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Canine Emotional States Assessment with HeartRate Variability
Eri Nakahara∗, Yuki Maruno∗, Takatomi Kubo†, Rina Ouchi†, Maki Katayama‡,Koichi Fujiwara§, Miho Nagasawa‡¶, Takefumi Kikusui‡, and Kazushi Ikeda†
∗ Kyoto Women’s University, Kyoto, Japan, E-mail: [email protected]† Nara Institute of Science and Technology, Nara, Japan, E-mail: [email protected]
‡ Azabu University, Kanagawa, Japan, E-mail: [email protected]§ Kyoto University, Kyoto, Japan E-mail: [email protected]
¶ Jichi Medical University, Tochigi, Japanm E-mail: [email protected]
Abstract—Emotions of a person affect the person’s perfor-mance in a task and so do emotions of a rescue dog thatworks after disasters. Hence, estimating emotions of a rescuedog by the handler can improve its performance and welfare.Emotions also appear in physiological signals such as heart ratevariability (HRV). In fact, HRV has information of emotions inboth cases of human and dogs. To make emotion estimation morepractical, we proposed a method for emotion estimation fromHRV of dogs and evaluated its performance using real data. Themethod classified positive, negative, and neutral emotions with88% accuracy within each subject and 72% over all subjects.These accuracies are high enough for practical use in rescuedogs.
I. INTRODUCTION
Emotion, which is critical for working and learning, affects aperson’s performance both mentally and physically. For exam-ple, a strong motivation in rehabilitation improves performance[1]. This effect can even be applied to animals [2]. If theanimals’ handlers recognize the emotions more correctly, theycan control the animals better. One of such situations is rescuedogs that finds injured people after disasters. A rescue dogshows a lower performance when it has negative emotions. Ifthe dog’s handler recognizes the negative emotions or fatigues,s/he can find a substitute to maximize the performances ofrescue dogs.
Canine emotional states are estimated from physiologicaldata since emotion induces such changes as oxcytocin secre-tion, cortisol concentration, heart rate and heart rate variability(HRV) [3], [4], [5], [6]. Among the physiological data above,HRV of a dog is easier to measure and changes dependingon the emotional situation of the dog [6]. In practical useof emotion information, however, we need to develop anestimation system of the emotions. In this paper, we proposeda classifier to estimate emotions of a dog from HRV andevaluated its accuracy using real data.
II. MATERIALS AND METHODS
The RRI data used in this study were the same as those usedin [6]. 39 healthy dogs and their owners in Azabu Universitywere recruited (Age, mean ± SE = 4.56 ± 0.56 years). Theexperiment was approved by the ethical committee of AzabuUniversity.
Fig. 1. Sensors equipped to a dog.
Fig. 2. An example of RRI in the experiments.
A. RRI data collection
The dogs’ electrocardiogram (ECG) was collected with sur-face electromyography sensor (TS-EMG01, ATR Promotions,Japan) at an amplification factor 250×, filter 0.5-150 Hz, andsampling rate 1 kHz. The sensor was attached to each dog’sback between the scapulae (Fig. 1) and three electrodes wereplaced on the chest along the sternum.
The ECG data were processed by our original MATLAB(mathworks.com) script to detect R waves, calculate RRintervals (RRI) automatically, and correct results interactivelywith visual inspection (Fig. 2).
Fig. 3. The procedure of the experiment.
TABLE IFEATURES OF RRI
meanNN Mean of RRI.SDNN Standard deviation of RRI.
RMSSD Root mean square of adjacent RRI differences.Total Power (TP) Variance of RRI.
NN50 The number of adjacent RRI pairs which have a differencemore than 50 ms within a given time-window.
Fig. 4. Examples of features of HRV.
B. Procedure
The dogs’ owners wore a transceiver to receive cues froman experimenter and changed their behavior according to thecues.
The experimenter sent an owner the first cue when theowner’s dog laid down spontaneously in an hour, which wasregarded as an indication of habituation to the room andthe ECG sensor. Otherwise, the experiment was stopped.Receiving the first cue, the owner stayed with the dog forfive minutes (neutral condition) until the second cue. Then,the owner called the dog’s name and gently petted it forfive minutes (positive condition) until the third cue and thenreturned to the neutral condition for five minutes. Receivingthe fourth cue, the owner departed from the experiment roomand left the dog alone for five minutes (negative condition)until the fifth cue and then returned to the room and the
experiment was over (Fig. 3).To remove the order effect, half of the dog-owner pairs were
replaced the order of the positive and negative conditions.
C. Preprocessing
Each time series was split into 10-second intervals. Theframes that include missing values due to arrhythmia wereexcluded from the analysis below.
D. Classification of emotions
The features of HRV in Table I were extracted from RRIdata in each interval (Fig. 4) [6], [7].
Although each of the features had statistically been testedto see whether it changes according to emotions [6], all of thefeatures were input to the classifier because a combination ofthe features might be more effective.
Fig. 5. Estimation accuracy of the Random Forest classifier. (a) estimationwithin a subject (b) estimation over subjects
In our study, Random Forest [8] was used as a classifiersince it is well-known as a high generalization performanceclassifier in the machine learning community. Random Forestis a kind of ensemble learning methods where each learningmachine is a decision tree trained with resampled data. In ouranalysis, the R package “randomForest” [9] was employed.The parameters of Random Forest were chosen by grid searchso that its performance was maximized using the cross vali-dation [10].
E. Evaluation
We used 70% of the collected data for training and theremainder to evaluate the estimation ability.
The estimation was done in two ways: Estimation within asubject and estimation over all subjects. In the former, eachclassifier was trained using the dataset from one dog andevaluated using the same dataset. In the latter, the data fromall the dogs were mixed for training and evaluation.
III. RESULTS
10 of 39 dogs were excluded from the analysis because threedogs did not lie down within an hour, three failed in recordingtheir RRI due to equipment trouble, one failed in loading theirRRI due to file collapse, and three showed unusual behaviorduring the positive condition [6].
As a result, Random Forest classified positive, negative, andneutral emotions with 88% accuracy within one subject and72% over all subjects (Fig. 5).
IV. DISCUSSION
Although the accuracy of 72% over all subjects is not high,handlers can collect the RRI data and construct a classifier foreach rescue dog in advance. Hence, the classifier is practicalfor rescue dogs since the accuracy of 88% within a dog ishigh enough.
These accuracies of Random Forest are higher than theaccuracies when the classifier uses the features extracted in [6],that is, SDNN for the positive condition and RMSSD for thenegative condition. The accuracy of SDNN was 53% within adog and 39% over all subjects, and that of RMSSD was 56%within a dog and 39% over all subjects. This improve impliesthat combining features using machine learning technique ismore efficient than choosing one using statistics.
These accuracies are a little lower than that of 72% over allsubjects and that of 91% within a dog where the accelerometerdata were used as features [11]. A possible reason is becauseECG signals have lower signal-to-noise ratio compared toacceleration in noisy environments. The combination of thesensors may improve the accuracies.
V. CONCLUSIONS
We proposed a method for emotion estimation from HRV ofdogs. The method classified each dog’s emotional state with88% accuracy on average when it was trained with the dog’sdata. This accuracy is high enough for practical use in rescuedogs.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant Num-ber JP25118007, JP15H01620 and ImPACT Program of Coun-cil for Science, Technology and Innovation (Cabinet Office,Government of Japan) 2015-PM07-36-01.
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