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The impact of Japanese Economy by Emotion sensing communication.

Byline: hironao takahashi and rikyo takahashi

Since 1990, Japanese economy has been suffering its increasing rate of GDP due to economic crisis. But recently situation of Japanese economy is very positive and Tokyo stock exchange Nikkei market value was exceeded 20,000JPY these days. GDP is main indicator of economy measurement but the number of population and its age formation is big factor. After Dec 2004, Japanese population was decreasing and the age of more than 65 years old people are exceeding more than 30% from total population at 2015. We look at to enhance GDP value, we propose emotion sensing web service to enhance the buying power from more than 65 years old people. We selected GSR value to sense the people's emotion and design Ontology analysis engine for emotion sensing web service. Ontology rule engine was programmed by OWL, Protege API and JINA API with own inference layer. Evaluation of GSR sensing with more than 65 years old people and estimate Ontology analysis accuracy compare non Ontology analysis.

It shows more than 90% accuracy. Japanese GDP impact value was also simulated under the expectable scenario of more than 65 years old people. By our simulation, the emotion sensing web service boosts the value of Japanese GDP more than 93 trillion JPY at 2040 compares the original GDP value.

Keywords: Japanese GDP, Emotion sensing, Ontology

Introduction

Japan is the world's third largest automobile manufacturing country, has the largest electronics goods industry, and is often ranked among the world's most innovative countries leading several measures of global patent filings. Facing increasing competition from China and South Korea, manufacturing in Japan today now focuses primarily on high-tech and precision goods, such as optical instruments, hybrid vehicles, and robotics. Besides the Kant region, the Kansai region is one of the leading industrial clusters and manufacturing centers for the Japanese economy. About the GDP of Japan, it is the third largest in the world by nominal GDP, the fourth largest by purchasing power parity and is the world's second largest developed economy.[1] According to the International Monetary Fund, the country's per capita GDP (PPP) was at $36,899, the 22nd-highest in 2013. Japan is a member of Group of Eight. Japanese GDP was enhanced by the production line expansion and investment at industry.

But the buying power by people is actual motivation of the factor. Therefore, the number of population is one of main indication factor of GDP. Also, age formation of ration of people is another considerable impact factor.

Now Japanese population hits a peak (127,640,000-people) and getting reducing the total number of population from DEC 2004 [2]. The main reason is less crude birth rate (CBR). Japanese CBR is 1.43 at 2013. Due to this, the age formation was changed. The ration of more than 60 years old people is 30.2% at 2010 and increasing the value year by year It will become around 40% after 2040[3]. This mean, the consumer buying power more than 65 years old people will be big factor. To enhance the consumer buying power, more fluently interoperable communication web service is required. To build rich communication, emotion sensing is one of potential technology. At Web 2.0, people can communicate with each other by Internet and can create social community. Social Networking Sites (SNS) and blogs are applications that are established by participation of these people by the manner of interoperable communication [4], [5] and [6].

But, high quality communication between people to people is achieved by physical face to face is higher than internet communication. But, if we communicate with people's emotion, it makes better understand both of people. To sense each person's emotion, we selected GSR value to evaluate for it.

It utilizes the historical value of Galvanic Skin Response (GSR) and other healthcare data measures and real time measuring data [7]. The scientific study of GSR began in the early 1900s. GSR is a method of measuring the electrical conductance of the skin, which varies with its moisture level. This is of interest because the sweat glands are controlled by the sympathetic nervous system [8] so skin conductance is used as an indication of psychological or physiological arousal. There has been a long history of electro-dermal activity research [9], most of it dealing with spontaneous fluctuations or reactions to stimuli [10]. GSR value shows the human emotion situation by the value. The average value of human cell is 500-ohm and skin level is 10 M-ohm to 50M-ohms. We estimate if value is higher than average of ordinate value of user, it shows much higher positive emotion and less stress than average situation of user. To do this decision make, the service needs historical database for each user.

Proposing service has own ontology rule engine for GSR sensing data for each user autonomously.

There are some ways to approach emotional sense. The capturing face situation analysis cannot evaluate quantitatively due to different describing emotion level on face by person. ECG is a limited condition measurement for example, the bed- side terminal in hospital for patient. But proposing service is required much easier measurement for all people to communicate. Therefore, GSR measurement approach is much more appropriate.

