Koji Ara

A 15 -- 15 mm, 1 μA, reliable sensor-net module: enabling application-specific nodes

An ultra-small, low-power sensor-net module called ZN1 is developed. ZN1 integrates MCU, ZigBee RF into an ultra-small 15times15-mm module with an ultra-low standby current less than 1 muA. The and highly reliable operation for more than 15 years of the ZN1 enable low-cost practical application-specific sensor nodes for HVAC, structure monitoring, office management, security, medicine, and healthcare. We describe architecture and technologies for developing the ZN1 module. Moreover, we demonstrate two applications that we developed simultaneously: food sanitation and home healthcare. We also show results of the ZN1 module evaluation and discuss reliability of the ZN1

Organizational Engineering Using Sociometric Badges

We show how a wearable computing research platform for measuring and analyzing human behavior can be used to understand social systems. Using a wearable sociometric badge capable of automatically measuring the amount of face-to-face interaction, physical proximity to other people, and relative location, we are able to construct a dynamic view of an organizations social network by viewing interactions as links between actors. Combining this with email data, where e-mail exchanges indicate a social tie, we are able to form a robust view of the social network, using proximity information to remove spurious e-mail exchanges. We attempt to use on-body sensors in large groups of people for extended periods of time in naturalistic settings for the purpose of identifying, measuring, and quantifying social interactions, information flow, and organizational dynamics. We discuss how this system can lead to an automatic intervention system that could optimize the social network in real time by facilitating the addition and removal of links based on objective metrics in a socially natural way. We deployed this research platform in a group of 22 employees working in a real organization over a period of one month, and we found that betweenness in the combined social network had a high negative correlation of r = −0.49 (p

Sensible Organizations: Changing Our Businesses and Work Styles through Sensor Data

We introduce the concept of sensor-based applications for the daily business settings of organizations and their individual workers. Wearable sensor devices were developed and deployed in a real organization, a bank, for a month in order to study the effectiveness and potential of using sensors at the organizational level. It was found that patterns of physical interaction changed dynamically while e-mail is more stable from day to day. Different patterns of behavior between people in different rooms and teams (p < 0.01), as well as correlations between communication and a workers subjective productivity, were also identified. By analyzing a fluctuation of network parameters, i.e., “betweenness centrality, ” it was also found that communication patterns of people are different: some people tend to communicate with the same people in regular frequency (which is hypothesized as a typical pattern of throughput-oriented jobs) while some others drastically changed their communication day by day (which is hypothesized as a pattern of creative jobs). Based on these hypotheses, a reorganization, such that people having similar characteristics work together, was proposed and implemented.

A Proposal for Transaction-Level Verification with Component Wrapper Language

We propose a new approach to accelerate transaction level verification by raising the productivity of the verification suites including test patterns, protocol checker, and simulation-coverage analyzer This approach combines the conventional transaction level language such as C and the signal level language based on our previously developed Component Wrapper Language (CWL). This approach is based on two concepts. The first one is a complete separation between transaction-level verification and signal-level verification for generating suitable verification suites in each design phase. The second one is the quick generation of signal-level verification suites from the original specification written in CWL. Experimental results show that our approach should yield much shorter verification periods versus conventional methods.

Studying Microscopic Peer-to-Peer Communication Patterns

This paper describes first results of an ongoing research effort using real time data collected by social badges to correlate temporal changes in social interaction patterns with performance of individual actors and groups. Towards that goal we analyzed social interaction among a team of employees at a bank in Germany, and developed a set of interventions for more efficient collaboration. In particular, we were able to identify typical meeting patterns, and to distinguish between creative and high-executing knowledge work based on the interaction pattern.

Predicting flow state in daily work through continuous sensing of motion rhythm

We have constructed a new application of continuous sensing of human physiological data during daily a business setting. By capturing the subtle changes and differences in motion rhythm detected through an accelerator rather than trying to identify the context of human activities, we are envisioning the prediction of a persons psychological flow state, i.e., the engagement in ones task. A badge-shaped wearable sensor device called “Business Microscope” was developed and deployed in a real organization, an office supply firm, for one month to study how effectively flow states could be measured during daily work. We found that even though each subject behaved at different motion rhythms when they were in flow, the consistency of motion rhythm around 2 to 3 Hz was correlated with the richness of flow during work (r=0.47, p<0.01).

Beam-scan sensor node: Reliable sensing of human interactions in organization

We have developed a wearable sensor node with a low power and high detection rate by using sequential control of multiple infrared (IR) modules. Conventional sensor nodes are not practical in terms of size, sensing performance, and working hours. Therefore, we devised a name-tag-size (73 × 98 × 9 mm) sensor node, which captures face-to-face interactions within 2 meters and within an angle of 60°. The sensor node weighs 62 grams and works for more than twenty hours with a small 5-gram Li-ion battery. The sensor uses the beam-scan technique, in which four infrared modules, placed horizontally on the node, are controlled to be on and off sequentially, and this operation is done synchronously with other nodes. The beam-scan technique enables low-power operation with a consumption current of 7.2 mA and 21 hours of operation. We had tested the sensor node in a field trial that collected sensor data for six months from 20 people and had demonstrated that this technique is practical. Feedback from sensing data reminded us of the importance of meeting frequency and this improved our work habits.

Relationship between social interaction and mental health

In order to explore the relationship between human social interaction and mental health, we investigated the correlations between the amount of face-to-face contact time and quantified mental health. Social interaction data were obtained by wearable sensing system for two organizations in Japan. In this study, we regarded face-to-face contact between individuals as social interaction. The mental health of individuals was measured by psychological questionnaire. We found that the social behavioral property of individuals tended to reflect quantified stress in both organizations. The results suggest the possibility that people who experience large amounts of social interaction tend to have less stress.

Profiting from IoT: the key is very-large-scale happiness integration

Big data without link to value is merely a cost. We have studied how to profit from data with Internet-of-Things technologies for over 10 years to reach the answer: the Wearable Happiness Meter. It allows us to integrate the measure of both wellbeing and productivity of 7-billion people worldwide, which was the dream of the 18th-century philosopher Jeremy Bentham, numeration of the greatest happiness of the greatest number to measure the right and wrong. Knowing right and wrong with the 10x speed over conventional financial feedback accelerates the growth of the enterprise, the economy, and the individual to maximize the worldwide happiness. Here the integration is not only on a chip, but in the distributed massive chips embedded in the society.

A 15/spl times/15 mm, 1 /spl mu/A, reliable sensor-net module: enabling application-specific nodes

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