Hirohiko Suwa

An Emergency Medical Communications System by Low Altitude Platform at the Early Stages of a Natural Disaster in Indonesia

A natural disaster is a consequence of a natural hazard, such as a tsunami, earthquake or volcanic eruption, affecting humans. In order to support emergency medical communication services in natural disaster areas where the telecommunications facility has been seriously damaged, an ad hoc communication network backbone should be build to support emergency medical services. Combinations of requirements need to be considered before deciding on the best option. In the present study we have proposed a Low Altitude Platform consisting of tethered balloons combined with Wireless Fidelity (WiFi) 802.11 technology. To confirm that the suggested network would satisfy the emergency medical service requirements, a communications experiment, including performance service measurement, was carried out.

Characteristic tetanus infection in disaster-affected areas: case study of the Yogyakarta earthquakes in Indonesia.

Tetanus is an infectious disease caused by the contamination of wounds from bacteria that live in soil. The tetanus mortality rate remains high in developing countries affected by natural disasters. Whether the socio-demography and geographical conditions may influence the tetanus treatment outcome on the earthquake situation in Yogyakarta, Indonesia has not been investigated. We present 26 tetanus patients who were admitted to eight hospitals following the earthquakes that occurred on May, 27, 2006, in Yogyakarta, Indonesia. The independent variables were age, gender, distance, admission, hospitalization, and type of hospital with the dependent variable surviving or perishing. Data were analyzed by logistic regression methods on SPSS 17.0. The distance from the patients place of residence to the hospital were obtained and analyzed by using geospatial tools MapInfo 7.8 SCP and Global Mapper 7. Eight of the 26 patients were dead (30.8%) and statistical results showed that the distance (OR = 1.740, 95% CI = 1.068–2.835) and type of hospital (OR = 0.067, 95% CI = 0.001–3.520) were significant predictors of death. Our findings show that in order to reduce the mortality rates, performing triage systems based on the distance and type of hospital priority for internally displaced persons could be proposed as well as making provisions for the generally old population in order to prevent an outbreak of tetanus following earthquakes in Yogyakarta, Indonesia.BackgroundTetanus is an infectious disease caused by the contamination of wounds from bacteria that live in soil. The tetanus mortality rate remains high in developing countries affected by natural disasters. Whether the socio-demography and geographical conditions may influence the tetanus treatment outcome on the earthquake situation in Yogyakarta, Indonesia has not been investigated.FindingsWe present 26 tetanus patients who were admitted to eight hospitals following the earthquakes that occurred on May, 27, 2006, in Yogyakarta, Indonesia. The independent variables were age, gender, distance, admission, hospitalization, and type of hospital with the dependent variable surviving or perishing. Data were analyzed by logistic regression methods on SPSS 17.0. The distance from the patients place of residence to the hospital were obtained and analyzed by using geospatial tools MapInfo 7.8 SCP and Global Mapper 7. Eight of the 26 patients were dead (30.8%) and statistical results showed that the distance (OR = 1.740, 95% CI = 1.068–2.835) and type of hospital (OR = 0.067, 95% CI = 0.001–3.520) were significant predictors of death.ConclusionOur findings show that in order to reduce the mortality rates, performing triage systems based on the distance and type of hospital priority for internally displaced persons could be proposed as well as making provisions for the generally old population in order to prevent an outbreak of tetanus following earthquakes in Yogyakarta, Indonesia.

Designing an Emergency Medical Information System for the Early Stages of Disasters in Developing Countries: The Human Interface Advantage, Simplicity and Efficiency

The means of designing an effective user interface software package varies from one application to another. Almost the entire ICT infrastructure was damaged following the impact of the tsunami tidal wave. Under such circumstances, transporting critically ill patients is a must and becomes the first priority. Many considerations are needed when designing a specific user interface for emergency situations in developing countries. In this study we proposed how to design the user interface in order to support emergency medical care in the early stages of disasters. The user interface was classified into two tabs, firstly to indicate critically ill patients and secondly to notify details of the medicine having been administered to the patients. Classifying the user interface of emergency medical care information systems by using VHF radio connections will be beneficial, especially for the early stages of disaster-stricken developing countries, in order to preserve the lives of more victims.