The judgment of emotion sense utilizes real time sensing value with historical analysis data by ontology database. Using a quantitative analysis such as Monte Carlo simulation model and standard deviation from the central limit theorem is required a large amount of data to enhance the accuracy of decision make. Ontology database utilizes as basic data the history of personal information by generating ontology attribute of each entity of GSR sense data. Adopt Ontology technique to determine the feelings of individuals with high accuracy in the inference rules, by describing the conditions in Ontology Web Language (OWL). OWL makes input events even from low self-learning information that are intended to achieve a high decision making rate of emotional judgment.

Evaluation shows how users' emotion is moving every day. We estimate user's emotion by the value of GSR every time it makes a decision. To enhance the high accuracy rate, GSR value is analyzed by ontology rule that was designed by us for this service with historical data at individual person. Ontology GSR creates individual user entities and attributes. Evaluation shows higher accuracy rate in comparison with the traditional approach. Ontology analyzer achieves more than 90% accuracy of emotion situation.

The rest of the paper is structured as follows: Section 1 is introduction. Section 2 shows the impact of Japanese economy by emotion sensing service. Section 3 discusses the related works while section 4 describes architecture of Emotion Sensor Communication to Web Service. Section 5 narrates Ontology rule engine for GSR sensor. Section 6 shows the evaluation and Section 7 concludes this paper.

Related Works

In the early 1900s, one of the first references to the use of GSR instruments in Psychoanalysis is the book by C. G. Jung entitled Studies in Word Analysis, published in 1906 [11]. Wilhelm Reich also studied GSR in his experiments at the Psychological Institute at the University of Oslo in 1935-6 to confirm the existence of a bio-electrical charge behind his concept of vegetative, pleasurable streaming' [12]. GSR was used for a variety of types of research in the 1960s through the late 1970s, with a decline in use as more sophisticated techniques (such as EEG and MRI) replaced it in many areas of psychological research.

The Galvanic Skin Response (GSR) feedback instrument measures skin conductivity from the fingers and/or palms. The GSR is highly sensitive to emotions in some people.

GSR feedback has been used in the treatment of excessive sweating (hyperhidrosis) and related dermatological conditions, and for relaxing and desensitization training. GSR was often misunderstood as a difficult technique. GSR has gone through many phases of interest and rejection since the early 1900's. It has been used in important research on anxiety and stress levels (Fenz and Epstein, 1967) and it has been a part of lie detection (Raskin, 1973). Controversy has centered on the technique, underlying mechanisms, and the meaning of the responses obtained from the skin. There has been a long history of electro-dermal activity research, most of it dealing with spontaneous fluctuations. Most investigators accept the phenomenon without understanding exactly what it means (Hume, 1976). Although GSR is the oldest and yet most confusing term, it is also the one in common use. Many attempts have been made to improve and update the terminology.

Two such systems are proposed by the Society for Psycho-Physiological Research (Brown, 1967), and Venables and Martin (1967). Electro Dermal Response (EDR) is the umbrella under which the terms fall. Basically there are two techniques in the history of electro-dermal measurement.

In one a current is passed through the skin and the resistance to passage is measured; in the other no current is used externally and the skin itself is the source of electrical activity.

GSR Physiology is easily measured and is relatively reliable. GSR has been used as an index for those who need some measurable parameter of a person's internal state".

As in EEG, there is not a clear understanding of what the measures reflect. Physiology, the GSR reflects sweat gland activity and changes in the sympathetic nervous system and measurement variables. Measured from the palm or fingertips, there are changes in the relative conductance of a small electrical current between the electrodes. The activity of the sweat glands in response to sympathetic nervous stimulation (increased sympathetic activation) results in an increase in the level of conductance. There is a relationship between sympathetic activity and emotional arousal, although one cannot identify the specific emotion being elicited. Fear, anger, startle response, orienting response and sexual feelings are all among the emotions that may produce similar GSR responses.

The Galvanic Skin Response (GSR) can be used for capturing the autonomic nerve response as a parameter of the sweat gland function. Due to relatively simplicity of measurement and a quite good repeatability, GSR can be considered to be useful and simple method for examining autonomic nervous system function, specifically the peripheral sympathetic system.

Physically GSR is a change in the electrical properties of the skin in response to different kinds of stimuli. In GSR changes in the voltage measured from the surface of the skin are recorded. The main origin of the signal has been suggested to be the activation of the sweat glands. The most commonly used stimuli are an electrical shock delivered to a peripheral nerve or auditory stimuli. However, any stimulus capable of an arousal effect can evoke the response and the amplitude of the response is more dependent on the surprise effect of the stimulus than on the physical stimulus strength.

