand held camera at 240 frames per second (FPS); iPhone 6s (Apple Inc., Cupertino, CA, USA). Subsequently, each dog was then instrumented using four IMU sensors (Vetsens, Newcastle-upon-Tyne, UK); one on each leg. This was performed in two stages. Firstly, a cohesive bandage (Vetrap, 3M, Bracknell, UK) was wrapped twice around the prospective sensor location (above the carpal joints of the thoracic limbs and below the tarsal joints of the pelvic limbs) as shown in Figure 1. The dogs were given a short time to habituate to wearing them and once deemed they had done so, sensors were attached to the lateral limb aspect using a second layer of cohesive bandage (placed directly over the first).

Figure 1. Sensors were attached in the positions shown and secured with coloured cohesive bandages.

Each IMU sensor contained a 3-axis gyroscope and accelerometer. Both were simultaneously sampled at 100 Hz and output wirelessly over Bluetooth to a laptop (Apple Macbook Pro, Apple Inc., Cupertino, CA, USA). Received data was aggregated into a log-file for post processing. To provide a distinct start point, within both video and IMU data the sensors were videoed being tapped five Sensorstimes.2017 ,These17, 309 taps were clearly visible in the video and could be identified in the accelerometer data5 of 17 as short, distinct impulses; see Figure 2.

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These were repeated after the walk, prior to stopping the data and video collection. Once this Figurehad 2. Sensorsoccurred and the videosensors were were synchronized attached with on a short layer successive of cohesive taps, bandage which (over appear the as one sharp that spike Figurein the 2.previously accelerometerSensors andapplied). video data Sensors stream. were were synchronized attached in ona consistent short successive orientation; taps, USB whichport facing appear downwards as sharp spike and sited on the lateral aspect of the limb. The bandages of the left and right limb pairs were coloured in the accelerometer data stream. differently (Left: green, Right: red) to assist in their differentiation during the subsequent annotation of the high speed videos. They were judged to be fully habituated when researchers observed a return 2.2. Processingto oftheIMU gait pattern Data displayed prior to bandage attachment. Once the researchers conducting the experiment were satisfied that initial loss of proprioception (caused due to the leg bandages) had In a pre-processingsubsided and the dogs stage were beforewalking with any a consistent analysis, gait, IMUvideo and data data pertainingcapture was started. to each At leg was time-synchronizedthis stage the based dogs were on alignment made to repeat of the the same taps walkinserted protocol as intoused when the accelerometernot instrumented. signal. Next, data from each2.2. Processing leg was of re-labeledIMU Data out of Cartesian space and into a dog-centric coordinate system consisting of; dorsal-ventralIn a pre-processing (DV), stage medial-lateral before any analysis (ML),, IMU and data anterior-posterior pertaining to each (AP)leg was axes time- (see Figure3). During mountingsynchronized thesensors, based on alignment careful of attention the taps insert wased giveninto theto accelerometer ensure the signal. sensors Next, data positive from z-axis was always alignedeach leg with was the re-labeled positive out of ML Cart axis.esian space This and was into achieveda dog-centric by coordinate mounting system sensors consisting on of; the left legs dorsal-ventral (DV), medial-lateral (ML), and anterior-posterior (AP) axes (see Figure 3). During back-to-front.mounting The importance the sensors, careful of this attention step was ensured given to similarensure the signal sensors features positive z-axis (troughs) was always could be used for both leftaligned and right with legs.the positive The ML aligned axis. This and was reoriented achieved by data mounting was sensors then analyzedon the left legs on back-to- a leg-by-leg basis through a numberfront. The of importance operations of this that step automatically ensured similar si labelledgnal features signal (troughs) features could be pertaining used for both to left the point the and right legs. The aligned and reoriented data was then analyzed on a leg-by-leg basis through a foot left the floor (final contact, FC) and the point the foot hit the floor (initial contact, IC). In high-level number of operations that automatically labelled signal features pertaining to the point the foot left terms, the analysisthe floor (final scripts contact, identified FC) and the FC point events the foot through hit the floor spotting (initial contact, peak features IC). In high-level that correspond terms, to the foot initiatingthe movementanalysis scripts from identified the FC end events of a through stance spotting phase. peak Subsequently, features that correspond IC contacts to the arefoot then found when the footinitiating stops movement moving from and the comes end of to a stance rest (velocity phase. Subsequently, drops to IC zero) contacts at are the then end found of a when swing phase. the foot stops moving and comes to rest (velocity drops to zero) at the end of a swing phase.

