Research topics

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Overview

Humanophilic systems Lab. researches on cyber-physical systems (CPS) that snuggle up to human life by combining sensors (IoT), machine learning, networks, and applications. Primarily, we are focusing on human activity recognition using sensors (IoT), machine learning (AI). However, we cover various topics widely. Sometimes, we develop novel sensors (Hardware) and applications. In recent years, we start focusing on research related to human behavior change by information technologies as the next research beyond human activity recognition.

Sensing & Network

Activity Recognition by tiny-wearable sensor SenStick
SenStick is the platform including the tiny BLE-enable sensing board (eight typical MEMS sensors are embedded), peripheral software, and 3D case data. It was developed to focus on data analysis, which is the essence of IoT research, and ready to use upon purchase without having to do electronic work. Anyone can easily measure data by using an iOS / Android application or Node.js library. The unusual shape, small and stick-shaped, is suitable for making our belongings such as glasses, chopsticks, and canes a sensor. A rechargeable LiPo battery is used instead of coin batteries (Competetive product usually adopts a coin battery). The BLE chip is equipped with Nordic’s nRF52, which allows advanced data processing with Cortex-M4F. Unlike a general BLE sensor, a large-capacity flash memory (32 Mbytes) is embedded on the board, so continuous logging is possible without a smartphone. SenStick has released circuit diagrams, firmware, and peripheral software, all as university research results, and it is possible to rewrite the firmware and develop original sensor boards.
  1. Masashi Takata, Yugo Nakamura, Yohei Torigoe, Manato Fujimoto, Yutaka Arakawa, Keiichi Yasumoto, “Strikes-Thrusts Activity Recognition Using Wrist Sensor Towards Pervasive Kendo Support System,” WristSense’19 – Workshop on Sensing Systems and Applications using Wrist Worn Smart Devices, March 2019.
  2. Yugo Nakamura, Yutaka Arakawa, Takuya Kanehira, Masashi Fujiwara, and Keiichi Yasumoto, “SenStick: Comprehensive Sensing Platform with an Ultra Tiny All-In-One Sensor Board for IoT Research，” Journal of Sensors, vol. 2017, Article ID 6308302, 16 pages, 2017. doi:10.1155/2017/6308302
  3. Yugo Nakamura, Yutaka Arakawa, and Keiichi Yasumoto, “SenStick: A Rapid Prototyping Platform for Sensorizing Things,” The Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2016), 4-6 Oct. 2016.
  4. Yugo Nakamura, Takuya Kanehira, Yutaka Arakawa, Keiichi Yasumoto, “SenStick 2: ultra tiny all-in-one sensor with wireless charging,” ACM Ubicomp 2016, Demo, pp. 337–340, Sep. 2016. (Best Demo Award)
  5. Yutaka Arakawa, “SenStick: Sensorize Every Things,” ACM Ubicomp 2015, Demo, pp.349–352, Sep. 2015.
Context recognition by an eye tracker (Pupil/Tobii)
