As global logistics demand increases, improving the efficiency of warehouse operations has become critical. To achieve this, it's crucial to identify inefficient tasks and layouts by recognizing various warehouse conditions. To address these challenges, we've constructed a large-scale camera infrastructure to convert object positions and movements into data. However, transmitting all video data to the cloud results in significant data transmission and power consumption. Edge AI cameras analyze and extract video data locally, transmitting only essential information and significantly reducing them. Edge AI cameras require a low-computation, high-accuracy object detection model due to limited computational power and complex warehouse environments. Furthermore, the same object appears differently based on the camera's position and angle. Therefore, customizing models for each camera improves accuracy, but the annotation cost would be very high. In this study, we propose a method to perform part of the training data generation on the camera, reducing data transmission and improving annotation efficiency.

In this study, we propose MoDeMIT (Mobility-Derived Multi-Insight Transfer), a human mobility prediction method that transfers various patterns shared across cities, extracted from mobility histories. Existing studies on human mobility prediction have primarily focused on learning the mobility patterns of users in the target city, without considering applications in cities with limited data. Moreover, relying solely on mobility patterns poses inherent limitations on prediction accuracy. The proposed MoDeMIT addresses these issues by defining and transferring multiple patterns shared across cities, such as lifestyle patterns and large-scale mobility patterns derived from mobility histories, as well as mobility patterns. This approach enables improvements in prediction accuracy compared to existing methods. We validate the effectiveness of MoDeMIT using real-world human mobility datasets. Furthermore, in the HuMob Challenge 2025 (GISCUP), MoDeMIT achieved a GEOBLEU score of [Average: 0.1632, CityA: 0.1504, CityB: 0.1471, CityC: 0.1801, CityD: 0.1753] and ranked within the top five teams.

Worker location data is key to higher productivity in industrial sites. Cameras are a promising tool for localization in logistics warehouses since they also offer valuable environmental contexts such as package status. However, identifying individuals with only visual data is often impractical. Accordingly, several prior studies identified people in videos by comparing their trajectories and wearable sensor measurements. While this approach has advantages such as independence from appearance, the existing methods may break down under real-world conditions. To overcome this challenge, we propose CorVS, a novel data-driven person identification method based on correspondence between visual tracking trajectories and sensor measurements. Firstly, our deep learning model predicts correspondence probabilities and reliabilities for every pair of a trajectory and sensor measurements. Secondly, our algorithm matches the trajectories and sensor measurements over time using the predicted probabilities and reliabilities. We developed a dataset with actual warehouse operations and demonstrated the method’s effectiveness for real-world applications.

The rapid growth of e-commerce, rising consumer expectations, and labor shortages in Japan pose challenges for logistics warehouse optimization. While automation has advanced outbound operations, inbound processes remain inefficient. This study presents a comprehensive approach to digitizing and optimizing large-scale logistics warehouses, with a case study at TRUSCO NAKAYAMA Corp. in partnership with Nagoya University. Key technologies include a large-scale camera array, multi-camera object tracking, and smartphone-based task estimation. Our contributions include real-time tracking of personnel and packages, cooperative annotation for improved object recognition, and synthetic data augmentation. Additionally, truck berth analysis and indoor localization enhance operational efficiency. To optimize worker shifts and warehouse layout, we apply Factorization Machine Quantum Annealing (FMQA), achieving a 37.4% reduction in lead times and a 14.3% decrease in labor hours. A visualization tool enables warehouse operators to make data-driven decisions. This research demonstrates the potential of digital transformation in logistics and provides a scalable framework for broader industry adoption.