Figure 3. Cartesian to animal centric coordinate system (Anterior = A, Posterior = P, Dorsal = D, Figure 3. CartesianVentral = V, to Medial animal = M, Lateral centric = L). coordinate Direction of the system arrow discerns (Anterior positive = A,axis. Posterior End mapping = was P, Dorsal = D, Ventral = V, Medial+X = AP, +Y = M,= DV, Lateral +Z = ML). = L). Sensors Direction were placed of the laterally arrow ondiscerns each leg such positive that the axis. medial-lateral End mapping was +X = AP, +Y =axis DV, were +Z aligned. = ML). Sensors were placed laterally on each leg such that the medial-lateral axis were aligned.The gyroscope data aligned in the AP axis was smoothed with a 4th order Butterworth filter (fc = 5 Hz). The result of this operation is a roughly sinusoidal signal with troughs around the mid- swing point of each step. A window was placed around each trough and used to highlight possible locations where FC and IC events could occur. The search window width was derived from results reported in Ladha et al. [43]. For FC times, the AP acceleration was decomposed using a 2nd order sym4 wavelet. FC events were detected as high-points, preceding the ML gyroscope trough and within the search window. These points were manually verified using synchronized high-speed video to corresponded to the limb initiating movement from a stationary position (stance) into swing.

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The gyroscope data aligned in the AP axis was smoothed with a 4th order Butterworth filter (fc = 5 Hz). The result of this operation is a roughly sinusoidal signal with troughs around the mid-swing point of each step. A window was placed around each trough and used to highlight possible locations where FC and IC events could occur. The search window width was derived from results reported in Ladha et al. [43]. For FC times, the AP acceleration was decomposed using a 2nd order sym4 wavelet. FC events were detected as high-points, preceding the ML gyroscope trough andSensors within 2017 the, 17 search, 309 window. These points were manually verified using synchronized high-speed6 of 17 video to corresponded to the limb initiating movement from a stationary position (stance) into swing. To determineTo determine IC eventsIC events the the AP AP acceleration acceleration was was integrated integrated usingusing a Runge-Kutta Runge-Kutta based based approach approach to to yieldyield velocity. velocity. To caterTo cater for for integration integration drift, drift, the the integration integration was reset for for each each step; step; only only the the signal signal withinwithin the the search search window window was was considered. considered. Velocity was was subsequently subsequently smoothe smoothedd IC events; IC events; found found as as low-pointslow-points precedingpreceding bothboth the paired FC FC event event and and the the ML ML gyroscope gyroscope trough trough within within the the search search window.window. A diagrammatic A diagrammatic representation representation of of the the eventevent detectiondetection process process is is shown shown inin Figure Figure 4 4with with example signal traces. example signal traces. Once FC and IC events were identified, steps were delimited between subsequent IC events on Once FC and IC events were identified, steps were delimited between subsequent IC events on the same leg; swing time was calculated as the period between FC and IC events and stance time as the same leg; swing time was calculated as the period between FC and IC events and stance time as the period between IC and subsequent FC events. the period between IC and subsequent FC events.

Figure 4. Transformed signal traces and diagrammatic representation of how FC and IC events are Figure 4. Transformed signal traces and diagrammatic representation of how FC and IC events are detected.detected. From From the the FC FC and and IC IC events, events, step-time step-time and and swing-time swing-time can be inferred. inferred.

2.3. Video2.3. Video Annotation Annotation All videosAll videos taken taken during during the the experiment experiment were were manuallymanually annotated by by two two observers observers using using the the ElanElan software software (Max (Max Planck Planck Institute, Institute, Nijmegen, Nijmegen, The The Netherlands). Netherlands). In In the the annota annotationtion process, process, each each leg leg waswas considered considered separately separately and and swing swing phase phase for for each each step step marked marked as as per per the the ethogram ethogram inin AppendixAppendix A. SwingA. eventsSwing thatevents were that obscured, were obscured, out of shot out orof unclearshot or wereunclear ignored. were ignored. The rationale The rationale behind annotating behind swingannotating phase was swing that phase it is was an easythat it feature is an easy to visuallyfeature to pick visually out; pick it enables out; it enables extraction extraction of FC of and FC IC eventsand as IC start events and as end start points and end of thepoints annotation of the annotation respectively respectively with little with room little forroom ambiguity. for ambiguity. For the For the control walks, 11 consecutive walking strides were annotated for each dog to enable the control walks, 11 consecutive walking strides were annotated for each dog to enable the extraction extraction of 10 consecutive step start and end points for each leg (see Figure 5). This quantity of steps of 10 consecutive step start and end points for each leg (see Figure5). This quantity of steps was was chosen as all dogs in our test cohort (no matter of size) were able to complete without accelerating chosen as all dogs in our test cohort (no matter of size) were able to complete without accelerating or decelerating towards the track limits. Videos of the instrumented walking bouts were annotated fully with every step included. The initial and final synchronization taps were also annotated to allow synchronization of the annotation with the data provided by the sensors. Gait type was also noted as either walk or trot as per the ethogram in Appendix A.

