Publication

@inproceedings{Globecom2023_Taya,

title = {Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs},

author = {Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2023},

date = {2023-12-04},

urldate = {2023-12-04},

booktitle = {Proceedings of IEEE Global Communications (GLOBECOM) 2023},

publisher = {IEEE},

address = {Kuala Lumpur, Malaysia},

series = {GLOBECOM 2023},

abstract = {Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimization algorithms to FL by considering machine learning (ML) tasks as parameter optimization problems. Conversely, the consensus-based multi-hop federated distillation (CMFD) proposed in the authors' previous work makes neural network (NN) models get close with others in a function space rather than in a parameter space. Hence, this study solves two unresolved challenges of CMFD: (1) communication cost reduction and (2) visualization of model convergence. Based on a proposed dynamic communication cost reduction method (DCCR), the amount of data transferred in a network is reduced; however, with a slight degradation in the prediction accuracy. In addition, a technique for visualizing the distance between the NN models in a function space is also proposed. The technique applies a dimensionality reduction technique by approximating infinite-dimensional functions as numerical vectors to visualize the trajectory of how the models change by the distributed learning algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3600100.3626263,

title = {A Trajectory Estimation Method from Spatially Sparse and Noisy Beacon Data Based on Spring Dynamics},

author = {Junya Maruyama and Yudai Honma and Yuuki Nishiyama and Yoshihiro Kawahara},

url = {https://doi.org/10.1145/3600100.3626263 

https://buildsys.acm.org/2023/},

doi = {10.1145/3600100.3626263},

isbn = {9798400702303},

year  = {2023},

date = {2023-11-15},

urldate = {2023-11-15},

booktitle = {Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},

pages = {286–287},

publisher = {Association for Computing Machinery},

address = {Istanbul, Turkey},

series = {BuildSys '23},

abstract = {Analysis of trajectory data within buildings offers insights for optimizing environmental design and habitability. However, data from indoor location sensors tend to be sparse and noisy. This makes it difficult for conventional route estimation models to be applied effectively. Our study seeks to derive detailed, temporally, and spatially rich trajectory data from this compromised sensor information. We achieve this by interpreting trajectories as continuous stay points. To facilitate this, we introduce a building corridor network that conceptualizes buildings as a series of points. Routes are inferred using a sequence estimation model applied to this network. This approach employs spring dynamics, which balance the resistance to staying with the attraction to specific beacons, via mathematical optimization. Notably, our model can deduce a trajectory of 131 points from only 15 beacons with, an accuracy rate of 87. Our method presents a promising avenue for capturing extensive route data.},

keywords = {Architectural Space, Building corridor Network, Human trajectory},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{UbiComp2023_Nishiyama,

title = {Smartwatch-Based Sensing Framework for Continuous Data Collection: Design and Implementation},

author = {Yuuki Nishiyama and Kaoru Sezaki},

year  = {2023},

date = {2023-10-08},

urldate = {2023-10-08},

booktitle = {Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2023 ACM International Symposium on Wearable Computers},

publisher = {Association for Computing Machinery},

address = {Cancun, Mexico},

series = {UbiComp-ISWC '23},

abstract = {Smartwatches are an increasingly popular technology that employs advanced sensors (e.g., location, motion, and microphone) comparable to those used by smartphones. Passive mobile sensing, a method of acquiring human behavior data from mobile and wearable devices inconspicuously, is widely used in research fields related to behavior analysis. In combination with machine learning, passive mobile sensing can be used to interpret various human and environmental contexts without requiring user intervention. Because smartwatches are always worn on the wrist, they have the potential to collect data that cannot be collected by smartphones. However, the effective use of smartwatches as platforms for passive mobile sensing poses challenges in terms of battery life, storage, and communication. To address these challenges, we designed and implemented a tailored framework for off-the-shelf smartwatches. We evaluated power consumption under eight different sensing conditions using three smartwatches. The results demonstrate that the framework can collect sensor data with a battery life of 16-31 h depending on the settings. Finally, we considered potential future solutions for optimizing power consumption in passive sensing with off-the-shelf smartwatches.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{imci2023_onodera,