As global demand for logistics continues to grow, improving the efficiency of warehouse operations has become increasingly important. While many processes in logistics warehouses have been automated, the receiving area still relies heavily on manual work. Therefore, optimizing this area is essential. When optimizing operations in a logistics warehouse, various problems must be considered, such as layout design and worker assignment. Previous research has typically focused on these problems individually. However, because they are closely related, jointly optimizing them can further improve overall efficiency. In this study, we focus on the receiving area and conduct joint optimization of layout design and worker assignment. Specifically, we extend and apply a black-box optimization method called FMQA, which combines Factorization Machines (FM) with Quantum Annealing (QA). By using Factorization Machine regression, we build an objective function from simulator data. We then use Quantum Annealing to minimize it and improve the receiving area. A comparison with an actual warehouse environment, using the multi-agent simulator, shows that our approach can reduce mean package processing time by up to 22.1%. This result demonstrates the effectiveness of joint optimization of layout design and worker assignment.

Clustering people by their life patterns is valuable in government and business fields. Existing studies often rely on semantic data such as Point of Interest or stay purpose. However, they have the problem that obtaining large datasets is difficult due to the need for annotation work. Some studies try to use only location data. However, they do not reveal the semantics of the area where visitors stay because they only label visited areas by significance according to duration and frequency of stay. In this paper, we propose a framework, LPSeL, for clustering people's Life Patterns at a Semantic Level using only raw GPS location data. LPSeL is based on the idea that analyzing human mobility first requires understanding urban space. Therefore, it begins with area modeling, which models areas in a city based on people's activities. Then, treating human mobility as a sequence of area representations makes it possible to model individuals by semantic-level characteristics of their life patterns. We showed that LPSeL is capable of estimating people's attributes from their life patterns using a real-world dataset consisting of GPS data collected from tens of thousands of smartphone users.

Understanding the characteristics of various urban areas is crucial for applications such as urban planning, tourism policies, market analysis, and infection control. Techniques for embedding areas as vectors in a latent space based on human mobility patterns are actively researched. Many of these area embedding methods define areas as points, grids, or polygons on a geospatial plane and then embed them. However, existing methods do not allow for mutual transformation between these forms and sizes after the initial embedding. Additionally, if the characteristics of an area change due to events such as the opening of new buildings, re-embedding is necessary. Meanwhile, the Word2Vec technique, a representative word embedding method, has a property called additive compositionality. This property allows for the arithmetic operation of word meanings through the arithmetic operations of word embeddings. In this paper, we propose a method to apply this property to existing area embedding techniques, leveraging it for practical tasks such as area shape transformation and searching for areas with trends change.

We present an assisting system for creating a ceiling plan. Conventional methods of creating a ceiling plan are time-consuming and high-cost. Our system requires only two inputs from a user and outputs the panoramic ceiling image that shows the whole ceiling surface. The system detects the ceiling fixtures and depicts them seamlessly for a reliable resulting image. We confirmed the possibility of assisting in creating a ceiling plan with our system through the experiment.

Spatio-temporal data is utilized in various fields, but its scale is continuously growing, leading to significant labor and costs in storage and processing. Therefore, the value that can be derived from spatio-temporal data is diluted due to management costs. We propose a new data management flow using various metadata and common programs for spatio-temporal data utilization. Traditionally, various spatio-temporal data processing have been implemented and processed according to each spatio-temporal data. We defined spatio-temporal data structure metadata and performed data processing based on metadata using a common data processing program. Furthermore, we automated the generation of data structure metadata by combining our data skeleton recognition method and generative AI model. Using this flow, we expect to improve the sustainability of utilizing spatio-temporal data.

As global demand for logistics continues to grow, optimizing the automation and efficiency of distribution warehouse operations is of paramount importance. Digitalizing warehouse environments, which refers to the process of sensing the physical space and extracting meaningful information from the obtained data, offers a promising solution to this challenge. However, converting raw warehouse data, such as video footage captured inside the warehouse, into actionable metadata (e.g., tracking the movement paths of workers and products or analyzing the usage patterns of different warehouse locations) often necessitates significant human intervention. The rise of machine learning further complicates this, as it requires the manual preparation of extensive training datasets. In this paper, we introduce a framework that semi-automates the digitalization process in complex warehouse settings. This framework employs dense optical flow and representation learning to autonomously segment warehouse objects and cluster similar objects, thereby substantially cutting down on annotation costs. To evaluate our approach, we constructed a large-scale data collection platform with over 60 fixed cameras in a real-world logistics warehouse, and the video data from this platform was then applied to our framework. Our evaluations indicate that our method markedly reduces both the time and resources required for warehouse digitalization using the captured video data.

