Details of a Researcher - Nakazawa, Jin

Papers - Nakazawa, Jin

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Protocol for an interventional study to reduce postpartum weight retention in obese mothers using the internet of things and a mobile application: a randomized controlled trial (SpringMom)

Kawasaki M., Mito A., Waguri M., Sato Y., Abe E., Shimada M., Fukuda S., Sasaki Y., Fujikawa K., Sugiyama T., Ota E., Nakazawa J., Okoshi T., Takimoto H., Honda Y., Inoue E., Hiramatsu Y., Arata N.

BMC Pregnancy and Childbirth (BMC Pregnancy and Childbirth) 21 ( 1 ) 2021.12

Background: Obese pregnant women are known to experience poorer pregnancy outcomes and are at higher risk of postnatal arteriosclerosis. Hence, weight control during and after pregnancy is important for reducing these risks. The objective of our planned randomized controlled trial is to evaluate whether the rate of change in body weight in obese women before pregnancy to 12 months postpartum would be lower with the use of an intervention consisting of Internet of Things (IoT) devices and mobile applications during pregnancy to 1 year postpartum compared to a non-intervention group. Methods: Women will be recruited during outpatient maternity checkups at four perinatal care institutions in Japan. We will recruit women at less than 30 weeks of gestation with a pre-pregnancy body mass index ≥ 25 kg/m2. The women will be randomly assigned to an intervention or non-intervention group. The intervention will involve using data (weight, body composition, activity, sleep) measured with IoT devices (weight and body composition monitor, activity, and sleep tracker), meal records, and photographs acquired using a mobile application to automatically generate advice, alongside the use of a mobile application to provide articles and videos related to obesity and pregnancy. The primary outcome will be the ratio of change in body weight (%) from pre-pregnancy to 12 months postpartum compared to before pregnancy. Discussion: This study will examine whether behavioral changes occurring during pregnancy, a period that provides a good opportunity to reexamine one's habits, lead to lifestyle improvements during the busy postpartum period. We aim to determine whether a lifestyle intervention that is initiated during pregnancy can suppress weight gain during pregnancy and encourage weight loss after delivery. Trial registration: UMIN: UMIN (University hospital Medical Information Network) 000,041,460. Resisted on 18th August 2020. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047278.

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Investigating the occurrence of selfie-based emotional contagion over social network

W Sasaki, Y Nishiyama, T Okoshi, J Nakazawa

Social Network Analysis and Mining 11 (1), 1-14 (Social Network Analysis and Mining) 11 ( 1 ) 8 - 8 2021.12

Research paper (scientific journal), Joint Work, ISSN 18695450

Happiness is obviously one of the most fundamental essence that affects many aspects of our lives. Past research found that happiness of one person affects that of other people. What occurs under this propagation of emotion is called “emotional contagion,” a phenomenon wherein through perception, people experience the same emotion expressed by someone when communicating with them. Although online communication is increasing due to growth of mobile computing, emotional contagion on online communication is not well studied yet. Particularly, it is not yet clear if emotional contagion among people occurs through selfie photographs posted on the social network media. We implemented “SmileWave,” the social networking system for investigating selfie-based emotional contagion. The key feature of SmileWave is detecting “smile degree” in user’s posting selfies and in reactive facial expressions when the user is viewing the posted photographs from others. Our in-the-wild user studies with 38 participants for 2 weeks revealed the occurrence of selfie-based emotional contagion over the social network, based on the results that the users’ smile degree improved (15% on average) when the user looked at posted selfie photographs.

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DEMO: Extracting Multiple Promotional Media Elements from Urban Spaces

Motoki Y., Nakayama M., Kondo S., Ishikawa E., Jinno S., Nakazawa J.

Proceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021 (Proceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021) 407 - 409 2021.08

ISSN 9781665412520

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Physical Activity Analysis of College Students During the COVID-19 Pandemic Using Smartphones

Y Nishiyama, Y Kakino, E Naka, Y Noda, S Hashiba, Y Yamada, W Sasaki, ...

arXiv preprint arXiv:2103.06515 abs/2103.06515 2021

Protocol for an interventional study to reduce postpartum weight retention in obese mothers using the internet of things and a mobile application: a randomized controlled trial …

M Kawasaki, A Mito, M Waguri, Y Sato, E Abe, M Shimada, S Fukuda, ...

