Sozo Inoue

Quantitative evaluation of unlinkable ID matching schemes

As pervasive computing environments become popular, RFID devices, such as contactless smart cards and RFID tags, are introduced into our daily life. However, there exists a privacy problem that a third party can trace users behavior by linking devices ID.The concept of unlinkability, that a third party cannot recognize whether some outputs are from the same user, is important to solve the privacy problem. A scheme using hash function satisfies unlinkability against a third party by changing the outputs of RFID devices every time. However, the schemes are not scalable since the server needs O(N) hash calculations for every ID matching, where N is the number of RFID devices.In this paper, we propose the K-steps ID matching scheme, which can reduce the number of the hash calculations on the server to O(log N). Secondly, we propose a quantification of unlinkability using conditional entropy and mutual information. Finally, we analyze the K-steps ID matching scheme using the proposed quantification, and show the relation between the time complexity and unlinkability.

Systematic error detection for RFID reliability

In this paper, we propose an approach to increase the reliability of RFID (radio frequency identification), not relying on the performance of each devices, but relying on a mechanism of entire networked system, which manages objects and/or people in the real world with RFID tags. We identified there exist many factors to reduce the accurate identifications of RFID tags, which results in the reduction of the total-system reliability. Moreover, we show the effect of our approach in diverse parameters such as the number of misidentifications, the length of transportations, the number of RFID readers.

A Large Scale Gathering System for Activity Data with Mobile Sensors

In this paper, we show a large-scale activity gathering system with mobile sensor devices such as smart phones and accelerometers. We gathered over 35,000 activity data points from more than 200 people over approximately 13 months. We describe the design rationale of the system, and analyze the gathered data through statistics, clustering, and application of an existing activity recognition method. Our results show that there is a challenging field for activity recognition in larger-scale activity data.

Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset

In this paper, we provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, and big data combined the patient medical records and sensors attempted for 2 years, and also propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by balanced classification rate. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital.

HASC2011corpus: towards the common ground of human activity recognition

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.

Toward High-Level Activity Recognition from Accelerometers on Mobile Phones

In this paper, we propose an unsupervised method for multi-level segmentation, which could be used for a pre-process of non-sequential activity recognition, and could construct a high-level activity recognition using accelerometers on mobile phones. We extend single-level segmentation to multi-level by sweeping the temporal parameter. To confirm the validity of our approach. we pursued the experiment of gathering accelerometer data of real nursing in a hospital. After the experiment and multi-level segmentation, we confirmed several phenomena to imply the validity of multi-level segmentation such that sequence seems to be properly segmented fitting to the annotations transcribed from the voice, that there are peaks of lower-level segment boundaries without higher-level boundaries, and that higher-level boundaries are not lower-level boundaries.

Triage with RFID Tags

In this paper, a triage system using RFID and its experiment are described. Triage is a procedure used by emergency personnel to ration limited medical resources to massive injured people, in which triage tags, are used to 1: classify and transport the injured effectively, and 2: obtain and publish the state and the scale of the casualty incident. We applied RFID tags and mobile devices with a wireless network to speed up and increase the accuracy of these applications, verified through an experiment assuming massive injured people, and demonstrated that it even accelerates the transportation of the injured people

Gathering Large Scale Human Activity Information Using Mobile Sensor Devices

In this paper, we show current status of gathering large scale human activity information using mobile sensor devices. If human activity can be objectively measured, we can expect various applications, such as health care and agriculture. We developed an activity information gathering system using mobile sensor devices with three axis accelerometers, and gathered about 10,000 activity data of 170 people during 5 months so far. As a result, we found specific activities which are easily gathered and ones which are not. Moreover, we found some problems of motivation measures.

Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study

Background The prevalence of non-communicable diseases is increasing throughout the world, including developing countries. Objective The intent was to conduct a study of a preventive medical service in a developing country, combining eHealth checkups and teleconsultation as well as assess stratification rules and the short-term effects of intervention. Methods We developed an eHealth system that comprises a set of sensor devices in an attaché case, a data transmission system linked to a mobile network, and a data management application. We provided eHealth checkups for the populations of five villages and the employees of five factories/offices in Bangladesh. Individual health condition was automatically categorized into four grades based on international diagnostic standards: green (healthy), yellow (caution), orange (affected), and red (emergent). We provided teleconsultation for orange- and red-grade subjects and we provided teleprescription for these subjects as required. Results The first checkup was provided to 16,741 subjects. After one year, 2361 subjects participated in the second checkup and the systolic blood pressure of these subjects was significantly decreased from an average of 121 mmHg to an average of 116 mmHg (P<.001). Based on these results, we propose a cost-effective method using a machine learning technique (random forest method) using the medical interview, subject profiles, and checkup results as predictor to avoid costly measurements of blood sugar, to ensure sustainability of the program in developing countries. Conclusions The results of this study demonstrate the benefits of an eHealth checkup and teleconsultation program as an effective health care system in developing countries.

An RFID-based multi-service system for supporting conference events

In this paper, we introduce an experiment in an international conference using an RFID-based multi-service system. In the system, each user has a nameplate with an RFID tag, which can be used for multiple services. From the results, the system could provide immediate information for capturing the state of the conference without interrupting the operation of the conference, while the results raise essential problem of privacy management.