There is research to sense eye movements in order to recognize human context. Through gaze and pupil diameter, it is possible to unravel information about what you are interested in and how much you understand. Measuring devices are called eye trackers, and they are commercially available from ones attached to personal computers to wearable ones. Using these devices, we are developing things such as measuring interest during sightseeing and a dynamic textbook creation support system linked to the line of sight.
  1. Yuki Matsuda, Dmitrii Fedotov, Yuta Takahashi, Yutaka Arakawa, Keiichi Yasumoto, Wolfgang Minker, “EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioural Cues and Audio-Visual Data” MIDPI Sensors 2018, 18(11), 3978. (Impact factor: 2.475)
    https://doi.org/10.3390/s18113978
  2. Dmitrii Fedotov, Yuki Matsuda, Yuta Takahashi, Yutaka Arakawa, Keiichi Yasumoto, Wolfgang Minker, “Towards Real-Time Contextual Touristic Emotion and Satisfaction Estimation with Wearable Devices,” IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), Demo, March 2019.
  3. Shoya Ishimaru, Ko Watanabe, Nicolas Großmann, Carina Heisel, Pascal Klein, Yutaka Arakawa, Jochen Kuhn and Andreas Dengel, “Demonstration of HyperMind Builder: Pervasive User Interface to Create Intelligent Interactive Documents,” Ubicomp/ISWC 2018, Oct. 2018.
Urban sensing by a participatory mobile sensing platform ParmoSense
There is still a lot of city information that cannot be searched using services like Google Maps, such as park playground equipment information and collection and delivery times of each postbox. As a method for efficiently sensing such urban data, there is a concept of participatory mobile sensing that utilizes users’ smartphones. Of course, just handing out the app doesn’t help with sensing, so it is necessary to incorporate rewards and gamification to motivate participants. Therefore, we have developed ParmoSense as a participatory mobile sensing platform with gamification functions.
  1. 河中祥吾, 松田裕貴, 諏訪博彦, 藤本まなと, 荒川豊, 安本 慶一, “観光客参加型センシングによる観光情報収集におけるゲーミフィケーションの有効性調査,” 情報処理学会 マルチメディア, 分散, 協調とモバイル (DICOMO2018) シンポジウム, 2018年7月4日-6日.
  2. 松田裕貴，河中祥吾，諏訪博彦，荒川豊，安本慶一, “ユーザ参加型センシングの割り込みに対する応答性調査 〜時空間データとタスク難易度およびユーザ属性による考察〜,” 情報処理学会 マルチメディア, 分散, 協調とモバイル (DICOMO2017) シンポジウム, 2017年6月28日-30日. (最優秀プレゼンテーション賞)
  3. Yutaka Arakawa and Yuki Matsuda, “[Invited Paper] Gamification mechanism for enhancing a participatory urban sensing: survey and practical results,” Journal of Information Processing, Vol.24, No.1, pp.31–38, Jan. 2016.
  4. 松田裕貴, 新井イスマイル, 荒川豊, 安本慶一, “スマートフォン搭載照度センサの個体差に対応した夜道における街灯照度推定値校正手法の提案,” 情報処理学会論文誌, Vol.57, No.2, pp. 520–531 , 2016年2月.
  5. Yoshitaka Ueyama, Morihiko Tamai, Yutaka Arakawa, and Keiichi Yasumoto, “Gamification-Based Incentive Mechanism for Participatory Sensing”, IEEE CROWDSENSING 2014 (First International Workshop on Crowdsensing Methods, Techniques, and Applications) in conjunction with IEEE PerCom 2014, pp.98–103, Mar. 24, 2014.
  • Activity recognition by WiFi signal analytic
  • Activity recognition by Acoustic sound analysis
  • Activity recognition by a smartphone and smartwatch
  • Urban sensing by 5G and LoRaWAN
  • Urban Sensing by social data analysis
  • Energy-harvest Human Activity recognition
  • Communication skill sensing by analyzing a micro gesture and facial expression