BMC pregnancy and childbirth 21 (1), 1-14 2021

WellComp 2021: Fourth International Workshop on Computing for Well-Being

T Okoshi, J Nakazawa, JG Ko, F Kawsar, S Pirttikangas

Adjunct Proceedings of the 2021 ACM International Joint Conference on … (UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers) 108 - 111 2021

ISSN 9781450384612

With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metrics commonly applied in computing, such as "efficiency"and "productivity", the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly emphasized in the recent years. Through the second "WellComp"(Computing for Well-being) workshop, we will intensively discuss about the contribution of ubiquitous computing towards users' well-being that covers both physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2021 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to well-being and ubiquitous computing.

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Making Cities Smarter with Regional IoT and the Force of Information

Y Chen, J Nakazawa

Journal of The Institute of Electrical Engineers of Japan 141 (1), 11-14 2021

Extracting Multiple Promotional Media Elements from Urban Spaces

Y Motoki, M Nakayama, S Kondo, E Ishikawa, S Jinno, J Nakazawa

2021 IEEE International Conference on Smart Computing (SMARTCOMP), 407-409 2021

Combining Blockchains, Smart Contracts, and Complex Sensors Management Platform for Hyper-Connected SmartCities: An IoT Data Marketplace Use Case

G Palaiokrassas, P Skoufis, O Voutyras, T Kawasaki, M Gallissot, ...

Computers 10 (10), 133 (Computers) 10 ( 10 ) 2021

In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.

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A Comparative Study of CIPN Symptom Estimation Methods Based on Machine Learning

S Hamanaka, W Sasaki, T Okoshi, J Nakazawa, K Yagasaki, H Komatsu

ISSN 9781450384612

Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of anticancer medicines that causes muscular weakness and falls in cancer patients. Therefore, we compared the methods for identifying and estimating the factors that cause symptoms in CIPN based on factor analysis and four different machine learning algorithms. The results of factor analysis showed that the latent factors causing CIPN can be classified into three categories: "Morning body condition", "Pain in the legs"and "Dizziness". We chose models for machine learning based on the premise of multi-task learning and built numerous machine learning models to estimate various CIPN symptoms. According to the results of the comparison studies, TabNet has the best generalization performance.

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A Methodology for Generating Systems Architectural Glimpse Statements Using the 5W1H Maxim

O Voutyras, AH Bokhari, A Tsuge, G Palaiokrassas, T Kawasaki, ...

Computers 10 (10), 131 (Computers) 10 ( 10 ) 2021

Attempts to facilitate and streamline systems architecting have resulted in a great number of reusable principles, practices, mechanisms, frameworks, and tools. Such a practice is the use of architectural viewpoints and views. However, as systems change, these practices should also evolve. The increasing scale and complexity of systems resulting from an ever-growing pool of human needs and breakthroughs may lead, in some cases, to an increased gap between the abstraction activities attempting to capture the whole of a system, and the instantiation activities that produce concrete and detailed descriptions of a system’s architecture. To address this issue, this article introduces a new notion, that of architectural glimpse statements, fundamental questions acting as the building blocks for architectural views and products. This notion can help architects ask the right questions in the right manner to create fundamental statements, the elaboration on which can lead directly to concrete architectural products. Working on top of standardized and common approaches, the article introduces a language for the creation of architectural glimpse statements using the 5W1H maxim. Based on this language, a tool and guidelines are also provided to facilitate the usage of glimpses. Finally, the overall methodology is demonstrated in two case studies.

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Search Wandering Score: Predicting Timings of Online Shopping based on Wandering in User’s Web Search Queries

K Tsubouchi, W Sasaki, T Okoshi, J Nakazawa

2020 IEEE International Conference on Big Data (Big Data), 1681-1688 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020) 1681 - 1688 2020.12

Joint Work, ISSN 9781728162515

Many researchers and companies have engaged in estimating users' interests so that an online shopping system can tell what he/she wants now. This paper tackles the next challenge in online shopping, i.e., predicting the times that users go shopping online. To predict the timing of online shopping, we focus on wandering behavior in web search activities and propose a search wandering score (SWS). Online shopping behavior can be categorized into three states: wandering shop-ping, focused shopping, and others. Wandering shopping is a state in which users make purchases in high SWS situations; focused shopping is a state in which users buy things in low SWS situations. Unlike previous studies, our work is based on an analysis of large-scale data containing shopping and search logs produced by approximately 200,000 users of a real web portal site for over a year. The results of an extensive evaluation show that our methodology can predict user's future shopping behavior types with 86% accuracy. This research is the first step towards understanding the relationship between users' mental states and their online shopping behavior.