title = {Assessing Infant and Toddler Behaviors through Wearable Inertial Sensors: A Preliminary Investigation},

author = {Ayaka Onodera and Riku Ishioka and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2023},

date = {2023-10-01},

urldate = {2023-10-01},

booktitle = {Proceedings of the 20th ACM International Conference on Multimodal Interaction},

pages = {xx-xx},

publisher = {Association for Computing Machinery},

address = {Paris, France},

series = {ICMI '23},

abstract = {Accurately assessing a child’s health status and taking appropriate action in case of illness or other emergencies is crucial to recording and sharing a childcare situation, such as sleeping hours, amount of exercise, and timing of meals. 

Although numerous applications and systems have been proposed to assist in recording and sharing these records, the process is still performed manually, representing a significant burden for parents. Therefore, automatic recording of infants' and toddlers' daily activities is required. 

Moreover, existing automatic infant behavior recognition methods have significant limitations on the space and target of application. 

In this study, we implement a machine-learning model to investigate and propose a method to classify typical daily behaviors of infants and toddlers using a chest-mounted low-sampling rate accelerometer. 

In particular, the proposed method classifies eight activities: sleeping, crawling, walking, standing, sitting, drinking milk, eating baby food, and holder by a caregiver. 

As a dataset, we collected accelerometer data for nearly 18 hours with reference videos from ten infants and toddlers between 6 and 24 months. Based on the data, we extracted 60 time- and frequency-domain features calculated from the single accelerometer and a user feature to recognize the target daily activities. In the best case of our performance evaluation with different window sizes and machine learning models, our classification model reaches nearly 80% accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wowmon2023_han,

title = {HeadSense: Visual Search Monitoring and Distracted Behavior Detection for Bicycle Riders},

author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://coe.northeastern.edu/Groups/wowmom2023/index.html},

doi = {10.1109/WoWMoM57956.2023.00043},

isbn = {979-8-3503-3165-3},

year  = {2023},

date = {2023-07-12},

urldate = {2023-07-12},

booktitle = {2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},

pages = {281-289},

address = {Boston, Massachusetts},

abstract = {Distracted riding behavior is one of the main causes of bicycle-related traffic accidents, resulting in a large number of casualties and economic losses every year. There is an urgent need to address this problem by accurately detecting distracted riding behaviors. Inspired by the observation that distracted riding behaviors induce unique head motion features that respond to the rider's attention, we present the HeadSense, a helmet-based system that not only monitors the visual search episode of the rider but also detects distracted riding behaviors. Specifically, HeadSense leverages the inertial motion unit (IMU) to recognize distracted behaviors such as using smartphones, attracting to the roadside element, and abreast riding. We designed, implemented, and evaluated HeadSense through extensive experiments. We conducted experiments with 19 participants inside the university's campus. The experimental results show that HeadSense can achieve an overall accuracy of 86.14% while monitoring visual search episodes. Moreover, HeadSense can detect the occurrence of distracted riding behaviors with an average precision of up to 85.04%.},

keywords = {Head Movement Detection, Human Activity recognition, Mobile sensing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{percom2023_han,

title = {HeadMon: Head Dynamics Enabled Riding Maneuver Prediction},

author = {Zengyi Han and Liqiang Xu and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.percom.org/PerCom2023/},

doi = {10.1109/PERCOM56429.2023.10099215},

isbn = {978-1-6654-5378-3},

year  = {2023},

date = {2023-03-13},

urldate = {2023-03-13},

booktitle = {IEEE International Conference on Pervasive Computing and Communications (PerCom)},