Disaster area mapping using spatially-distributed computing nodes across a DTN

Disaster area mapping is critical to guiding evacuees to safety and aiding responders in decision-making. During disasters however, Cloud-based mapping services cannot be relied upon, because network infrastructures may have been damaged. In this study, we propose a disaster area mapping system that functions under challenged-network environments in a disaster area. The system infers a pedestrian map with walking speed information from data gathered by civilians and responders with mobile devices. To generate the map, the system addresses the following challenges: how to collect disaster area data, how to share data without continuous end-to-end networks, and how to generate maps without Cloud-based mapping services. First, the system leverages human mobility to collect disaster area data. Civilians and responders with mobile devices function as sensor nodes and log their GPS and velocity traces while moving based on the Post-Disaster Mobility Model. Second, the system uses mobile devices to establish a Delay-Tolerant Network, through which nodes opportunistically share data. Finally to generate the map, the collected data are routed to Computing Nodes: devices with more computational resources than mobile devices that are spatially-distributed across the disaster area. The Computing Nodes infer the map from the data and share it with evacuees. Through experimental evaluations and computer simulations, we found that the system significantly decreases the time required to generate and deliver a map to an evacuee, compared to a case without the system. Furthermore, the overall reduction in time increases as the size of the data required to generate the map and the number of DTN nodes increase.

Exploring Accuracy-Cost Tradeoff in In-Home Living Activity Recognition Based on Power Consumptions and User Positions

Advanced context-aware services at home such as elderly monitoring requires highly accurate living activity recognition in a home environment. Existing studies on living activity recognition suffer from high deployment and maintenance costs, privacy intrusion due to utilization of cameras and microphones, and few recognizable activities or low recognition accuracy. In this paper, to solve these problems, we propose a new living activity recognition method. Our method utilizes only power meters attached to appliances and a positioning sensor attached to a resident of a home to mitigate privacy intrusion. We target 10 different living activities which cover most of our daily lives at home and construct activity recognition models based on machine-leaning. To accurately recognize the activities from the sensor data by power meters and position sensor, we explore the best combination of time window width for samples of training/test data, features, and machine-learning algorithms. Furthermore, we thoroughly investigate the tradeoff between the sensor data granularity and the consequent recognition accuracy. Through experiments using sensor data collected by four participants in our smart home, the proposed method achieved 97.8% average F-measure of recognizing 10 target activities with the finest sensor data granularity (position estimation error ≤ 0.1m, 16 power meters) and 86.9 % F-measure with room-level position accuracy and one power meter for each of four rooms.

Characteristics and risk factors for typhoid fever after the tsunami, earthquake and under normal conditions in Indonesia

BackgroundAlthough typhoid transmitted by food and water is a common problem in daily life, its characteristics and risk factors may differ in disaster-affected areas, which reinforces the need for rapid public health intervention. Surveys were carried out post-tsunami in Banda Aceh, post-earthquake in Yogyakarta, and under normal conditions in Bandung, Indonesia. Logistic regression analysis was used to assess the risk factors with the dependent variable of typhoid fever, with or without complications.FindingsCharacteristic typhoid fever with complications was found in 5 patients (11.9%) affected by the tsunami in Aceh, 8 (20.5%) after the earthquake in Yogyakarta, and 13 (18.6%) in Bandung. After the tsunami in Aceh, clean water (OR = 0.05; 95%CI: 0.01-0.47) and drug availability (OR = 0.23; 95%CI: 0.02-2.43) are significant independent risk factors, while for the earthquake in Yogyakarta, contact with other typhoid patients (OR = 20.30; 95%CI: 1.93-213.02) and education (OR = 0.08; 95%CI: 0.01-0.98) were significant risk factors. Under normal conditions in Bandung, hand washing (OR = 0.07; 95%CI: 0.01-0.50) and education (OR = 0.08; 95%CI: 0.01-0.64) emerged as significant risk factors.ConclusionThe change in risk factors for typhoid complication after the tsunami in Aceh and the earthquake in Yogyakarta emphasizes the need for rapid public health intervention in natural disasters in Indonesia.