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A mobility-aware pub/sub architecture for short-lived data in smart cities: Poster abstract

Kawasaki T, Okoshi T, Nakazawa J

SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems (SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems) 645 - 646 2020.11

Research paper (international conference proceedings), Joint Work, ISSN 9781450375900

With an increase in the number of IoT devices, the amount of data transferred between the devices and applications is becoming huge. To ensure that these data are properly used, a new IoT data transfer system is needed to better control the timing and the content of the data to transmit. One of the promising means for such a large-scale city-data transfer is the publish/subscribe messaging model, which can separate data senders and receivers so that they can run independently. However, existing pub/sub systems cannot cope well with the mobility of senders and receivers, thereby limiting its applicability to real-world uses. In concrete, they don't consider Time-to-live (TTL) of data. Users can use the data anytime and within TTL of it. IoT platforms can improve controlling data transmission by used to this characteristic. In this paper, we focus on the Time-to-Live of data (data-TTL). Our system can control data transmission by using data-TTL and combine with the user's movement information. We have constructed a system, that is capable of control data transmission for mobility aware.

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Behavification: bypassing human's attentional and cognitive systems for automated behavior change

T Okoshi, W Sasaki, J Nakazawa

Adjunct Proceedings of the 2020 ACM International Joint Conference on … (UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers) 692 - 695 2020.09

Research paper (international conference proceedings), Joint Work, ISSN 9781450380768

To achieve behavior change in our lives for several specific goals towards better well-being, various types intervention systems have been proposed. However, because of humans' bounded rationality, such explicit information provision for the users do not always reach their attention, thus fails the needed information for the behavior change. This paper propose new concepts "behavification"with which information system bypasses the user's cognitive, attentional and perceptive system by not notifying him/her and modifies the system behavior to directly influence the user's behavior under user's explicit advance permission. Through a series of group discussion and survey sessions with 19 participants, we investigated how the potential behavification users see its appropriateness to different application categories, killer application scenarios, and design guidelines and rationales for a behavification system to support such scenarios. We found total 58 application scenarios and propose 3 guidelines "advance user permission", "multi-modality"and "integration with real-world action/actuation".

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WellComp 2020: third international workshop on computing for well-being

T Okoshi, J Nakazawa, JG Ko, F Kawsar, S Pirttikangas

Research paper (international conference proceedings), Joint Work, ISSN 9781450380768

With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as "efficiency"and "productivity", the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our third "WellComp"(Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users' well-being that covers physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. After big success of two previous workshops WellComp 2018 and 2019, with strong international organization members in various ubicomp research domains, WellComp 2020 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to well-being and ubiquitous computing.

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Using iOS for inconspicuous data collection: A real-world assessment

Y Nishiyama, D Ferreira, W Sasaki, T Okoshi, J Nakazawa, AK Dey, ...

Research paper (international conference proceedings), Joint Work, ISSN 9781450380768

Mobile Crowd Sensing (MCS) is a method for collecting multiple sensor data from distributed mobile devices for understanding social and behavioral phenomena. The method requires collecting the sensor data 24/7, ideally inconspicuously to minimize bias. Although several MCS tools for collecting the sensor data from an off-the-shelf smartphone are proposed and evaluated under controlled conditions as a benchmark, the performance in a practical sensing study condition is scarce, especially on iOS. In this paper, we assess the data collection quality of AWARE iOS, installed on off-the-shelf iOS smartphones with 9 participants for a week. Our analysis shows that more than 97% of sensor data, provided by hardware sensors (i.e., accelerometer, location, and pedometer sensor), is successfully collected in real-world conditions, unless a user explicitly quits our data collection application.

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Estimating symptoms caused by CIPN using mobile and wearable devices

W Sasaki, R Ozawa, T Okoshi, J Nakazawa, K Yagasaki, H Komatsu

Research paper (international conference proceedings), Joint Work, ISSN 9781450380768

Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of anticancer drugs that causes muscle weakness in the cancer patients, causing them to fall. Therefore, we constructed "FD-AWARE", a system to understand the users' fall context and users' CIPN symptoms as the first step in preventing these falls. This system can collect the various sensor data from the iPhone and the Apple Watch, self-reported fall information data, self-reported user status data of CIPN symptoms, and their physical condition. We conducted a 2-week in-the-wild experiment with 8 patients who were actually suffering from CIPN. We constructed the machine learning models for estimating the users' status of CIPN symptoms and successfully achieved high accuracy of performance for several estimating models.