Sensors 2017, 17, 309 7 of 17 or decelerating towards the track limits. Videos of the instrumented walking bouts were annotated fully with every step included. The initial and ﬁnal synchronization taps were also annotated to allow synchronization of the annotation with the data provided by the sensors. Gait type was also noted as either walk or trot as per the ethogram in AppendixA. Sensors 2017, 17, 309 7 of 17

Figure 5. Approach for determining agreement between prediction and annotation. Figure 5. Approach for determining agreement between prediction and annotation. 2.4. Inter-Rater Reliability 2.4. Inter-Rater Reliability To ensure annotation quality a sub-selection of five dogs non-instrumented walks were Toannotated ensure by annotation two annotators. quality The a sub-selection annotation files of were five dogsthen imported non-instrumented into Matlab walks using the were SALEM annotated by twotoolbox annotators. (Hanheide, The Lohse annotation and Dierker files et were al. [44]). then A importedcustom script into was Matlab written using to match the by SALEM time each toolbox (Hanheide,pair of swing Lohse phase and Dierkerannotations. et al. The [44 script]). A also custom gave feedback script was as to written any missing to match or additional by time steps each pair of swingin either phase annotator’s annotations. files. The The midpoints script also of each gave annotated feedback swing as to phase any missingwere calculated or additional (as the time steps in between FC and IC) and the mean time difference, in seconds, between annotators was calculated. either annotator’s files. The midpoints of each annotated swing phase were calculated (as the time The standard error of the mean time difference was also calculated. Provided together these metrics between FC and IC) and the mean time difference, in seconds, between annotators was calculated. give an indication of both the accuracy and consistency of annotations between annotators. The standard error of the mean time difference was also calculated. Provided together these metrics give an2.5. indication Effect of Instrumentation of both the accuracy and consistency of annotations between annotators.

2.5. EffectTo of Instrumentationdetermine if wearing the sensing equipment had any effect on normal gait pattern a comparison of control walks to instrumented walks was performed across all dogs. The lack of Toinstrumentation determine if wearingduring the the control sensing phase equipment meant only had the any duration effect onof gait normal features gait could pattern be aused comparison for of controlcomparison. walks The to instrumented instrumented and walks control was annotation performed files across were all imported dogs. Theto Matlab lack of using instrumentation SALEM. duringThe the mean control step duration, phase meant defined only as the the time duration between ofconsecutive gait features IC events, could were be calculated used for comparison.for both The instrumentedfiles for each anddog. controlThe instrumented annotation and files contro werel imported mean durations to Matlab were using then SALEM. correlated The using mean a step Pearson’s correlation to give an indication as to the amount of divergence between the two walking duration, defined as the time between consecutive IC events, were calculated for both files for each bouts. This was taken as an indication of the effect of the instrumentation on the gait. The same dog. The instrumented and control mean durations were then correlated using a Pearson’s correlation method was repeated using the mean swing durations. This was to investigate whether, not only was to givethe an step indication duration asunaffected to the amount but, the of composition divergence of between the step remained the two walking consistent bouts. with Thisthe normal was taken as angait indication post-instrumentation. of the effect of the instrumentation on the gait. The same method was repeated using the mean swing durations. This was to investigate whether, not only was the step duration unaffected but, the2.6. compositionComparing Annotations of the step and remained Predictions consistent with the normal gait post-instrumentation. To assess the performance of the system, at step detection, we first compared annotated steps to 2.6. Comparing Annotations and Predictions detected steps. This was performed using Matlab scripts which imported the annotation file and the Topre-processed assess the performancestep prediction of thedata. system, As videos at step were detection, captured we and first viewed compared in high annotated speed the steps to detectedannotation steps. files This first was needed performed adjust usinging to Matlabmatch the scripts times whichprovided imported by the sensors. the annotation The time origins file and the pre-processedof the two stepdata predictionsets were then data. aligned. As videos were captured and viewed in high speed the annotation To avoid the problem of neighboring steps being misinterpreted as false positives (FP), our files first needed adjusting to match the times provided by the sensors. The time origins of the two approach involved finding the center point between Final Contact (FC) and Initial Contact (IC) on data setsthe annotated were then steps. aligned. A time window the width of the maximum swing time observed within the dog Towas avoid then centered the problem on this ofcenter neighboring point. This stepswindow being provided misinterpreted the search area as for false the positives corresponding (FP), our approachswing involved phase mid-point finding in thethe centerpredictions. point The between true positives Final (TP), Contact FP’s (FC)and false and negatives Initial Contact (FN) were (IC) on the annotatedrecorded. steps.TPs were A timedefined window here as the those width annotate of thed maximumsteps with a swing single timepredicted observed step match. within FPs the dog was thenwere centeredidentified onas those this center steps of point. either This the predict windowed or provided annotated the datasets search with area multiple for the matches corresponding in the other dataset and those in the predicted dataset without a corresponding annotated step. FNs were identified as those annotations that could not be matched with a corresponding prediction. TP,