pages = {22--31},

publisher = {IEEE},

address = {Atlanta, USA},

abstract = {Although micro-mobility brings convenience to modern cities, they also cause various social problems, such as traffic accidents, casualties, and substantial economic losses. Wearing protective equipment has become the primary recommendation for safe riding. However, passive protection cannot prevent the occurrence of accidents. Thus, timely predicting the rider's maneuver is essential for active protection and providing more time to avoid potential accidents from happening. Through the qualitative study, we argue that we can use the rider's head dynamic as an information source to predict the rider's following maneuvers. We accordingly present HeadMon, a riding maneuver prediction system for safe riding. HeadMon utilizes the head dynamics of a rider by installing an inertial measurement unit on the helmet. It uses the extracted head dynamics features as the input of the deep learning architecture to achieve prediction. We implemented the HeadMon prototype on Android smartphone as a proof of concept. Through comprehensive experiments with 20 participants, the result demonstrates the excellent performance of HeadMon: not only could it achieve an overall precision of at least 85% for maneuver prediction under a 4s prediction time gap, but it also could keep a high accuracy under a low sampling rate. The low-cost feature of HeadMon allows it to be readily deployable and towards more safety riding.},

keywords = {Head Movement, Human Activity Prediction, mobile computing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{published_papers/40226623,

title = {Enabling Block Transmission on Backoff-based Opportunistic Routing},

author = {Eri Hosonuma and Yuuki Nishiyama and Kaoru Sezaki and Takumi Miyoshi and Taku Yamazaki},

doi = {10.1109/CCNC51644.2023.10059637},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (IEEE CCNC 2023)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{published_papers/40226624,

title = {Cooperative Local Distributed Machine Learning Considering Communication Latency and Power Consumption},

author = {Shota Ono and Taku Yamazaki and Takumi Miyoshi and Yuuki Nishiyama and Kaoru Sezaki},

doi = {10.1109/CCNC51644.2023.10060678},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (IEEE CCNC 2023)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-34609-5_34,

title = {Detecting Hand Hygienic Behaviors In-the-Wild Using a Microphone and Motion Sensor on a Smartwatch},

author = {Haoyu Zhuang and Liqiang Xu and Yuuki Nishiyama and Kaoru Sezaki},

editor = {Norbert A. Streitz and Shiníchi Konomi},

doi = {10.1007/978-3-031-34609-5_34},

isbn = {978-3-031-34609-5},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Distributed, Ambient and Pervasive Interactions},

pages = {470--483},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {In recent years, the emergence of the COVID-19 pandemic has led to new viral variants, such as Omicron. These variants are more harmful and impose more restrictions on people’s daily hygiene habits. Therefore, during the COVID-19 pandemic, it is logical to automatically detect epidemic protective behaviors without user intent. In this study, we used multiple sensor data from an off-the-shelf smartwatch to detect several defined behaviors. To increase the utility and generalizability of the research results, we collected audio and inertial measurement unit (IMU) data from eight participants in real environments over a long period. In the model-building process, we first created a binary classification between hand hygiene behaviors(hand washing, disinfection, and face-touching) and daily behavior. Then, we distinguished between specific hand hygiene behaviors based on audio and IMU. Ultimately, our model achieves 93% classification accuracy for three behaviors(Hand washing, face touching, and disinfection). The results prove that the accuracy of the classification of behaviors has improved remarkably, which also emphasizes the feasibility of recognizing hand hygiene behaviors using inertial acoustic data.},

keywords = {Audio, Hygiene behaviors detection, IMU, Multimodal Fusion, Wearable device},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_sukeda,

title = {Poster abstract: Recursive Queueing Estimation Using Smartphone-based Acoustic Ranging},

author = {Issey Sukeda and Hiroaki Murakami and Yuuki Nishiyama and Hiroaki Murakami and Yoshihiro Kawahara},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

keywords = {Acoustic sensing, Mobile sensing, queueing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_xu,

title = {Poster abstract: Convolutional Compressed Sensing for Smartphone Acceleration Data Compression},

author = {Liqiang Xu and Yuuki Nishiyama and Masamichi Shimosaka and Kota Tsubouchi and Kaoru Sezaki},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