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SmileCityReport: Emotion-aware Participatory Sensing for Smart Cities with Double-sided Photo Shooting

W Sasaki, Y Eigen, A Medela, A Litke, VC Núnez, T Okoshi, J Nakazawa

2020 IEEE International Conference on Pervasive Computing and Communications … (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020) 1 - 6 2020.03

Research paper (international conference proceedings), Joint Work, ISSN 9781728147161

Collection of information of the events taking place in local neighborhoods along with the emotional statuses of the people involved can enable us to realize an 'affective smart city map', with which, for example, the local authority can review the measures adopted for the local areas and whether their these measure have actually contributed to the quality of life (QoL) and well-being of the people. To realize such an information system having easy-deployability, real-time and secure protection of user's sensitive data, we propose SmileCityReport, a smartphone app-based participatory sensing that can easily capture both the city events and the reporter's emotion-related status based on a novel technique that uses two cameras simultaneously. For our evaluation, we evaluated 15 users over one week and confirmed that the proposed methodology contributes to more activity and (estimated) more positive emotional status of the users, and also that the emotion-related facial expression values constitute valuable data that can be publicly shared.

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SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems, Virtual Event, Japan, November 16-19, 2020

Jin Nakazawa, Polly Huang

SenSys 2020

ISSN 978-1-4503-7590-0

Multi-layered Security Technologies to Ensure Hyper Connected Smart Cities with Blockchain, BigData, Cloud and IoT

J Nakazawa, AM TST

IEICE Technical Report; IEICE Tech. Rep. 2020

A mobility-aware pub/sub architecture for short-lived data in smart cities

T Kawasaki, T Okoshi, J Nakazawa

Proceedings of the 18th Conference on Embedded Networked Sensor Systems, 645-646 2020

iOS crowd–sensing won’t hurt a bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform

Y Nishiyama, D Ferreira, Y Eigen, W Sasaki, T Okoshi, J Nakazawa, ...

International Conference on Human-Computer Interaction, 223-243 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) 12203 LNCS 223 - 243 2020

Research paper (international conference proceedings), Joint Work, ISSN 9783030503437

The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.

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GANonymizer: Image Anonymization Method Integrating Object Detection and Generative Adversarial Network

T Tanimura, M Kawano, T Yonezawa, J Nakazawa

EAI International Conference on IoT in Urban Space, 109-121 (EAI/Springer Innovations in Communication and Computing) 109 - 121 2020

Joint Work, ISSN 25228595

Sharing and analyzing image data from ubiquitous urban cameras must enable us to understand and predict various contexts of the city. Meanwhile, since such image data always contains privacy data such as people and cars, we cannot easily share and analyze the data through the Internet for the viewpoint of privacy protection. As a result, most of urban image data are only kept/shared within the camera owners, or even discarded to reduce risks of privacy data leakage. To solve the privacy problem and accelerate sharing of urban image data, we propose GANonymizer that automatically detects and removes objects related to privacy from the urban images. GANonymizer combines two neural networks: (1) a network which detects objects related to privacy such as persons and cars in an input image using object detection network and (2) a network that removes the detected objects naturally as though they are not exist originally. Through our experiment of applying GANonymizer to urban video images, we confirmed that GANonymizer partially achieved natural removal of objects related to privacy.

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Civic Crowdsensing Through Location-Aware Virtual Monsters

J Nakazawa

Distributed, Ambient and Pervasive Interactions: 8th International … (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) 12203 LNCS 464 - 476 2020

Joint Work, ISSN 9783030503437

We present a new model for encouraging people to get involved with monitoring and taking part in the life of cities. Cities could be smarter if IoT and people could serve as engaged and pro-active data resources (i.e., crowd sensing). This study tackles two challenges: methods by which the privacy of people who act as sensors/actuators can be guaranteed and methods to create a unified programming model for crowd sensors alongside other IoT functions. To achieve these goals, we introduce a new concept called Lokemon (Location Monster). Each sensing space is characterized as a personified target. Lokemon asks users to imagine themselves to be monsters associated with target spots when achieving sensing tasks. Lokemon is also expressed as a PubSub node so that the data from Lokemon can be easily accessed in the same way as data from IoT is assessed. The article explains the concept of Lokemon and its programming model. We report our evaluation of the effectiveness of Lokemon in a campus experiment that was performed for four weeks.

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CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications

M Kawano, T Yonezawa, T Tanimura, NK Giang, M Broadbent, R Lea, ...

EAI International Conference on IoT in Urban Space, 3-15 (EAI/Springer Innovations in Communication and Computing) 3 - 15 2020

Joint Work, ISSN 25228595

A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities.