Sensors 2017, 17, 309 8 of 17 swing phase mid-point in the predictions. The true positives (TP), FP’s and false negatives (FN) were recorded. TPs were defined here as those annotated steps with a single predicted step match. FPs were identified as those steps of either the predicted or annotated datasets with multiple matches in the other dataset and those in the predicted dataset without a corresponding annotated step. FNs were identified as those annotations that could not be matched with a corresponding prediction. TP, FP and FN counts were collected on a per-leg, per-dog basis. The three metrics were summed to receive a total value of each which encompassed the performance of the prediction algorithm across all legs and dogs. These were then used to calculate the Positive Predictive Value (PPV) and sensitivity of the algorithm:

TP PPV = (1) TP + FP

The sensitivity is a metric of the proportion of steps correctly identiﬁed as steps and can be calculated as: TP Sensitivity = (2) TP + FN The F-score gives an overall indication of (but is not synonymous with) the accuracy of the prediction method [45]. Here the traditional balanced F-score is used which is the harmonic mean of PPV and sensitivity and is calculated as:

PPV·Sensitivity F − Score = 2· (3) PPV + Sensitivity

2.7. Performance Bias A preliminary investigation of possible sources of performance bias was performed through the use of a Linear Mixed Effects Model. The outcome measure was chosen as the error between annotated and predicted swing phases, measured in seconds. The height to withers (HTW-linear), age (linear) and sex (two levels: male or female) were chosen as potential sources of bias in the algorithm’s performance. The subject was included as a random effect to account for any differences that were not collected and accounted for here. The leg the sensors were attached to was also included as a nested variable within the subject. The model formula was therefore:

Error ~ HTW + Sex + Age + (1|Dog/Leg) (4)

Four other variations of this model were constructed for comparison using likelihood ratio tests. Each of these variations removed one effect variable. Models were run and analyzed in R (Version 3.3.1, R Core Team, Vienna, Austria, 2015) using the LME4 package [46].

3. Results The average time taken to make a measurement on the dog was 17 min. This was typically broken down as: 3 min to talk to the owners; 3 min to conduct the control walk; 5 min for the habituation to equipment; 1 min to set-up instruments once habituated; 2 min to attach sensors; and 3 min to conduct the instrumented walk. 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. Data from all 19 dogs was successfully captured with the sensors and subsequently analyzed. Across all four legs of all nineteen dogs, a total of 15,660 steps were annotated. Step frequencies of the four legs, across all dogs, were 3922, 3844, 3780 and 3937 for the front left (FL), front right (FR), hind left (HL) and hind right (HR) legs, respectively, and as such do not appear to differ. However, the number of steps taken did appear to vary greatly between dogs with a range of 489 steps per dog to 1255 steps per dog and a mean of 824 steps per dog (230 SD). Step frequency was signiﬁcantly negatively correlated to the HTW of the dog (r = −0.903, n = 19, p < 0.001; Figure6) which is consistent with Heglund et al. [47]. Sensors 2017, 17, 309 9 of 17 Sensors 2017, 17, 309 9 of 17

FigureFigure 6. Plot 6. ofPlot height of height to withers to withers vs. stepvs. step frequency. frequenc They. The linear linear relationship relationship closely closely resemblesresembles the one previouslyone previously reported reported in Heglund in Heglund et al. [ 47et ].al. [47].

3.1. Inter-Rater3.1. Inter-Rater Reliability Reliability The videosThe videos captured captured were were annotated annotated byby twotwo trainedtrained annotators annotators using using the the same same ethogram ethogram (Appendix A). No mismatches between annotators were reported when identifying steps; perhaps (AppendixA). No mismatches between annotators were reported when identifying steps; perhaps because of the very obvious leg-swing. The mean difference in the swing phase mid-points recorded becauseby ofthe the annotators very obvious was 0.016 leg-swing. s (±0.000 The Standard mean differenceError of the in Mean the swing (SEM)) phase which mid-points corresponds recorded to 3.8 by the annotatorsframes at the was video 0.016 capture s (±0.000 rate. Standard This is approximately Error of the equal Mean to (SEM)) 5% of the which mean corresponds swing phase toduration 3.8 frames at the(0.315 video s) capture which falls rate. well This below is approximately the 10% degree equal of error to 5%deemed of the acceptable. mean swing The low phase error duration and SEM (0.315 s) whichvalues falls suggest well below the annotations the 10% degree made of were error high deemedly similar acceptable. and demonstrated The low error a high and SEMlevel valuesof suggestconsistency. the annotations made were highly similar and demonstrated a high level of consistency.