keywords = {Acceleration data, Compressed sensing, Convolutional neural network, Smartphone sensor},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_ishioka,

title = {Poster abstract: UV index estimation leveraging GNSS sensors on smartphones},

author = {Riku Ishioka and Yuuki Nishiyama and Kota Tsubouchi and Kaoru Sezaki},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

keywords = {GNSS, Passive mobile sensing, smartphone, UV index estimation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_suzuki,

title = {Poster abstract: Room Scale Localization Improvement Utilizing Stay Time Characteristics of Each Room},

author = {Ryoto Suzuki and Yuuki Nishiyama and Hiroaki Murakami and Yoshihiro Kawahara and Kaoru Sezaki},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

keywords = {Bluetooth low energy, iBeacon, Indoor localization, Received signal strength indicator},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3557915.3560969,

title = {A Plug-in Memory Network for Trip Purpose Classification},

author = {Suxing Lyu and Tianyang Han and Yuuki Nishiyama and Kaoru Sezaki and Takahiko Kusakabe},

url = {https://doi.org/10.1145/3557915.3560969},

doi = {10.1145/3557915.3560969},

isbn = {9781450395298},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},

publisher = {Association for Computing Machinery},

address = {Seattle, Washington},

series = {SIGSPATIAL '22},

abstract = {Trip purpose plays a critical role in reflecting human mobility behavior. However, it is relatively difficult to determine. With the rapid growth of urban mobility and big mobile data, utilizing these data for trip purpose classification has been a long-term objective to enhance travel demand and behavior models used in urban planning. Although studies on this topic have been extensively conducted, most past research preferred relying on traveler attributes or long-term travel histories to achieve accurate results. These data could be privacy sensitive and often do not satisfy real-world scenarios. This study addresses the problem of classifying trip purpose by only space activity information to avoid privacy conflict. 1) External memories are collected from factorized components based on the non-negative Tucker decomposition scheme. 2) These memories are extended by the cross-attention mechanism to achieve feature augmentation. 3) Subsequently, a novel concept called "latent mode alignment" is proposed. By leveraging the linear characteristics of external memories, geographic contextual latent modes are represented and matched with travel activities; this procedure is called älignment." 4) The gate mechanism controls the eventual outputs for update. The proposed plug-in memory network (PMN), combined with baseline models, effectively outperforms the original settings. Moreover, combination models are validated with strong tolerance through missing data tests, which are common and problematic in real-world scenarios. The proposed PMN is a plug-and-play design that is easy to combine with newly developed classification models, and other memory collection methods can be expected.},

keywords = {Human mobility, matrix factorization, memory network, trip purpose},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mobicovid22_nishiyama,

title = {MOCHA: Mobile Check-in Application for University Campuses Beyond COVID-19},

author = {Yuuki Nishiyama and Hiroaki Murakami and Ryoto Suzuki and Kazusato Oko and Issey Sukeda and Kaoru Sezaki and Yoshihiro Kawahara},

doi = {10.1145/3492866.3557736},

isbn = {978-1-4503-9165-8/22/10},

year  = {2022},

date = {2022-10-18},

urldate = {2022-10-18},

booktitle = {The Twenty-third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing},

volume = {MobiHoc '22},

publisher = {ACM},

address = {Seoul, Republic of Korea},

abstract = {Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.},

keywords = {Bluetooth beacon, COVID-19, Mobile sensing, Shared space management, Social Implementation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smc2022_dong,