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AffectiveNemo: Promoting Owner-Fish Interaction by Displaying Observed Fish Emotion with Speech Bubble

N Isokawa, J Nakazawa, T Okoshi

Proceedings of the Sixth International Conference on Animal-Computer … (ACM International Conference Proceeding Series) 18 - 5 2019.11

Research paper (international conference proceedings), Joint Work, ISSN 9781450376938

Aquarium fish is one of the most common household companion animals for humans. Keeping and appreciating aquarium fish enriches us and provides comfort to our lives. However, difficulty in communicating and understanding their fish may negatively impact the person's motivation to continuously take care of their fish. In order to enhance human-fish interaction, we propose “AffectiveNemo”, an ACI system for aquarium fish, that estimates and displays “observed emotion” (defined as fish's emotional states that the system estimates based on observations by humans), via speech bubbles shown on an LCD display placed behind the aquarium. The system estimates the observed emotion by analyzing the movement of the fish in the aquarium, and combining this data along with the emotions that the humans perceive the fish to have. We evaluated the accuracy of each module as a preliminary experiment, conducted a user study on the influence of displaying observed emotion, and evaluated the utility of the system. Our findings concluded that the system can correctly detect the observed emotion, further enhance viewer sensitivity by displaying the observed emotion of aquarium fish that humans are perceptive, and promote the interest in aquarium fish.

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SleepThermo: The affect of in-cloth monitored body temperature change during sleep on human well-being

K Katsumata, Y Noda, N Isokawa, S Katayama, T Okoshi, J Nakazawa

Adjunct Proceedings of the 2019 ACM International Joint Conference on … (UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers) 1174 - 1177 2019.09

Research paper (international conference proceedings), Joint Work, ISSN 9781450368698

With the advent of 24/7 technology-driven society, there are rising discomforts and increasing concerns over sleep. Quality of sleep critically affects human well-being and everyday performance. A good night’s sleep, therefore, is essential to prepare one’s mind and body for the next day. In this paper, we propose SleepThermo, a system that identifies the affect of in-cloth monitored body temperature change during sleep on human well-being. The primary purpose of this research is to determine the relationship between in-clothing body temperature and mental/physical conditions, and then elicit positive insights to improve sleep quality. Our evaluation proved that there is a potential for body temperature to be used in identifying mental and physical conditions. In addition, personalized models achieved better balanced accuracy than overall models in all subjects.

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WellComp 2019: second international workshop on computing for well-being

T Okoshi, J Nakazawa, JG Ko, F Kawsar, S Pirttikangas

Research paper (international conference proceedings), Joint Work, ISSN 9781450368698

With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as “efficiency” and “productivity”, the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our second “WellComp” (Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users’ well-being that covers physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2019 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to wellbeing and ubiquitous computing.

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Situation-Aware Emotion Regulation of Conversational Agents with Kinetic Earables

S Katayama, A Mathur, M Van den Broeck, T Okoshi, J Nakazawa, ...

2019 8th International Conference on Affective Computing and Intelligent … (2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019) 725 - 731 2019.09

Research paper (international conference proceedings), Joint Work, ISSN 9781728138886

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Demo: Motivating long-term dietary habit modification through mobile MR gamification

Katsumata K, Eigen Y, Noda Y, Tsuruoka M, Hashiba S, Numoto S, Katayama S, Okoshi T, Nakazawa J

MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services) 671 - 672 2019.06

Research paper (international conference proceedings), Joint Work, ISSN 9781450366618

In correlation with the socio-economic development, changes in people’s lifestyle brought about significant impact on dietary patterns. Though public concerns over healthy eating are increasing, many are still uncertain when choosing a well balanced meal amid welter of information. In this paper, we propose “KomaFLens”, a mobile system and application built for Microsoft HoloLens, which aims to motivate long term dietary habit modification through gamification. The primary purpose of this research is to enhance the users’ nutritional knowledge and to guide them to make healthier choices in their diet. Our preliminary evaluation revealed interesting points for discussion regarding the procedure for capturing food labels. Streamlining the operational method to boost tractability will improve the accuracy when recording food intakes.

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Demo: Situation-aware conversational agent with kinetic earables

Katayama S, Mathur A, Okoshi T, Nakazawa J, Kawsar F

Research paper (international conference proceedings), Joint Work, ISSN 9781450366618

