Age Estimation from Dual-Task Behavior forComprehensive Growth Assessment of Children
Chengju ZhouISIR, Osaka UniversityIbaraki, Osaka, JapanEmail: zhou@am.sanken.osaka-u.ac.jp
Ikuhisa MitsugamiHiroshima City UniversityHiroshima, Hiroshima, JapanEmail: mitsugami@hiroshima-cu.ac.jp
Kota AokiISIR, Osaka UniversityIbaraki, Osaka, JapanEmail: aoki.k@am.sanken.osaka-u.ac.jp
Fumio OkuraISIR, Osaka UniversityIbaraki, Osaka, JapanEmail: okura@am.sanken.osaka-u.ac.jp
Yasushi YagiISIR, Osaka UniversityIbaraki, Osaka, JapanEmail: yagi@am.sanken.osaka-u.ac.jp
Abstract—This paper presents age estimation of school-agechildren from dual-task behavior, which is known to be af-fected by cognitive ability. By observing a school-age participantperforming the dual-task with Kinect and other sensors, ourdual-task experience system measures his/her anthropometric,kinematical, and cognitive aspects of information simultaneously.We collected observations of more than a thousand school-agechildren, applied regression for estimating ages from each aspectand all of them, and discussed their differences. As a result,we found that age estimation using the all aspects is the best.This fact indicates that our age estimation could be useful forcomprehensive growth assessment of children.
I. INTRODUCTION
Within the growing process of children, various body char-acteristics are developing with increasing age [1], [2], e.g.,body height becoming taller, walking style becoming morestable and learning ability improving with age increasing. Sochild’s age can be estimated by these features. For example,Shi et al. use the performance of physical examinations to esti-mate age for elementary school children [3], and Moshman [4]studies relation between cognitive development and age ofchildren. But, a common problem in those existing studiesis that they only explored one aspect of growth information,which is not sufﬁcient information for age estimation.In this paper, we adopt dual-task observation to overcomethis problem. Dual-task, which means doing two tasks (usuallycombination of physical task such as walking and stepping,and cognitive task like arithmetic calculation), is originallyproposed for rehabilitation of elderly people with cognitivedisability, but useful for our purpose; by adopting this dual-task methodology, we can obtain anthropometric, kinematical,and cognitive features at the same time. The anthropometricfeature is simply obtained from range and joint data of aparticipant. The kinematical and cognitive features are fromperformance of the physical and cognitive tasks, respectively.We develop a data-collection system to easily and auto-matically record a participant’s dual-task behavior, and locateit in a national science museum in Japan, so that we ﬁnally
collect more than a thousand school-age children data. Dual-task performance of each participant is subsequently quantiﬁedby various features. We estimate his/her age with both separateaspects or combination of all aspects of the features, andcompare performance of them to ﬁnd the best one. We thenconﬁrm that using the combination of all different aspects offeatures gives the best performance comparing using only oneaspect. And its accuracy is less than one year. Since we collectthe data from ordinary children and the number of subject isrelative large, in statistical sense, our age estimation usingall aspects of information could be used for comprehensivegrowth assessment of children.
II. DATA COLLECTION AND FEATURES EXTRACTION
We design a system to automatically record various mea-surements about the participants who are performing dual task.We need to consider the way to realize automation and themethod to guarantee the ethical rights of the participants. Oursolution is as follows: Before each step of the procedure,guidance is displayed on the screen for participants. At thebeginning of the experiment, we explain to participants thatwhat kind of information will be recorded and ask for theiragreement to use this information for research purpose. At theend, the participant is provided an opportunity again to refusethe agreement again after checking the captured data.For dual task, we adopt stepping in place as our motor task,and use calculation of addition or subtraction between one andtwo digits (e.g. 74+2 =?) as the cognitive task. The MicrosoftKinect sensor is used to track each participant’s steppingmovement of the whole body. The color image sequence, depthmap sequence and the time series of 3D coordinates of thebody joints are captured. Fig.1 shows out an example of thesedata collected from a participant. In addition to those physicalmeasurements, features about the dual task (responding timeand correctness of answer) is also recorded. Throughout thelong-term exhibition in a national science museum in Japan,we collected data from from 1113 children aged from 6 to 15years old.
(a)(b)(c)(d)
Fig. 1. Data captured by Kinect sensor. (a) color image; (b) depth map (onlyhuman region) (c) and (d) are the skeleton image from front view (X-Y plane)and lateral view (Z-Y plane)
From the collected data, we quantitatively describe threeaspects of information with various features. Cognitive infor-mation which is about the performance of answering arith-metical questions, is expressed by average of answer timeand correct answer ratio. As anthropometrics, we measurebody height from 3D point cloud which is converted fromdepth map, lengths of arm span/legs calculated from 3D jointskeleton and also their ratios (e.g. ratio of length of arm span toheight). Kinematical characters which describe the movementof stepping is also extracted from time series of 3D jointposition. We use the speed and variability in temporal andspacial space of all the joints (except some joints at tip) andaxes to describe the stepping performance.
III. AGE ESTIMATION
A. Regression and evaluation method
The regression method we use for age estimation is gradientboosting regression [6] which uses additive strategy to learnbasis model with forwards stage-wise fashion. Here we chooseclassiﬁcation and regression tree (CART) as basis model (weaklearner). The most important advantage of this method isthat with the CART as basis model, it does not require anyassumptions of linearity in the data.We repeat 5-fold cross-validation 5 times to evaluate theregression performance. The performance of age estimationis measured by Mean Absolute Error (MAE). Given theestimated age ˆati and ground truth age ati for the ith testsample, the deﬁnition of MAE is:
i=1|ˆati − ati|,
where N t the number of test samples. The reported MAE isthe average of those 25 execution mentioned above. Statisticalsigniﬁcance tests are performed using one-way analysis ofvariance (ANOV) with the null hypothesis that two sets ofsamples are drawn from populations with the same mean.
B. Results and discussion
The average of MAE in table I are different with each othersigniﬁcantly (p-value ≪ 0.001). Observing MAE results, weﬁnd that the MAE of the anthropometric aspect feature is
TABLE IPERFORMANCE COMPARISON OF DIFFERENT FEATURES
AspectsMAE[years]
Cognitive1.57
Anthropometric0.90
Kinematical1.38
Combination (in feature level)0.85
smallest, which means the anthropometric aspect is the mosteffective for age estimation. The kinematical aspect is thesecondly effective, while the cognitive one is the worst.When concatenating all the aspects of features, the MAEdecreases statistically signiﬁcantly to 0.85 years. This meanscombination features have the most discriminative ability todescribe growth condition of children comparing with singleaspect of information. The improvement is not so large com-pared with that from the anthropometric features, which meansthat the two other aspects do not provide much contribution.It might be because they do no not develop with age as fastas anthropometry do. On the other hand, the kinematical andcognitive aspects show measurable contribution (MAE is muchsmaller than chance level of 2.5 years) for age estimation.
IV. CONCLUSION
Motivated by the fact that dual-task behavior is usefulfor evaluating cognitive ability, we investigate age estimationperformance using dual-task observation. Since the dual-taskbehavior is captured by Microsoft Kinect, we can obtain an-thropometric and kinematical features as well as the cognitiveone, so that we can investigate which feature and whichcombination gives the best performance for the age estimation.The experiment results using more than a thousand school-agechildren data revealed that using all the features gives the bestperformance and its accuracy is less than one year. Consideringthe accuracy under the experiments with quite large numberof ordinary school-age participants, our age estimation canbe regarded as a good measure for assessing the childrencomprehensive growth.
REFERENCES
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