A low-code platform is a software development environment that allows for the creation of applications through graphical user interfaces and configuration instead of traditional hand-coded computer programming. In this study, the application to classify a dataset of traffic sign images using the KNIME low-code deep learning development platform will be discussed to represents this software performance especial in term of model optimization processes. By creates the workflow to perform image preprocessing, create the CNN layer under KERAS sequential API and finding the best set of key hyperparameters among traditional KNIME build-in optimization algorithm including Brute force, Hill climbing, random search, Bayesian Optimization and black-box optimizer Optuna optimization algorithm under 3 types CNN architecture as simple CNN, Resnet-50 and VGG16 to classify traffic sign images. The result demonstrates that both grid search and random search optimization can be effective, while both Optuna and Bayesian optimization stands out as a powerful method due to its ability to efficiently explore the hyperparameter space and achieve superior results to meet 99% accuracy under simple CNN environment, but Optuna is significantly improve optimization times than Bayesian about 7 - 8 times. The KNIME low-code platform provides a user-friendly environment for developing and fine-tuning models to contribute the ongoing progress in machine learning and deep learning research development.

Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness, which are greatly preferred by humans.

Human-robot collaboration and cooperation are critical for Autonomous Mobile Robots (AMRs) in order to use them in indoor environments, such as offices, hospitals, libraries, schools, factories, and warehouses. Since a long transition period might be required to fully automate such facilities, we have to deploy AMRs while improving safety in the mixed environments of human and mobile robots. In addition, human behaviors in such environments might be difficult to predict. In this paper, we present a Digital Twin for Autonomous Mobile Robots system named DigiMobot to support, manage, monitor, and validate AMRs in indoor environments. First, DigiMobot can simulate human behaviors and robot movements to verify and validate AMRs to improve safety in a virtual world. Secondly, DigiMobot can monitor and manage AMRs in the physical world by collecting sensor data from each robot in real-time. Since DigiMobot enables us to test the robot systems in the virtual world, we can deploy and implement AMRs in each facility without any modifications. To show the feasibility of DigiMobot, we develop a software framework and two different types of autonomous mobile robots. Finally, we conduct real-world experiments in a warehouse located in Saitama, Japan, in which more than 400, 000 items are stored for commercial purposes.

Contagious diseases like COVID-19 spread periodically and threaten our lives. Self-protection, such as washing hands, wearing a mask, and staying home, are simple and practical solutions to safeguard against these diseases. Most governments and health departments recommend that people maintain self-protection. Although continuous self-protection effectively prevents the spread of infection, only the intent to self-protect is unsustainable in the long term. In this study, we design, develop, and deploy an application to track users' daily activities semi-automatically and enhance self-protection behavior using mobile sensing and gamified feedback techniques. Currently, more than 324 people have installed the app via AppStore, and 52 users have shared their activity data to our research group.

In this paper, LSTM-based neural network is applied to indoor localization using mobile BLE tag's signal strength collected by multiple scanners. Stability of signal strength is a critical factor of wireless indoor localization for higher accuracy. While traditional methods like trilateration and fingerprinting suffer from noise and packet loss, deep learning based methods perform well. We focus on large-scale exhibition where wireless signal gets unstable due to many people. Proposed neural network consists of fully connected layers for noise removal and LSTM layers for time-series feature extraction. The network takes the time-series of signal strength as input and outputs the estimated location. In the evaluation, the number of layers is changed to find the optimal structure. As a result, the best configuration achieved the error of 2.44m at 75 percentile for the data of a large-scale exhibition in Tokyo.