Conversational agents are increasingly becoming digital partners of our everyday computing experiences offering a variety of purposeful information and utility services. Although rich on competency, these agents are entirely oblivious to their users’ situational and emotional context today and incapable of adjusting their interaction style and tone contextually. To this end, we present a first-of-its-kind situation-aware conversational agent on kinetic earable that dynamically adjusts its conversation style, tone, volume in response to users emotional, environmental, social and activity context gathered through speech prosody, ambient sound and motion signatures. In particular the system is composed of the following components: • Perception Builder: This component is responsible for building an approximate view of user’s momentary experience by sensing his/her 1) physical activity, 2) emotional state, 3) social context and 4) environmental context using different purpose-built acoustic and motion sensory models [4, 5]. • Conversation Builder: This component enables a user to interact with the agent using a predefined dialogue base, and for this demo, we have used Dialogflow [1] populated with a set of situation-specific dialogues. • Affect Adapter: This component is responsible for guiding the adaptation strategy for the agent’s response corresponding to the user’s context, taking into account the output of the perception builder and a data-driven rule engine. We have devised a set of adaptation rules using multiple quantitative and qualitative studies that describe the prosody, volume and speed to shape agents response. • Text-to-Speech Builder: This component is responsible for synthesising the agent’s response in a voice that accurately reflects a user’s situation using IBM Bluemix Voice service [2]. This synthesis process interplays various voice attributes, e.g., pitch, rate, breathiness, glottal tension etc. to transform agents voice according to the rule of the Affect Adapter.

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CityFlow: exploiting edge computing for large scale smart city applications

NK Giang, VCM Leung, M Kawano, T Yonezawa, J Nakazawa, R Lea, ...

2019 IEEE International Conference on Big Data and Smart Computing (BigComp … (2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings) 1 - 4 2019.04

Joint Work, ISSN 9781538677896

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Cruisers: An automotive sensing platform for smart cities using door-to-door garbage collecting trucks

Y Chen, J Nakazawa, T Yonezawa, H Tokuda

Ad Hoc Networks 85, 32-45 (Ad Hoc Networks) 85 32 - 45 2019.03

Research paper (scientific journal), Joint Work, ISSN 15708705

Due to the advance in information and communication technologies, smart cities have become an increasingly appealing vision. One indispensable basis in the research, development and implementation of smart cities is urban sensing technology. Automotive sensing is a novel urban sensing technology that equips sensors to vehicles, like buses, taxi cabs or garbage trucks and utilizes their mobility to help to conduct urban sensing tasks. In this paper, we present Cruisers, an automotive sensing platform for smart cities, based on the following ideas: a) Garbage collecting trucks are used to host sensors, b) Cellular communication network is used to deliver sensed data to servers, and c) Proxy server(s) are adopted to convert the format of sensed data to required ones, such as sampling rate, spatial interval or pub/sub protocol. To validate the feasibility of Cruisers, we implement a real system that consists of 66 customized sensing modulars integrating typical environmental and mobility sensors and deploy them into the garbage collecting trucks at Fujisawa, a medium-sized city in Japan. Performance evaluation is conducted to investigate the performance of Cruisers in terms of scalability, data publishing delay and spatial coverage.

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Resilient Edge: A scalable, robust network function backend

Y Hayakawa, K Yasukata, J Nakazawa, M Honda

IEICE Transactions on Information and Systems 102 (3), 550-558 (IEICE Transactions on Information and Systems) E102D ( 3 ) 550 - 558 2019.03

Joint Work, ISSN 09168532

Increasing hardware resources, such as multi-core and multi-socket CPUs, memory capacity and high-speed NICs, impose significant challenges on Network Function Virtualization (NFV) backends. They increase the potential numbers of per-server NFs or tenants, which requires a packet switching architecture that is not only scalable to large number of virtual ports, but also robust to attacks on the data plane. This is a real problem; a recent study has reported that Open vSwitch, a widely used software switch, had a buffer-overflow bug in its data plane that results the entire SDN domain to be hijacked by worms propagated in the network. In order to address this problem, we propose REdge. It scales to thousands of virtual ports or NFs (as opposed to hundreds in the current state-of-the art), and protect modular, flexible packet switching logic against various bugs, such as buffer overflow and other unexpected operations using static program checking. When 2048 NFs are active and packets are distributed to them based on the MAC or IP addresses, REdge achieves 3.16 Mpps or higher packet forwarding rates for 60 byte packets and achieves the wire rate for 1500 byte packets in the 25 Gbps link.