 HumanoPhillic system in a work place

Estimating Human Psychological State (Stress, QoL, Work Engagement)
We focus on estimating human psychological states, such as stress and QoL (Quality of Life). Under the work style reform, Workers stress checks have become a social issue, and the goal is to make it easier to know your condition with smartphones and wearable devices. So far, paper questionnaires such as WHOQOL-BREF have been used for QoL surveys, and when analyzing the correlations between the results and sensor data, it has become clear that it can be recognized to some extent by machine learning. In the future, we will respond to experiments at actual companies and through questionnaires (work engagement, etc.)
  1. Chishu Amenomori, Teruhiro Mizumoto, Hirohiko Suwa, Yutaka Arakawa, Keiichi Yasumoto, “A Method for Simplified HRQOL measurement by Smart Devices,” 7th EAI International Conference on Wireless Mobile Communication and Healthcare (MobiHealth 2017), November 2017.
  2. 雨森千周，水本旭洋，荒川豊，安本慶一, “WHOQOL-BREFに基づくHRQOL評価におけるスマートデバイスを用いた簡易計測手法の提案,” 情報処理学会 マルチメディア, 分散, 協調とモバイル (DICOMO2017) シンポジウム, 2017年6月28日-30日. (優秀プレゼンテーション賞)
Continuous posture sensing chair CENSUS
It is known that office workers’ oversitting can cause back pain and stiff shoulders. Therefore, it is important to adjust the chair to suit your body shape, and various high-performance chairs are on the market. However, in fact, there is no quantitative measurement of what kind of posture when working sat on the chair or whether you are sat in a good posture. Therefore, we have developed a chair that can always sense posture.
  1. Yasuhiro Otoda, Teruhiro Mizumoto, Yutaka Arakawa, Chihiro Nakajima, Mitsuhiro Kohana, Motohiro Uenishi, and Keiichi Yasumoto, “Census: Continuous Posture Sensing Chair for Office Workers,” 2018 IEEE International Conference on Consumer Electronics (ICCE), January 2018.
  2. 音田恭宏, 水本旭洋，荒川周造，小花光広，中島千尋，上西基弘，荒川豊，安本慶一, “着座姿勢に基づくワークプレイスの高機能化の検討,” 情報処理学会全国大会, 6T-05, 2017年3月18日.
  3. 音田恭宏, 水本旭洋, 荒川豊, 荒川周造, 中島千尋, 小花光広, 上西基弘, 安本慶一, “椅子に装着したモーションセンサを用いた着座姿勢推定手法,” 電子情報通信学会ライフインテリジェンスとオフィス情報システム研究会, 2017年3月2日, 石垣市.
Energy-harvest place recognition system
By collating information about where and how many times you are in an office building, you can objectively grasp the number of times you go to a smoking area and the number of meetings, and reflect and improve efficiency. There are various methods for estimating indoor locations, but in any case, power sources may be a problem. Therefore, we propose a place estimation method that does not require charging by using energy harvesting elements such as solar cells both as a power source and sensor. This paper was accepted as a full paper at the top conference IEEE PerCom2019.
  1. Yoshinori Umetsu, Yugo Nakamura, Yutaka Arakawa, Manato Fujimoto, Hirohiko Suwa, “EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables,” IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), Full paper, March 2019.
  2. Yutaka Arakawa, Yoshinori Umetsu, Yugo Nakamura, Manato Fujimoto, Hirohiko Suwa, Keiichi Yasumoto, “Poster: Feasibility Study toward a Battery-free Place Recognition System based on Solar Cells,” Ubicomp/ISWC 2018, Oct. 2018.
  3. 梅津雅吉, 中村 優吾, 荒川豊, 藤本まなと, 諏訪博彦, 安本慶一, “EHAAS : 環境発電素子の発電量に基づくウェアラブル場所推定システム,” 情報処理学会 マルチメディア, 分散, 協調とモバイル (DICOMO2018) シンポジウム, 2018年7月4日-6日.
  4. 梅津雅吉, 中村 優吾, 荒川豊, 藤本まなと, 諏訪博彦, 安本慶一, “環境発電素子の発電量に基づくコンテキスト認識手法の提案,”
    情報処理学会第87回モバイルコンピューティングとパーベイシブシステム研究会, 2018年5月25日. (奨励発表賞)
  5. 梅津雅吉, 藤原 聖司, 中村 優吾, 藤本 まなと, 荒川豊, 安本慶一, “環境発電素子の発電量に基づく屋内行動認識システムの検討,” 電子情報通信学会総合大会, ISS特別企画「学生ポスターセッション」, No.ISS-SP-063, p.178, 2018年3月20日. (優秀ポスター賞)
  • Security and Trust for IoT devices
  • Optimizing a timing of push notification