3.2. Effect3.2. Effect of Instrumentation of Instrumentation A visibleA visible change change in locomotionin locomotion was was noticeable noticeable immediately after after the the bandages bandages were were attached. attached. No dogsNo dogs in the in cohortthe cohort showed showed any any signs signs of of pain pain oror discomfortdiscomfort but but all all show showeded a varying a varying degree degree gait gait abnormality (increased step height and variable pace), although the effect was transient. All dogs abnormality (increased step height and variable pace), although the effect was transient. All dogs seemed to habituate to the instruments within a 3–10 min time span and habituation was found to seemed to habituate to the instruments within a 3–10 min time span and habituation was found to accelerate with dog mobility. For this reason, encouragement with use of toys or treats was given to acceleratemove withduring dog habituation. mobility. For this reason, encouragement with use of toys or treats was given to move duringTo quantify habituation. the effects of instrumentation the average step durations of those steps annotated Tofor quantify the control the and effects instrumented of instrumentation walks were correla the averageted against step each durations other for of each those dog. steps The annotated same for thewas control performed and instrumentedfor the average swing walks phase were durations. correlated The against step duration each other showed for a eachstrong dog. significant The same was performedcorrelation for(r =the 0.88, average n = 19, swingp < 0.001; phase Figure durations. 7a). The swing The step phase duration duration showed also gave a strong a strong signiﬁcant and correlationsignificant (r = correlation 0.88, n = 19, (r =p 0.76,< 0.001; n = 19, Figure p < 0.001;7a). Figure The swing 7b). phase duration also gave a strong and signiﬁcant correlation (r = 0.76, n = 19, p < 0.001; Figure7b).

SensorsSensors2017 2017,,17 17,, 309 309 1010 ofof 1717 Sensors 2017, 17, 309 10 of 17

Figure 7. (a) Pearson’s correlation between Control and Instrumented step duration (r = 0.88); FigureFigure 7.7. ((a)) Pearson’s correlation between Control and Instrumented step duration (r == 0.88); (b) Pearson’s correlation between Control and Instrumented swing duration (r = 0.76). ((bb)) Pearson’sPearson’s correlationcorrelation betweenbetween ControlControl and and Instrumented Instrumented swing swing duration duration (r (r = = 0.76).0.76). 3.3. Comparing Annotations and Predictions 3.3.3.3. Comparing Annotations andand PredictionsPredictions A total of 15,671 steps were predicted to have occurred across all dogs and legs. Of these a total AA totaltotal ofof 15,67115,671 stepssteps were were predicted predicted to to have have occurred occurred across across all all dogs dogs and and legs. legs. Of Of these these a total a total of of 12,678 were accepted as TPs (80.9%) and 2993 were rejected as FP (19.1%). 2680 of the 15,660 12,678of 12,678 were were accepted accepted as TPs as (80.9%)TPs (80.9%) and 2993 and were 2993 rejected were rejected as FP (19.1%). as FP 2680(19.1%). of the 2680 15,660 of the annotated 15,660 annotated steps were not matched with a prediction and were therefore labelled as FN (17.1%). stepsannotated were steps not matched were not with matched a prediction with a andprediction were therefore and were labelled therefore as FNlabelled (17.1%). as FN (17.1%). Of the steps accepted as TP, 7249 were annotated as walking with 1965 of these recorded from OfOf thethe stepssteps acceptedaccepted as as TP, TP, 7249 7249 were were annotated annotate asd as walking walking with with 1965 1965 of theseof these recorded recorded from from the the front left (FL) leg, 1790 from the front right (FR), 1726 from the hind left (HL) and 1768 from the frontthe front left (FL)left (FL) leg, 1790leg, 1790 from from the front the front right right (FR), (FR), 1726 1726 from from the hind the hind left (HL) left (HL) and 1768 and from1768 thefrom hind the hind right (HR). 4977 TP steps were annotated as trotting of which 1361 were from the FL, 1250 from righthind (HR).right (HR). 4977 TP4977 steps TP weresteps annotatedwere annotated as trotting as tro oftting which of which 1361 were 1361 from were the from FL, the 1250 FL, from 1250 the from FR, the FR, 1176 from the HL and 1190 from the HR. 1176the FR, from 1176 the from HL andthe HL 1190 and from 1190 the from HR. the HR. The PPV for all dogs and legs was 0.81 and Sensitivity 0.83. The resultant F-Score was 0.82. TheThe PPVPPV forfor allall dogsdogs andand legslegs waswas 0.810.81 andand SensitivitySensitivity 0.83.0.83. The resultant F-Score was 0.82. Figure 8 shows a PPV-Sensitivity plot of each dog’s performance with each dog color coded by their FigureFigure8 8 shows shows a a PPV-Sensitivity PPV-Sensitivity plot plot of of each each dog’s dog’s performance performance with with each each dog dog color color coded coded by by their their HTW. As can be seen the 16 of the dogs are grouped in the top right section of the graph. There are HTW.HTW. AsAs cancan bebe seenseen thethe 1616 ofof thethe dogsdogs areare groupedgrouped inin thethe toptop rightright sectionsection ofof thethe graph.graph. ThereThere areare however, three outliers which report poor Sensitivity scores. Two of the three outliers are those with however,however, threethree outliersoutliers whichwhich reportreport poorpoor SensitivitySensitivity scores.scores. Two ofof thethe threethree outliersoutliers areare thosethose withwith the lowest HTW measurements. thethe lowestlowest HTWHTW measurements.measurements.