Access to Document (DOI)

OmimamoriNet: an outdoor positioning system based on Wi-SUN FAN network

Y Chen, M Sakamura, J Nakazawa, T Yonezawa, A Tsuge, Y Hamada

2018 Eleventh International Conference on Mobile Computing and Ubiquitous … (2018 11th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2018) 1 - 6 2019.02

Joint Work, ISSN 9784907626341

We propose in this paper an outdoor positioning system based on Wi-SUN FAN network, the goal of which is to protect the elderly, young children and even pets via estimating their locations in a city. In order to achieve long-term portability and network-side positioning, the system does not directly rely on GPS receiver mounted on terminals but use machine learning for location estimation via the received signal strength indication (RSSI) measurements. In particular, the system consists of Wi-SUN beacons, Wi-SUN base-stations and vehicular devices. A beacon, attached to the one to be positioned, broadcasts wireless signal periodically so that its location can be estimated using machine learning algorithms from the RSSIs measured at multiple base-stations that are densely deployed over a city to construct an ad hoc network. Using the mobility of vehicles that roam over a city routinely, such like garbage collection trucks, buses and taxies. Vehicular devices containing both a Wi-SUN beacon and a GPS are used to collect RSSIs and the corresponding GPS coordinates to train the estimation models. We develop a prototype system consisting of 9 base-stations and deploy it to our university campus to conduct a field experiment to validate the proposed approach. Offline analysis on the data collected from the experiment showed that a RandomForest learner performs best among four selected learning algorithms using the default parameters of Weka 3.8, which achieves a mean absolute error of 35.43m and a root mean squared error of 44.21m, respectively. Evaluation on network performance is also conducted.

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Damaged lane markings detection method with label propagation

T Nukita, Y Kishimoto, Y Iida, M Kawano, T Yonezawa, J Nakazawa

2018 IEEE 24th International Conference on Embedded and Real-Time Computing … (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018) 203 - 208 2019.01

Joint Work, ISSN 9781538677599

We propose a damaged traffic lane detection method ensuring high accuracy in spite of only a few number of supervised data which are labeled traffic lane images. In general, supervised machine learning approach is very powerful for image classification. However, preparing a large amount of supervised data is time-consuming task because labeling damaged or not damaged is usually done manually through visual inspection of images. Thus, lowering the cost of labeling data is a great concern. To this end, we adopt a semi-supervised machine learning approach which learns from both labeled and unlabeled data by constructing graph based on the image similarity. We captured a large amount of the road lane images. Then, we constructed graph structure whose nodes are the road lane images and whose edges are the similarity between the images. In several nodes, we assigned labels which denote "damaged" or "not damaged." Finally we utilized the label propagation, which made it possible to infer the labels of the unlabeled data from the labeled data. These estimation resulted in the accuracy rate over 85% from the supervised data, which accounted for only 1.8% of the total data.

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Deepcounter: Using deep learning to count garbage bags

K Mikami, Y Chen, J Nakazawa, Y Iida, Y Kishimoto, Y Oya

Joint Work, ISSN 9781538677599

This paper proposes DeepCounter, an automotive sensing system where deep learning based image processing technology is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck in order to sense a fine-grain spatio-temporal distribution on the amount of disposed garbage in cities that is envisioned to be helpful to develop novel applications related to garbage collection there. A prototype system is implemented on a GPU-integrated signal-board computer. A detection-tracking-counting (DTC) algorithm is developed and implemented based on the single shot multibox detector (SSD), a well-known real-time object detection algorithm. Experimental evaluation validates the feasibility of the proposed approach using video of realistic garbage collection in Fujisawa city, Japan.

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PerCom PhD Forum 2019: PhD Forum on Pervasive Computing and Communications 2019-Program

V Kalogeraki, J Nakazawa

2019 IEEE International Conference on Pervasive Computing and Communications … (2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019) 2019

ISSN 9781538691519

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Continuous shape changing locomotion of 32-legged spherical robot

H Nozaki, Y Kujirai, R Niiyama, Y Kawahara, T Yonezawa, J Nakazawa

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems … (IEEE International Conference on Intelligent Robots and Systems) 2721 - 2726 2018.12

Research paper (international conference proceedings), Joint Work, ISSN 9781538680940

Shape changing robot is an approach towards locomotion on uncertain terrain due to its omni-directional features. However, the current locomotion method for such robots rely on discontinuous rolling. We propose a free form locomotion: an omni directional continuous crawling for deformable robots. This method introduce continuous shifting of contact surface similar to amoeba movement. A Mochibot that has thirty two telescopic legs is developed to verify the proposed locomotion method. Through the experiments, we have confirmed that the robot can track smooth paths: straight, smooth, and hand written curves. We also evaluate errors between desired and measured trajectories of the robot.

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Poster abstract: Using deep learning to count garbage bags

Mikami K, Chen Y, Nakazawa J

SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems (SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems) 329 - 330 2018.11

Joint Work, ISSN 9781450359528

The information of daily garbage diposal can be used to develop many appealing applications in smart cities. This poster introduces DeepCounter, an automotive sensing system to providing a fine-grained spatio-temporal distribution on the amount of disposed garbage bags. In the system, deep learning based image processing is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck. A prototype system is implemented and experimental evaluation validates the feasibility of our proposal using realistic garbage collection videos in Fujisawa city, Japan.