Impact of Japanese economy

If we could to enhance the social communication ability through web service with emotionally manner by GSR sensing web service, it will be a big impact factor to improve Japanese economy. The ratio of population more than 65 years old people was occupied more than thirty percent at 2015. This ratio will increase year by year and will reach around 40% by 2040. If emotion sensing web service can improve their buying power 10% by 2020, the expectable increasing buying power will be 8%. Because 80% the financial capacity in Japan is holding by more than 65 years old people. We simulate the impact of GDP increment from 2015 to 2040 and ratio of boost up the buying power from 8% to 24% (10% to 30% influences of emotion sensing web service in these people). Figure 3 shows the result of the simulation under our assumption. The service starts from year of 2015 and achieves 8% impact at 2020, then increasing the ratio up to 24% by 2040. The increment rate of Japanese GDP is follow as actual result from 2010 to 2015.

It is approximate 1.2% per year. This simulation result shows Japanese GDP will be 733.224 Trillion JPY. Original estimate GDP is 669 Trillion JPY. Therefore, the impact value of Japanese GDP is 64.224 Trillion JPY.

4. Emotion sensing web service architecture

The architecture of Emotion Sense Communication for Web service GSR sensing data center is shown in figure 4 and the process of GSR measure for communication is shown in Figure 5. The service is web based user who was registered to data center at bigging. First of all, the user accesses the site of service and touches his hand or body with GSR sensor. GSR sensor senses user's electric resistance and sends it to website. The data from GSR sensor is stored in to GSR ontology database in the GSR data center. GSR ontology database analyzes what is the status of user's GSR sensing value at present time in today. It is also analyzed with historical data from database. Then GSR ontology rule defined the condition of user by the value of GSR eventually. Generally, if GSR value is higher than averaged value, user is in positive emotion. If the sensed value is smaller than average value, user may have some psychological stress and is in negative emotion.

The algorithm of this decision make is individual user's historical entities relationship. Therefore, decision make criteria are individual value for each user. GSR ontology is tuned by user's historical GSR value. It also adds the value of environmental factor such as weather, and user's healthcare status. Each value has weight value by the times of frequently cycle.

The sampling time of GSR and other factor from user, the GSR ontology program counts the value of each data per minute. It is greatly dependent on the sampling of the data processing time. In addition less transient effect is also cached by the data to pass. The value of sensed data was calculated by the total sum of sense data and its weight plus threshold value to judge. Basically, function and weight are main parameters. The following formula is very standard model of this decision make.

Function X is GSR value, Y is weather value and Z is other health care value. Each of them is a different aspect and weight value. Basically, weight and type of data are not static but dynamic parameters. This module senses each sampling time and calculates with historical sensed data.

Ontology rule engine for Gsr sensor

Ontology rule engine is utilizing intelligent web application attack protection and network security [13], [14] and [15]. Our ontology engine is designed referring these existing models.

Ontology rule engine for GSR sensor is shown in figure-6.

The input data are GSR sensor value, weather status and user 's healthcare background database. The ontology rule was written by Ontology Web Language (OWL) and its architecture is composed of four layers as shown in figure 5. Inference layer does consistency, classification and inference by the result by the historical data with real time measured data. Rule layer is written in JENA [16] and Semantic Web Rule Language (SWRL). This layer does parsing and reasoning for rule. The ontology layer is composed by OWL API and OWL GUI. OWL GUI edits correction of data and widget. OWL API does logic cache for restriction and making of definition. The last one is conceptualization of domain layer. This layer is composed by ProtACopyrightgACopyright API and ProtACopyrightgACopyright GUI. ProtACopyrightgACopyright GUI makes table, class and widget. ProtACopyrightgACopyright API makes class, properties and individual model. All layers are connected by main Ontology database as shows as figure 7 [17]. By these layers, the accuracy of GSR sensor result is enhanced.

The class of GSR sensing data has properties, attributes system, policy, consequence, system component, input, encoding scheme, protocol and port. These classes are further sub-classified and only important one has been discussed. Class input describes the interaction of target application with other application, database, RMI or users. This class having property causing that connects with class Means' having subclasses Input Validation Error ' and Logical Exploit'. The subclass Logical Exploit' is further sub-classified into the classes of Exception Condition', Race condition', Atomicity Error ' and Serialization Error'.

The Ontology attribute of each entity of GSR sense data is shown in Figure 8.

Thus, Ontology rule engine studies by the real time measured sensor data and add ontology database to adjust more realistic value for each user.