 Application & Service

Behavior Change Support System
Like Apple Watch’s activity reminder, it is becoming a reality that information devices work on people and change their future behavior. These information devices that can change their behavior are sometimes referred to as Digital Medicine for lifestyle-related disease prevention and are expected to spread to society in the future. We are developing digital signage that actively talks to people passing by, and is conducting empirical research on whether these can stimulate behavioral changes in daily dialogue.
  1. Zhihua Zhang, Yuta Takahashi, Manato Fujimoto, Yutaka Arakawa, Keiichi Yasumoto, “Investigating Effects of Interactive-signage-based Stimulation for Promoting Behavior Change,” Computational Intelligence Journal (Conditionally accepted)
  2. Zhihua Zhang, Yutaka Arakawa, Harri Oinas-kukkonen, “Design of Behavior Change Environment with Interactive Signage Having Active Talk Function,” PerPersuasion’19 – 1st International Workshop on Pervasive Persuasive System for Behavior Change, March 2019.
  3. Zhihua Zhang, Yuta Takahashi, Manato Fujimoto, Keiichi Yasumoto, and Yutaka Arakawa, “Investigating User Reactions to Interactive-signage-based Stimulation Toward Behavior Change,” 11th International Conference on Mobile Computing and Ubiquitous Network (ICMU), Oct. 2018.
  4. 張志華, 高橋雄太, 藤本まなと, 荒川豊, 安本 慶一, “行動変容を誘発するインタラクティブサイネージへのユーザの反応調査,” 情報処理学会 マルチメディア, 分散, 協調とモバイル (DICOMO2018) シンポジウム, 2018年7月4日-6日.
Walking support system by  unconscious speed control based on predicted future heart rate
The number of people walking is increasing due to health-consciousness. However, if you do not walk at an appropriate pace, there is a risk of increasing the burden on the heart. Therefore, we developed a walking support system that predicts the future. heartbeat before walking from the past exercise history and the gradient information of the route, and presents the optimal route based on that prediction. We have developed a system that can change the pace of pedestrians unconsciously by exquisitely changing the speed of the music.
  1. Atsushi Otsubo, Hirohiko Suwa, Yutaka Arakawa, Keiichi Yasumoto, ”BeatSync: Walking Pace Control through Beat Synchronization between Music and Walking,” IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), Demo, March 2019.
  2. 大坪敦, 諏訪博彦, 荒川豊, 安本慶一, “音楽の引き込み効果を用いた歩行ペース誘導アプリの検討,” 情報処理学会関西支部大会, 2018年9月30日.
  3. Shogo Maenaka, Hirohiko Suwa, Yutaka Arakawa, and Keiichi Yasumoto, “Heart Rate Prediction for Easy Walking Route Planning,” SICE Journal of Control, Measurement, and System Integration, Vol.11, No.4, pp.284–291, July 2018.
  4. Shogo Maenaka, Hirohiko Suwa, Yutaka Arakawa, and Keiichi Yasumoto, “Recommending Optimal Route for Walking Support based Heart Rate Prediction,” The Second International Workshop on Smart Sensing Systems (IWSSS’17) , August 2017.
  5. 前中省吾, 諏訪博彦, 荒川豊, 安本慶一, “ウォーキング支援のための心拍数予測に基づいた最適経路探索手法,” 情報処理学会 マルチメディア, 分散, 協調とモバイル (DICOMO2015) シンポジウム, pp. 1186 — 1193, 2015年7月. (優秀論文賞)
  • Smart Office
  • Smart appliance control in a smart home
  • Smart care reporting system in a nursing home
  • Efficient exercise support system

Past topics

  • 2010- : Wireless near field communication based on UDP broadcast.
  • 2009- : Indoor positioning method with WiFi mesh network
  • 2007-2008 : Pre-downloaded Contents Delivery Network based on P2P communication.
  • 2007-2008 : uGrid (Ubiquitous Grid) network and its applications (wall display, IP over everything)
  • 2007-2008 : High speed path calculation method with a dynamic reconfigurable processor DAPDNA2
  • 2001-2006 : QoS control method on Optical Burst Switching Network
  • 2000-2001 : Packet discarding method in ATM networks