FigureFigure 8.8.Results Results of PPVof PPV and Sensitivityand Sensitivity calculations calculations showing showing four outliers four andoutliers no signiﬁcant and no interactionsignificant Figure 8. Results of PPV and Sensitivity calculations showing four outliers and no significant betweeninteraction height between and systemheight performance.and system performance. interaction between height and system performance.

Sensors 2017, 17, 309 11 of 17

The exclusion of these outliers results in an improvement in overall performance. The PPV improves by 0.03 to 0.84 and the Sensitivity improves more significantly, by 0.09, to 0.92. The F-Score also improves to 0.88. Sensors 2017, 17, 309 11 of 17 3.4. Processing of IMU Data TheThe exclusion total number of these of steps outliers used results for performance in an improvement evaluation in was overall 5041 performance. (FL = 1312, FRThe = PPV 1310, improvesHR = 1217, byHL 0.03 = to 1202). 0.84 and Mean the errors Sensitivity for FC improves and IC detection more significantly, are listed in by Table 0.09,1 toand 0.92. distribution The F-Score of alsodisagreement improves to between 0.88. annotations and predictions are shown graphically for walking and trotting in Figures9 and 10. 3.4. Processing of IMU Data For both walk and trot gaits, the results demonstrate that there is no significant bias towards left orThe right total paws. number It is alsoof steps clear used that therefor performance is no bias between evaluation front was and 5041 hind (FL limbs = 1312, for initial FR = contact 1310, HRdetection. = 1217, HL The = performance 1202). Mean errors for final for contact FC and detection IC detection is slightly are listed better in Table on the 1 and fore distribution limbs than theof disagreementhind limbs. between annotations and predictions are shown graphically for walking and trotting in Figures 9 and 10. Table 1. The mean error (seconds) for the initial and final contacts during both the walking and trotting Tablegaits of1. eachThe leg.mean error (seconds) for the initial and final contacts during both the walking and trotting gaits of each leg. Foot Walking FC Walking IC Trotting FC Trotting IC Foot Walking FC Walking IC Trotting FC Trotting IC FL 0.042 0.015 0.018 0.028 FL 0.042 0.015 0.018 0.028 FR 0.035 0.008 0.004 0.011 FR 0.035 0.008 0.004 0.011 HL 0.084 −0.006 0.072 0.008 HL 0.084 −0.006 0.072 0.008 HR 0.071 0.012 0.056 0.012 HR 0.071 0.012 0.056 0.012

F ro n t L e ft IC d e te c tio n e rro r. W a lk in g F ro n t R ig h t IC d e te c tio n e rro r. W a lk in g 200 200

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0 0 -0.5 0 0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 Figure 9. Disagreement between predictions and annotations during walking. Figure 9. Disagreement between predictions and annotations during walking.

Sensors 2017, 17, 309 12 of 17 Sensors 2017, 17, 309 12 of 17

F ro n t L e ft IC d e te c tio n e rro r. T ro ttin g Front Right IC detection error. Trotting 50 50

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FigureFigure 10. 10Disagreement. Disagreement betweenbetween predictions predictions and and annotations annotations during during trotting. trotting.

