Takuya Maekawa

This paper proposes an activity recognition method that models an end user's activities without using any labeled/unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user's physical characteristics such as height and gender to find other users whose sensor data prepared in advance may be similar to those of the end user. Then, we model the end user's activities by using the labeled sensor data from the similar users. Therefore, our method does

In a line production system of a factory, a worker repetitively performs predefined operation processes. This paper tries to recognize work by factory workers in an unsupervised manner. Specifically, we propose an unsupervised measurement method for estimating lead time (duration) of each period of an operation process using a wrist-worn accelerometer because the lead time greatly affects productivity of the line production system. Our proposed method automatically finds a frequent sensor data s

A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attentio

The in vitro actions of recombinant human leukemia inhibitory factor (LIF) were studied on the human leukemia cell lines HL60 and U937. Parameters analyzed were the suppression of stem cell generation using sequential clonal cultures, alterations of surface antigen expression, and morphological changes. When acting alone, LIF had no observable effects on the number, size, or morphology of colonies formed by HL60 or U937 cells, surface phenotype expression, or recloning capacity of cells of eithe

In mobile computing environments, handheld devices with low functionality restrict the services provided for mobile users. We propose a new concept of collaborative browsing, where mobile users collaboratively browse Web pages designed for desktop PC. In collaborative browsing, a Web page is divided into multiple components, and each is distributed to a different device. In mobile computing environments, the number of handheld devices, their capabilities, and other conditions can vary widely amo

It is difficult for users of mobile devices such as cellular phones equipped with a small screen and a poor input interface to browse Web pages designed for desktop PCs with large displays. Many studies and commercial products have tried to solve this problem. Web pages include images that have various roles such as site menus, line headers for itemization, and page titles. However, most studies of mobile Web browsing haven't paid much attention to the roles of Web images. In this paper, we defi

This demo paper describes our daily activity sensing and recognition system with a wrist-worn sensor device called WristSense. The wrist-worn device is equipped with an accelerometer and camera, and can send the sensor data to a Bluetooth-enabled smart phone. With the accelerometer, we capture the wearer's hand postures and hand movements. With the camera, we capture visual information related to an object that the wearer is holding. An object that the wearer is using relates strongly to the act

The object-blog service application automatically converts raw sensor data to environment-generated content (EGC), including texts, graphs, and figures. This conversion facilitates data searching and browsing. Generated content can serve several purposes, including memory aids, security, and communication media. In object-blog, personified objects automatically post entries to a Weblog about sensor data obtained from sensors attached to the objects. Feedback thus far from participants working wi

Abstract Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification incl