We propose an integrated framework for sensing, recognizing and utilizing of subjective time as context. Various studies on experimental psychology have showed several factors which affects subjective time. Those factors should be partially captured by ubiquitous sensors such as smartphones and wearable devices, therefore, we tackle to create common and individual model for subjective time based on the sensor data. We report our first prototype implementation for the framework based on AWARE framework with adding experience sampling method for subjective time recognition. In addition, we discuss potential applications which leveraging advantages of subjective time as context.

This paper proposes an accurate estimation method of walking speed using deep learning for smartphone-based Pedestrian Dead Reckoning (PDR).PDR requires to estimate speed and direction of pedestrians accurately using accelerometer and gyroscope.To improve the accuracy of PDR, existing works focused to improve the key factors of speed estimation (i.e., stride and/or step estimation) by adapting deep learning.On the contrary, our research proposes to adapt deep learning more directly to estimate walking speed from sensor data of smartphone. We evaluate the accuracy of proposed method by comparing with conventional PDR method. As a result, we confirmed that proposed method can estimate the speed more accurately.

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.

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.

Can we inspect the road condition at a low cost? City infrastructures, such as roads are very important for citizens to their city lives. Roads require constant inspection and repair due to deterioration, but it is expensive to do so with manual labor. Meanwhile, there are official city vehicles, especially garbage trucks that run through the entire area of a city every day and have cameras to record their driving. When we use these cameras, we can watch roads conditions anytime, anywhere. In our study, we focus on these cameras and attempt detecting the road damage, such as road marking blur. To achieve our goal, we explore the new system in this paper. This system adopts the object detection approach that is end-to-end learning and based on deep neural networks, which propose the blur region candidate and detect whether the road markings are blurred or not all at once. In our experiment, first, we obtain the drive recorder video from sanitation engineer and then annotate them. After annotation, we trained our models and calculate the mean average precision to evaluate our models. As a result, our model performs on our collected dataset.

Collecting urban data in a citywide scale plays a fundamental role in the research, development and implementation of smart cities. This demo introduces Cruisers, an automotive sensing platform for smart cities, which is developed based on the following ideas. a) Garbage collecting trucks are used as host automobiles to accommodate sensors, b) 3G cellular communication network is used to wirelessly deliver sensed data directly to servers, and c) Proxy server(s) are adopted to convert the format of sensed data to required ones. This platform has been deployed to 24 garbage collecting trucks at Fujisawa city, i.e., nearly 1/4 of the total number of such trucks in the city. An iOS application is also developed to demonstrate the sensing process and the covered area.

Recent technological trends in mobile/wearable devices and sensors have been enabling an increasing number of people to collect and store their "lifelog" easily in their daily lives. Beyond exercise behavior change of individual users, our research focus is on the behavior change of teams, based on lifelogging technologies and lifelog sharing. In this paper, we propose and evaluate six different types of lifelog sharing models among team members for their exercise promotion, leveraging the concepts of "competition" and "collaboration." According to our experimental mobile web application for exercise promotion and an extensive user study conducted with a total of 64 participants over a period of three weeks, the model with a "competition" technique resulted in the most effective performance for competitive teams, such as sports teams.

Recent technological trends on mobile/wearable devices and sensors have been enabling increasing number of people to collect and store their “life-logs” easily in their daily lives. Beyond exercise behavior change of individual user, our research focus is on the behavior change of teams, based on life-logging technologies and information sharing. In this paper, we propose and evaluate six different types of information sharing model among team members for their exercise promotion, leveraging concepts of “competition” and “collaboration”. According to our experimental mobile web application for exercise promotion and extensive user study among 64 total users for three weeks, the model with “external competition” technique resulted the most effective performance for competitive teams such as sport teams.

We propose a novel technique that aggregates multiple sensor streams generated by totally different types of sensors into a visually enhanced video stream. This paper shows major features of SENSeTREAM and demonstrates enhancement of user experience in an online live music event. Since SENSeTREAM is a video stream with sensor values encoded in a two-dimensional graphical code, it can transmit multiple sensor data streams while maintaining their synchronization. A SENSeTREAM can be transmitted via existing live streaming services, and can be saved into existing video archive services. We have implemented a prototype SENSeTREAM generator and deployed it to an online live music event. Through the pilot study, we confirmed that SENSeTREAM works with popular streaming services, and provide a new media experience for live performances. We also indicate future direction for establishing visual stream aggregation and its applications.