title = {DoubleCheck: Single-Handed Cycling Detection with a Smartphone},

author = {Xuefu Dong and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

doi = {10.1109/SMC53654.2022.9945380},

year  = {2022},

date = {2022-10-09},

urldate = {2022-10-09},

booktitle = {2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},

pages = {268-274},

abstract = {Riding bicycles with only one hand on the handlebar can severely undermine the operator’s steering capability and threaten road and transportation safety. Prior studies have exploited motion sensors to detect riding contexts and recognize related behaviors. Nevertheless, they fail to integrate a scheme to account for single-handed riding with elements crucial to danger prevention: awareness of the surroundings, response to danger, and convenient adoption. In this work, we proposed, designed, and implemented DoubleCheck: a smartphone-based real-time framework for cycling hand detection and distraction recognition. The method monitors handlebar holding on different road surfaces and recognizes hazardous distraction activities related to single-handed cycling using motion signals captured by a built-in inertial measurement unit in a handlebar-borne smartphone. It was designed on the premise that single-handed cycling enabled operators to adapt their body movements to different (often distracting) activities. We conducted an evaluation experiment using 22 participants on asphalt and pavement. The results indicate that DoubleCheck achieves an F1-score of 0.96 for hand detection and 0.69 for distraction recognition, demonstrating its efficacy as a candidate rider-safety precautionary measure.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ubicomp2022_shimojo,

title = {A Preliminary Study for Detecting Visual Search Behaviors During Street Walking Using Earable Device},

author = {Kazuki Shimojo and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-09-12},

urldate = {2022-09-12},

booktitle = {Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {USA and UK},

series = {UbiComp '22},

abstract = {Map applications on smartphones are powerful navigation tools for walking among places to visit for the first time and are used widely. On the other hand, checking the application tend to cause trouble on the road such as collisions with people, cars, and objects. To prevent the troubles, we need to detect the walker’s context regarding visual search behaviors and provide appropriate navigation information for the walker. In this paper, we propose a method to detect a walker’s context regarding visual search behaviors by using motion sensors on an earable device. We collected and investigated motion and gaze data from an earable device and gaze tracker respectively during street walking from five participants. Based on the investigation, we create a machine learning model for detecting looking around, smartphone, or normal during walking 

and stopping conditions. Our evaluations show that our models can detect more than 95% of walking and stopping conditions, and 71% of three details conditions during walking, respectively},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ubicomp2022_han,

title = {A Micro-mobility Sensing System to Portray Riding Styles},

author = {Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-09-12},

urldate = {2022-09-12},

booktitle = {Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {USA and UK},

series = {UbiComp '22},

abstract = {Riding style concerns the way a micro-mobility rider chooses to ride, and it plays a significant role in traffic safety. Portraying a rider’s 

riding style is a useful way to guide them towards safer riding behaviors, and offer fine-grained information for insurance companies 

and bike-sharing companies to provide better services. To this end, we propose a micro-mobility sensing system to portray riding 

styles. Utilizing helmet-mounted and bicycle handle-mounted inertial sensors, our sensing system is able to monitor the micro-mobility 

movement status, detect the maneuver behaviors, and the safety check condition of riders. With 10 participants’ experimental data, 

we present the feasibility of detecting maneuver behavior and recording the data that characterize the rider’s riding style with our 

sensing system, and therefore make it a viable addition to the micro-mobility.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mobisys2022_han,

title = {Poster: Head Dynamics Enabled Riding Maneuver Prediction},

author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.sigmobile.org/mobisys/2022/},

year  = {2022},

date = {2022-06-27},

urldate = {2022-06-27},

booktitle = {Proceedings of the 20th Annual International Conference on Mobile Systems, Applications, and Services},

publisher = {Association for Computing Machinery},

address = {Portland, Oregon},

series = {MobiSys '22},

abstract = {While micro-mobility brings convenience to the modern city, they also cause various social problems such as traffic accidents, casualties, and huge economic losses. Wearing protective equipment has become the primary recommendation for safe riding, but passive protection cannot prevent accidents from happening after all. Thus, timely predicting the rider's maneuver is essential for more active protection and buying more time to avoid potential accidents from happening. In this poster, we explore the feasibility of using riders’ head dynamics to predict their riding maneuvers. Through ten participants’ preliminary study, we observed that not only do riders’ head movements appear ahead of their maneuvers but also head movement patterns are distinct with different maneuver intentions. We then construct a deep learning network using Long Short Term Memory, achieving 89% of accuracy on maneuver prediction.},