For both walk and trot gaits, the results demonstrate that there is no significant bias towards left 3.5. Performanceor right paws. Bias It is also clear that there is no bias between front and hind limbs for initial contact detection.A total of The five performance statistical modelsfor final werecontact built detection for comparison. is slightly better The on first the wasfore limbs the full than model the hind asstated in thelimbs. formula above. Three other models were constructed, each identical except in their omitting of one3.5. of Performance the fixed Bias effects (HTW, Age and Gender). The final model dealt with the effect of the Dog random variable and required the expansion of the full equation given above to the long form. Each of A total of five statistical models were built for comparison. The first was the full model as stated these were compared to the full model with LRTs. in the formula above. Three other models were constructed, each identical except in their omitting of Age was not found to have a significant effect on error (χ2(1) = 1.9991, p = 0.1574), neither were one of the fixed effects (HTW, Age and Gender). The final model dealt with the effect of the Dog χ χ HTWrandom ( 2(1) variable = 0.698, andp = required 0.4035) the or sexexpansion ( 2(1) of = the 0.3496, full equationp = 0.5543). given However, above to the the long dog form. random Each effect wasof found these towere be highlycompared significant to the full ( χmodel2(1) = with 21.538, LRTs.p < 0.001). Age was not found to have a significant effect on error (χ2(1) = 1.9991, p = 0.1574), neither were 4. DiscussionHTW (χ2(1) = 0.698, p = 0.4035) or sex (χ2(1) = 0.3496, p = 0.5543). However, the dog random effect wasThis found paper to introducesbe highly significant what we (χ believe2(1) = 21.538, is the p < first 0.001). sensor based gait analysis targeted towards canines. The system shows promise to enable field based measurements conducted in non-clinical 4. Discussion settings. This would therefore enable the combination of both clinic-based assessments and longitudinalThis measurementspaper introduces when what the we dogbelieve is in is athe home first environment.sensor based gait Such analysis systems targeted have demonstratedtowards theircanines. utility The in human system shows clinical promise practice to [enable35] and fiel tod based some measurements extent with equineconducted species in non-clinical [29,34]. From settings. This would therefore enable the combination of both clinic-based assessments and a usability standpoint, the system was easy to configure and after brief temporary gait alterations longitudinal measurements when the dog is in a home environment. Such systems have (afterdemonstrated the devices weretheir utility attached in human to the dogs’clinical limbs), practice the [35] subjects and to showedsome extent good with compliance. equine species While the cohesive[29,34]. bandage From a usability solution standpoint, did not appear the system to allow was foreasy sensor to configure movement and after on thebrief limb, temporary it is considered gait an areaalterations where (after improvement the devices towards were attached practicality to the dogs’ could limbs), be made. the subjects The main showed concern good with compliance. the cohesive bandage approach is the usability it affords within a busy clinical setting; a specialized sock that constrains sensor orientation and position but doesn’t restrict movement would be more favorable. A specialized sock would also have the benefit of added standardization the position of the sensors Sensors 2017, 17, 309 13 of 17 around the circumference of the limb and with respect to the joint. Within this experiment neither were controlled for and are an anticipated source of error. The initial step to validate the system was to provide evidence that the sensors themselves did not affect the normal gait of the subjects. The strong, significant correlations of both the entire step duration and the swing phase demonstrate that instrumentation has no significant effect on the temporal aspects of the dogs’ gait. Results demonstrate a good capability to delineate steps and reject shuffles or stumbles (which produce signals unclassifiable by the FC/IC detection methods). In total, three signal features were used to segment out final and initial contact times for thoracic and pelvic limbs. The features were chosen for their high-contrast properties; which makes for robust automatic extraction and low opportunity for false positives. Performance, both before and after the exclusion of the outlying dogs, was promising. The F-scores provide a simple, single number impression of the prediction methods ability and indicate that the system is effective at measuring gait micro-features. To provide a more detailed impression of the performance these should again be broken down to their constituent parts. The PPVs both pre- and post-exclusion indicate the script can be said to be correctly identifying between 81% and 84% of its identified steps correctly. The sensitivity scores of 0.83 and 0.92 indicate that those steps identified correctly represent 83% and 92% of the total steps annotated. Put more simply, and in terms of the better performing model, of the steps reported as positive approximately 84% will be TP and those will directly correspond to 92% of the number of the potential total TPs. To put this context a recent paper studying the sensitivity of a force plate method in dogs to distinguish low grade lameness, found a sensitivity of 0.63 [48]. The cause of the three outliers’ poor performance scores remains ambiguous; no apparent pattern emerged between them. The linear mixed effects model excluded some potential candidate causes and furthermore, the error values presented in Table1 suggest no bias towards the left or right, or the pelvic or thoracic limb pairs. Alternative hypotheses to the source of the outliers warrant further investigation. Sensor and video synchronization could partially explain disagreements between annotations and predictions. The video was captured at 240 fps giving a temporal resolution of ~4ms between frames. The sensor data was captured at 100 Hz giving a temporal resolution of 10ms between samples. Synchronization between video and sensor was achieved through identifying the frame that corresponded to a tap on the sensor. However, as the sensor has three axes a tap impulse may not necessarily result in sample-aligned spike on all of them; the x axis was arbitrarily chosen for synchronization purposes. Additionally, when making taps on the sensor, no control was placed on the orientation of the sensor. This setup results in a possible uncertainty margin of 14 ms. As the sensor sample rate and video frame rate were constant, once synchronization was performed any time misalignment would remain consistent throughout that capture period and thus any synchronization based error would be systematic over all measurements. Therefore, when making measurements outside of an experimental environment, synchronization errors of this nature would not necessarily be of any issue and would not affect the usability of the system or the repeatability of results. The experiments described were designed to assess the IMU’s suitability at precisely detecting final and initial contact times. Although not of interest in isolation they are the fundamentals of calculating many parameters [6,34] including step time, stance time, swing time. Asymmetries and variabilities in these measures have been shown in human studies to be good predictors of various disease states. Thus, a system that can objectively measure with repeated precision final and initial contact times is likely to be of interest to both the canine research and veterinary communities. In addition, based on accurate dissection of step phase, parameters such as jerk, velocity, length and width can be calculated based on other signals directly available from the IMU sensor [9,49–51]. Such parameters are readily available from instrumented walkways and used for assessing lameness. An equivalently precise system that can be used in an unconstrained environment thus has the potential to be of interest to veterinary gait specialists [39,52]. Sensors 2017, 17, 309 14 of 17