Deploying Wireless Sensor Networks (WSN) securely still requires users to have certain skills and exert effort. In the near "sensor everywhere" future, a much simpler method for deploying WSN will be necessary for end-users. We propose LiDSN(Light Communication for Deploying Secure Wireless Sensor Networks) which enables users to achieve deployment tasks via simple interaction. LiDSN leverages light-based communication between an LED and a light sensor in order to add a new sensor node securely into existing WSN. Through touching interaction, a new sensor node ID and secret key can be transmitted to the WSN, and then the WSN is able to identify which node should be added while maintaining the security of the WSN.

This paper proposes and evaluates a method to detect and classify tweets that are triggered by places where users locate. Recently, many related works address to detect real world events from social media such as Twitter. However, geotagged tweets often contain noise, which means tweets which are not content-wise related to users' location. This noise is problem for detecting real world events. To address and solve the problem, we define the Place-Triggered Geotagged Tweet, meaning tweets which have both geotag and content-based relation to users' location. We designed and implemented a keyword-based matching technique to detect and classify place-triggered geotagged tweets. We evaluated the performance of our method against a ground truth provided by 18 human classifiers, and achieved 82% accuracy. Additionally, we also present two example applications for visualizing place-triggered geotagged tweets.

Recent progress in information technology enables people to easily broadcast events live on the Internet. Although the advantage of the Internet is live communication between a performer and listeners, the current mode of communication is writing comments using Twitter or Facebook, or some similar messaging network. In one type of live broadcast, musical performances, it is difficult for a musician, when playing an instrument, to communicate with listeners by writing comments. We propose a new communication mode between performers who play musical instruments, and their listeners by enabling listeners to control the performer's camera or illumination remotely. The results of four weeks of experiment confirm the emergence of nonverbal communication between a performer and listeners, and among listeners, which increases camaraderie amongst listeners and performers. Additionally, the dramatic impact of a performance is increased by enabling listeners to control various camera actions such as zoom-in or pan in real time. The results also provide implications for design of future interactive live broadcasting services.

This paper proposes the information access method that is based on the distance between users and objects. In Addition, demonstrate Lupe system, which visualizes WSN status information utilizing our method. The evaluative experiment shows that our method is useful in where a number of sensors are setup. As a result our method and Lupe system enable to easily brows WSN status information for end-user.

We propose a simple interaction to transfer information on smart phone to laptop/tablet PCs. We often encounter the situation that we need to send URL, which is preliminary accessed in mobile devices, from mobile devices to personal computers (PCs) to see the web page with wider screen. To support this information transfer, we utilize combination between vibrator in smart phones and accelerometer in laptop/tablet PCs. URL information is encoded to vibration patterns, and the patterns are detected and decoded by accelerometer in PCs. We demonstrate the interaction's efficiency and reasonability with actual products.

The paper proposes an intuitive device selecting interface called ``Vib-connect'' for device collaboration. Recent progress in Information Technology allowed various devices to join wireless network. As a result, various ways of device collaborations and services became possible. However, interfaces for selecting devices are still complicated for end-users and far from being intuitive. To solve this problem, we propose ``vib-connect'', an interface which enables users to select device intuitively by pasting a vib-connector, a small vibration-based device. Vib-connect solves these problem by implementing these device information as a unique vibration pattern. By keeping smart-phones or any kind of vibration generating device, end-users can easily select devices to collaborate. We implement a prototype, and through the evaluation, we confirmed high accuracy in vibration pattern detection and high usability satisfaction by non-expert users. By using "Vib-connect", users without any technical expertize can easily select devices to collaborate with.

The number of elders who encounter falling accidents has been increasing in the past few decades. Falling accidents could cause major injuries, such as having bruise, breaking bone, and in the worst case, losing life. Therefore, preventing elders from falling accidents is important in order to ensu