Beacon-based multi-person activity monitoring system for day care center

Recently, as elderly people population grows, the burden on caretakers are getting larger. In day care center, caretakers are taking care records aiming to improve care receivers Quality of Life (QoL). However, in the present situation, it is difficult for caretakers to record care receivers activity in detail because each care worker needs to take care of several care receivers at the same time and it is a large burden. To reduce the burden of caretakers, many elderly monitoring systems have been proposed so far, but most of them are not effective in the sense that they force care receivers to use dedicated device such as smart phone and/or particular applications that are obtrusive and cumbersome for care receivers. In this paper, we propose a novel elderly monitoring system which can monitor movements/activity of multiple care receivers at the same time by estimating existence area of each of the care receivers, without burdening them. Our proposed system estimates multiple care receivers existence area only using RSSI (Received Signal Strength Indication) of BLE (Bluetooth Low Energy). The feature of our proposed system is that it takes Movable-Beacon and Fixed Scanner style. We have validated the proposed system and confirmed that we can estimate multi-persons existence area at high accuracy using only BLE devices.

Food Weight Estimation using Smartphone and Cutlery

In this era of Internet of Things (IoT), the healthcare system is one of the fields that has received a lot of attention from researchers. Daily-life things and objects such as mobile phones, watches, or shoes are coupled with sensors to make health systems for monitoring, and managing people heath. Recently, some methods have been focused on using food photography and associated image-processing techniques to assess food nutrients to control calorie intake. However, one of the critical issues in such image-based dietary assessment tools is the accuracy and consistent estimation of the sizes and weights of the food portion in the image. In this paper, we propose a system that uses eating tools (cutlery) such as spoon, fork or chopsticks to measure the weight of a food in a picture, in order to estimate the calorie content of that food, for diet assessment and obesity prevention. Our system requires the user to take only a single image from the top with the cutlery in the picture. Using several image processing techniques and the EXIF metadata of the image, the system automatically estimates the diameter and the height of the food container and derives the food volume. Then, given the food type, the system combines the information about the container diameter, height and the food type to provide the weight of the food in the image. Our experiments show tenable results from the system which achieved an average relative error rate of 6.87% for the weight estimation, over the testing food images.

Design and Implementation of Middleware for IoT Devices toward Real-Time Flow Processing

Thanks to rapid advance and penetration of IoT devices, it is becoming possible to sense almost every information of real-world. This urges us to utilize data streams continuously generated from IoT devices in real-time. In this paper, aiming to locally process data streams by using computational resources of IoT devices, we propose middleware for IoT devices where the devices process data streams in real-time and in a distributed manner. The proposed middleware provides four functions: (1) distribution of tasks issued by application software into sub-tasks and distributed execution of the sub-tasks over multiple IoT devices, (2) distribution of data streams over IoT devices, (3) real-time analysis of the data streams, and (4) seamless integration of sensors and actuators. We have implemented a prototype of the proposed middleware for Raspberry Pi and show its basic performance.

Low-cost and Device-free Activity Recognition System with Energy Harvesting PIR and Door Sensors

Progress of IoT and ubiquitous computing technologies has strong anticipation to realize smart services in households such as efficient energy-saving appliance control and elderly monitoring. In order to put those applications into practice, high-accuracy and low-cost in-home living activity recognition is essential. Many researches have tackled living activity recognition so far, but the following problems remain: (i)privacy exposure due to utilization of cameras and microphones; (ii) high deployment and maintenance costs due to many sensors used; (iii) burden to force the user to carry the device and (iv) wire installation to supply power and communication between sensor node and server; (v) few recognizable activities; (vi) low recognition accuracy. In this paper, we propose an in-home living activity recognition method to solve all the problems. To solve the problems (i)--(iv), our method utilizes only energy harvesting PIR and door sensors with a home server for data collection and processing. The energy harvesting sensor has a solar cell to drive the sensor and wireless communication modules. To solve the problems (v) and (vi), we have tackled the following challenges: (a) determining appropriate features for training samples; and (b) determining the best machine learning algorithm to achieve high recognition accuracy; (c) complementing the dead zone of PIR sensor semipermanently. We have conducted experiments with the sensor by five subjects living in a home for 2-3 days each. As a result, the proposed method has achieved F-measure: 62.8% on average.