GSR ontology has rule generator. Figure-9 is dynamic rule generation process. Every rule contains an indicator for detecting a specific GSR value of the time. Rule based reasoning is done through Inference Engine by using semantic rules and ontology model saved in the knowledge base. Inferred knowledge model given by Inference engine is queried by the Rule Generator for the generation of detection rules. Semantic Query is generated by Query builder by using Rule template. Rules Generator pass the semantic query to inferred knowledge model and populate the rule template. Rules will be stored in the Rule Cache until unless there is some update in knowledge base. Detection Rules will be fetched by the Analyzer from Rule Cache and it uses the Rule Grammar for parsing the Detection Rules and analyzer examines the incoming user requests and outgoing responses.

Evaluation

A. GSR sense data test

This section evaluates GSR sense data from individual user. GSR sensing data is variable from user to user. Therefore, system needs to store individual historical data logs to estimate the average value for each user.

Figure 10 is GSR sense data result by multiple users. Testing equipment is GSR sensor (original made) and measurement tool by MT-4520 digital multiple testers from mother-tool Inc and Windows XP SP3 OS with testing monitor tool from tester. A to F users are already registered in this service. The service measures whenever there is log in by user. The service stores all sensing data by date and by time. Sensing data is variable and changes day by day. Therefore, the result of GSR ontology sense for human emotion is influenced by these situations. It also shows user's sex type and occupation. The human emotion is very sensitive by personal mind stability. It also shows daily basis behavior. The all of them are more than 65 years old. 66 years old men (A) who has no work is un stable condition. But two women 65years old (B) and 66 years old (E) are so stable. 67 years old (C) is also stable.

The point of this result, we notice their emotion condition and give some adjustment by from web service or other people's communication. By the communication, they may more stable.

The daily based GSR value shows different center of value. This is a one of indicator of emotion level. But the most impact factor is stability of the value each second. We need to consider this view point for evaluation. Figure 11 is GSR weekly value for each person.

B. Ontology engine accuracy level simulation

Evaluation carried out here includes GSR historical sense data with real time measure GSR sense data. The traditional real time measure of GSR sense data in both case are same accuracy as 50%. And historical GSR sense data is adjusted once a seven on ontology approach but traditional GSR model doesn't consider this value. It is always considering real sensing data only. Therefore, ontology GSR sensing accuracy result can maintain higher than traditional real time measure sensing data model. Accuracy rate is time consuming factor. In this simulation, we utilize exp (-rt) curve.

Equations

Here,

P(GSRhist) = GSR historical sense data accuracy

P(GSRreal) = real time measure sense data of GSR

Figure-12 is Ontology GSR accuracy vs. traditional GSR sensing accuracy. The accuracy rate of ontology maintains from 99.17% to 94.49% but traditional real time measure model is getting low accuracy. In this case, it drops 65.57% after 35 days. The reason of this result comes from advantage of Ontology database. Its rule utilizes historical data to adjust the average value of GSR for each user for high accuracy rate.

Conclusion

Since last two decade, Japanese GDP did not improve so much. To consider the impact of Japanese Economy, we are looking at GDP value in Japan. The big impact factor for GDP is the number of population and its age formation. We looked at the age formation and the consumer buying power by more than 65 years old people in our research. We propose emotion sensing communication for web service to these people to improve their buying power. The implementation of our proposal, we selected GSR sensing value. GSR value is indicator for human emotion stability by measing the resistance of electric in the cell of body. We measure GSR value and design its analysis methodology by Ontology analysis database which was programmed by OWL language, protege API and JINA API. We also developed the inference layer.

Ontology analysis is non quantitive model. Therefore, this implementation does not require pre teaching data. This is good advantage for this scenario of web service. The evaluation of GSR sensing with over 65 years old was shown at evaluation section. Some people are stable value but some people are not. The absolute GSR value is just one of factor but the stability of value per second is much more important to observe people's emotion real timely. We need to investigate more details of behavior of Japanese old people by our future research to capture the behaviour of this age of people.

We evaluate the impact of Japanese GDP by the scenario of predictable situation. The evaluation shows the increasing value of GDP after emotion sensing web service 64.224 Trillion higher than original estimated Japanese GDP value at year of 2040.

The potential application of our proposing emotion sensing web service, one is remote healthcare service with emotion sensing for elder people. The cure of people needs physical treatment with mental care. Emotion sensing web service is good for rural area people too. Other potential application is web radio and web TV service. Traditional web Radio and web TV are the broadcasting service model from their broadcast station. But if emotion sensing function was implemented, it will be a very high quality interoperable web channel which can indicate the listener's emotion real timely. The next step of our research, we will evaluate the actual enhancement of people's interest under the potential applications in the different market field. We are also going to investigate the type of people's behavior from GSR value at each age of people with different region and countries.

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Publication:Journal of Business Strategies (Karachi)
Date:Jun 30, 2015
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