Yasunobu Nohara

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.

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.

Floor sensing system using laser range finder and mirror for localizing daily life commodities

This paper proposes a new method of measuring position of daily commodities placed on a floor. Picking up an object on a floor will be a typical task for a robot working in our daily life environment. However, it is difficult for a robotic vision to find a small daily life object left on a large floor. The floor surface may have various texture and shadow, while other furniture may obstruct the vision. Various objects may also exist on the floor. Moreover, the surface of the object has various optical characteristics: color, metallic reflection, transparent, black etc. Our method uses a laser range finder (LRF) together with a mirror installed on the wall very close to floor. The LRF scans the laser beam horizontally just above the floor and measure the distance to the object. Some beams are reflected by the mirror and measure the distance of the object from virtually different origin. Even if the LRF fails two measurements, the method calculates the position of the object by utilizing information that the two measurements are unavailable. Thus, the method achieves two major advantages: 1) robust against occlusion and 2) applicable to variety of daily life commodities. In the experiment, success rate of observation of our method achieves 100% for any daily commodity, while that of the existing method for a cell-phone is 69.4%.

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.

Position tracking system of everyday objects in an everyday environment

We propose an object tracking system for a service robot working in an everyday environment. The system is composed of an intelligent cabinet, a floor sensing system and a data management system. The position of an object can be classified into three areas: 1) in/on furniture, 2) on the floor, 3) held by a human or a robot. Being equipped with a RFID reader and loadcells, the intelligent cabinet measures the position of an object in/on itself. The floor sensing system which uses a laser range finder, measures the position of an object on the floor and the position of a human walking in a room. The data management system integrates the position data of the intelligent cabinets and the floor sensing system, and it performs position measurement of an object carried by a human. The data management system provides robots with position information to support robot activities.

Empowering the Healthcare Worker Using the Portable Health Clinic

We present a remote healthcare consultancy system that enables healthcare workers to identify noncommunicable diseases in unreached communities. The healthcare system combines medical sensors with mobile health and is called a Portable Health Clinic. The Portable Health Clinic fits into a briefcase and is operated by the healthcare worker. The goal of this research is to empower the healthcare worker further by allowing her to recognize spurious measurements and to make lifestyle recommendations. In this paper, we show how to process the data: combine, link and compare - captured in patient electronic health records stored in database. We applied association rule technique to find common set of rules in order to build a clinical decision support system. We also showed examples of the meaningful information from the analyzed data to build a better clinical decision support.

Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries

Non-communicable diseases (NCDs) are no longer just a problem for high-income countries, but they are also a problem that affects developing countries. Preventive medicine is definitely the key to combat NCDs; however, the cost of preventive programs is a critical issue affecting the popularization of these medicine programs in developing countries. In this study, we investigate predictive modeling for providing a low-cost preventive medicine program. In our two-year-long field study in Bangladesh, we collected the health checkup results of 15,075 subjects, the data of 6,607 prescriptions, and the follow-up examination results of 2,109 subjects. We address three prediction problems, namely subject risk prediction, drug recommendation, and future risk prediction, by using machine learning techniques; our multiple-classifier approach successfully reduced the costs of health checkups, a multi-task learning method provided accurate recommendation for specific types of drugs, and an active learning method achieved an efficient assignment of healthcare workers for the follow-up care of subjects.

On authentication between human and computer

Electronic authentication with a portable device such as a smart card has been receiving increasing attention. In an authentication, the portable device is regarded as the human user himself. However, in an open environment like authentication systems, it is necessary to have a way of secure communication between the portable device and the human user. This paper considers an authentication of a server computer of a service provider by a human with a portable device as a part of the authentication and an attack by a client computer which relays the communication between the portable device and the server computer. As a defense against the attack, we introduce a system with a portable device which has an interface to show information to a human

Experiment for nursing activity analysis using mobile sensors and proximity sensors

In this study, in order to analyze duties of the nurses, we performed experiments to collect the duties activity data of the nurses for a long term. We set 38 nurses as subjects and asked them to carry out duties while attaching a wearable small sensor device, and collected the acceleration data, meeting information between nurses and the nurse duties information. In addition, we collected the location information of the nurses by using infrared information and communication equipment at the same time. In fact, we were able to extract a total of 1,805,760 pieces of activity rhythm of nurses, 462,418 pieces of location information data, 459,139 pieces of meeting information data, and 12,406 pieces of nurse duties information. In the future, we will visualize the nurses activities, and analyse the factors that affect the activity rhythm, the nurse duties, and the location information.

IEEE802.15.6 NB portable BAN clinic and M2M international standardization

The increase of non communicable diseases (NCDs) will change the direction of health services to emphasize the role of preventive medicine in healthcare services. The first short-range medical body area network (BAN) standard IEEE802.15.6 is expected to be used for secure and user-friendly sensor devices for portable medical equipment. A BAN is an enabler for uploading medical data to a backend system for remote diagnoses and treatment. Machine-to-Machine (M2M) infrastructure is also a key technology for providing flexible and affordable services extending electronic health record (EHR) systems. This paper proposes a BAN-based portable clinic that collects health-check data from user-friendly medical devices and sensors and sends the data to a local backend server, and it evaluates the clinic in fields of actual usage. We discuss issues experienced from actual deployment of the system and focus on integrating it into upcoming healthcare M2M infrastructure to achieve affordable and dependable clinic services. We explain the components and workflow of the clinic and the system model. The system is set up at a temporary health center and has a network link to a remote medical help center. The paper concludes with our plan to introduce our system to contribute to internationally standardized preventive medicine.