keywords = {Activity Recognition, Mobile sensing, Wearable sensing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smartcomp2022_komatsu,

title = {Toward Measuring Conversation Duration Using a Wristwatch-type Wearable Device},

author = {Yuki Komatsu and Kazuki Shimojo and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},

publisher = {IEEE Computer Society},

address = {Espoo, Finland},

keywords = {Context-recognition, Conversation Events, Edge Processing, Sound Classification, Wearable Computing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smartcomp2022_kasahara,

title = {Detecting Childcare Activities Using an Off-the-shelf Smartwatch},

author = {Yuki Kasahara and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},

publisher = {IEEE Computer Society},

address = {Espoo, Finland},

keywords = {Activity Recognition, Childcare Activity, Wearable sensing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-05431-0_23,

title = {Enhancing Self-Protection: What Influences Human’s Epidemic Prevention Behavior during the COVID-19 Pandemic},

author = {Liqiang Xu and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1007/978-3-031-05431-0_23},

doi = {10.1007/978-3-031-05431-0_23},

isbn = {978-3-031-05430-3},

year  = {2022},

date = {2022-06-01},

urldate = {2022-01-01},

booktitle = {Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-Being and Health, Art and Creativity: 10th International Conference, DAPI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings, Part II},

pages = {336–351},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

abstract = {Under the circumstance of the rapid spread of the COVID-19 pandemic, enhancing human’s awareness of self-protection is one practical method to slow down the epidemic. In this study, we utilize mobile sensing to track human activity and guide human’s epidemic prevention behavior by gamified feedback techniques by our developed application. Virtually, human’s self-protection awareness is affected by many factors and the measures to enhance people’s self-protection behavior against the epidemic COVID-19 has always been an unresolved issue. In order to search for factors that influence human’s self-protection behavior, we analyzed the relationships between various human activities and the percentage complete of human’s self-protection behavior and we have extracted some more general conclusions from the results. Based on our data analysis results, we also made some proposals to enhance self-protection behavior. Meanwhile, our study illustrates the effectiveness of the method that analyzes human self-protection behavior through mobile sensing. Our study also validates the effectiveness of persuasive technology on human’s self-protection behavior against the COVID-19 pandemic and therefore we advocate enhancing human’s self-protection awareness through external intervention and guidance by smart device.},

keywords = {COVID-19, Infection prevention, Mobile sensing, Persuasive technology., Self-protection behavior, Self-Tracking},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{percom2022_dong,

title = {DoubleCheck: Detecting Single-Hand Cycling with Inertial Measurement Unit of Smartphone},

author = {Xuefu Dong and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

doi = {10.1109/PerComWorkshops53856.2022.9767429},

isbn = {978-1-6654-1648-1},

year  = {2022},

date = {2022-03-21},

urldate = {2022-03-21},

booktitle = {IEEE International Conference on Pervasive Computing and Communications (PerCom)},

publisher = {IEEE},

address = {Pisa, Italy},

abstract = {Riding bikes with only one hand on the handlebar can severely undermine the steering capability of riders and risk road safety. In this study, we propose a first detection framework for monitoring single-hand cycling on bicycle travel, called DoubleCheck. It is based on the premise that riders adapt their body movement during single-hand cycling, which is distinguishable to the sensors even amid noise from the exasperate road surface. The system can detect handlebar-holding under different road conditions using motion signals from a built-in inertial measurement unit (IMU) in a handlebar-mounted smartphone. We implemented the system and invited 10 participants for our evaluation experiment. Our results show that DoubleCheck achieved an F1-score of 0.94 for hand detection, proving its efficacy for real-life implementation to improve road safety.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9982766,

title = {Detecting Face-Mask Wearing Status Using Motion Sensors in Commercially Available Smartwatches},

author = {Shota Ono and Yuuki Nishiyama and Kaoru Sezaki},

doi = {10.1109/HealthCom54947.2022.9982766},

year  = {2022},

date = {2022-01-01},

booktitle = {2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)},

pages = {107-112},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}