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Comparing ESM timings for emotional estimation model with fine temporal granularity

W Sasaki, J Nakazawa, T Okoshi

Proceedings of the 2018 ACM International Joint Conference and 2018 … (UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers) 722 - 725 2018.10

Joint Work, ISSN 9781450359665

Towards wellbeing-awareness in computing, researches for estimating users' emotions using smartphone sensor data have been actively conducted as smartphones are getting more and more ubiquitous. Most studies have constructed emotion estimation models based on machine learning with contextual data from the smartphones and user's self-reporting ground truth label often collected via Experience Sampling Method (ESM). However, snice our emotion changes frequently in our daily lives, trying to collect the ground truth of such volatile emotions leads a storm of ESMs which could be burden to the users. In order to find better ESM methods, we propose and compare 3 ESMs, namely Randomized ESM that executes in randomly timings, Trigger ESM that executes when the user's behavior changes, and Unlocking ESM that sets up ESM on the unlocking screen. We constructed various emotional estimation models with four types of time granularity (1 day, 1/3 day, 3 hours, 1 hour) in four weeks experience with eight persons. As for the response rate, Unlocking ESM was the highest. In addition, it was clear that Unlocking ESM had the highest estimation accuracy in most cases.

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WellComP 2018: First international workshop on computing for well-being

T Okoshi, JG Ko, S Pirttikangas, J Nakazawa, F Kawsar

Joint Work, ISSN 9781450359665

With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as “efficiency” and “productivity”, the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our first “WellComp” (Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users' well-being that covers both physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2018 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to wellbeing and ubiquitous computing.

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Accelerating Urban Science by Crowdsensing with Civil Officers

T Yonezawa, K Takeuchi, T Ito, M Sakamura, Y Kishino, F Naya, N Ueda, ...

Research paper (international conference proceedings), Joint Work, ISSN 9781450359665

We present how crowdsensing with civil officers contribute to collect larger amount of urban data with high spatial-temporal coverage, and enable to understand urban features by analyzing the data. We implemented a crowdsensing system which fits to daily city management, and conducted long-term experiment with garbage section of Fujisawa city, Japan. Based on analysis of collected data, we show that our system provide fine-grained urban data compared to crowdsensing with citizens with improving efficiency of city management. In addition, we analyze our dataset with demographic urban data, and clarified the relationship between pattern of uncollectible garbage thrown and features of resident area.

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Demo: Providing information of hidden spot for tourists to increase tourism satisfaction

Katayama S, Obuchi M, Okoshi T, Nakazawa J

UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers (UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers) 377 - 380 2018.10

Joint Work, ISSN 9781450359665

In this research, we propose a system that allows one to find a hidden spot that is scenic, yet unpopulated, with the intention of improving the satisfaction of one's trip. These hidden spots are not yet noted in the existing tourism services, however can provide a new place for the tourists to discover. We developed an application as a prototype to analyze and pinpoint hidden spots by using venue data from Foursquare [1]. We acquired 19,705 data around Tokyo and calculated the relative hiding spot degree. Thus, we made it possible to provide users with hidden spot information from smartphone application.

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Demo: Meal photo SNS with mutual healthiness evaluation for improving users' eating habits

Eigen Y, Nishiyama Y, Okoshi T, Nakazawa J, Yonezawa T

Joint Work, ISSN 9781450359665

We propose a Meal Photo SNS (called HealthyStadium) for improving users' eating habits by mutually assessing each others' health. This application's method is to evaluates pictures of meals leads to the realization of sustainable journaling. In addition, we implement competitive awareness that motivates the users to improve their eating habits by allowing users to share their healthiness ranking amongst the users. Through our study, we confirmed that our system enables users to motivate for improving eating habits. Also we found the number of records is increased by our system.

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Senbay: a platform for instantly capturing, integrating, and restreaming of synchronized multiple sensor-data stream.

Y Nishiyama, AK Dey, D Ferreira, T Yonezawa, J Nakazawa

MobileHCI Adjunct, 291 291 - 291 2018

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Automotive Sensing for Smart Cities: Current Practices and Challenges

Y Chen, T Yonezawa, J Nakazawa

2018

Introduction to the Special Issue on Real-Time, Embedded and Cyber-Physical Systems

NAKAZAWA JINChang, Li-Pin; Kuo, Tei-Wei; Gill, Chris; et al.

ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 13 ( SI ) 2014.11

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