The approaches presented could be integrated into a system that has potential to objectively quantify gait. It is anticipated that the margins of measurement error are sufficiently low such that subtle gait abnormalities can be detected. With careful design of a data-collection protocol, measurements made over a longitudinal time frame could be compared. Presently, the methods are untested outside of a controlled environment and it is anticipated that several challenges would need to be overcome before deployment in such a scenario. Practically, the sensors would need to be attached via more suitable mechanism that would prevent the dog chewing, tampering or destroying the attachment method when left unsupervised. On a processing level, further thought would need to be made on how to segment and compare measurements gathered while cornering to those gathered while walking straight. Movement at speed and the effects of fatigue upon gait would also be of interest for the deployment of this system in the measurement of gait in a more naturalistic setting than that used here [52–54]. Furthermore, an automatic mechanism for classifying gaits is needed such that inter-gait based measurements are not grouped. Once addressed the system can begin to be used to investigate gait abnormalities. Throughout the evaluation experiments, much was learnt about the practicality of making such measurements; particularly the importance of the habituation period and the ability to accelerate it by encouraging movement and providing additional, engaging and distracting stimuli, such as toys and treats. The sensors used in the experiments featured wireless data capture. While convenient there was the occasion where the wireless range of the devices became prohibitive and thus on a practical level, it would be desirable to have a memory buffer storage option in future iterations. As previously stated the attachment method was also not ideal and will require further refinement prior to future deployments. Although the system worked without bias on the healthy test dogs (which included a range of sizes and breeds), there is still significant work that needs to be done before the system is fit for purpose in a clinic. For example, as in human gait analysis, standardized collection protocols should be developed and fully benchmarked against. Such an exercise is the first step to being able to ascertain what a “normal” gait is on an inter-dog basis. Additionally, such standardization could assist in the investigation of the random variables leading to poor performance in a small portion of the sample. This should be accompanied by the collection of more detailed information regarding the dog’s physiology and history in an attempt to identify the cause of the errors reported here to more extensively correct, account for and recognize such problems in future deployments.

5. Conclusions This research evaluated the base component of an IMU-based gait measurement system compatible for use with canines. These results demonstrate good sensitivity and repeatability at a precision that is likely to be sufficient to identify clinically relevant gait abnormalities in dogs. In human movement science, such systems have found uses in diagnostics, rehabilitation and research into intervention effectiveness. It is anticipated similar benefits could be realized if the evaluated detection methods were to be developed into an end-to-end solution for canine veterinary practice. Once refined such a system will provide an exciting analogue to those systems currently showing extensive success in human research and medicine and will assist in providing further insight into the health and welfare of canines for use by their owners, veterinarians and researchers. Furthermore, the establishment of an open source solution for use with dogs could be adapted for use with other domestic quadruped mammals.

Acknowledgments: Funding was provided by a Newcastle University Medical Faculty Fellowship awarded to Lucy Asher, a studentship awarded to Jack O’Sullivan and a DTP studentship awarded to Zoe Belshaw with funding from the Centre for Evidence-Based Veterinary Medicine. BBSRC reference number BB/J014508/1. Author Contributions: Cassim Ladha: initial concept, developed detection algorithms, contributed equipment, helped in data collection, wrote the paper. Jack O’Sullivan: analysis and results section, developed collection protocol, helped in data collection, wrote the paper. Zoe Belshaw: developed collection protocol, expert gait Sensors 2017, 17, 309 15 of 17 assessment, wrote the paper. Lucy Asher: secured funding, organised recruitment, ethical application, wrote the paper, processed results. Animal Ethics: All recruitment and experiments were conducted in accordance with Newcastle University Ethics procedure. Informed consent was granted from all client owned animals recruited for use in this study. Conﬂicts of Interest: Z.B., J.O., L.A. have no conﬂicts of interest. C.L. is the owner of Vetsens. This company manufactures the sensor hardware used in the trial. This does not alter the authors adherence to MDPI’s standards on sharing data and materials.

Appendix

Table A1. Ethogram.

Annotated Gait Phases and Types Description The moment where all areas of the foot have left the Final Contact (FC) ground. Signifies the start of the swing phase. The moment where the first area of the foot again contacts Initial Contact (IC) the ground. Signifies the end of the swing phase. The period of time between FC and IC where the focal Swing Phase foot is not in contact with the ground. Gait pattern in which one leg is in the swing phase at any Walk time and the other three support the body. Gait pattern in which legs move in diagonal pairs (Front Trot Left and Hind Right; Front Right and Hind Left).

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