Kei Hiroi

• PhD.
• Professor (Associate) at Kyoto University

In the last two decades, researchers have investigated dance performance using activity recognition methods. However, most of the works did not focus on dance figures. Learning ballroom dance figures, a completed set of footsteps, makes it difficult for less experienced dancers. Hence, the paper presents a classification method of ballroom dance fi...

The paper presents a ballroom dance figure classification method with LSTM using video and wearable sensors. Ballroom dance is a popular sport among people regardless of age or sex. However, learning ballroom dance is very difficult for less experienced dancers as it has many complex types of “dance figures”, which is a completed set of footsteps....

This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series...

Bus transportation service is more influenced than other public transport by various factors such as traffic congestion, weather condition, number of passengers, traffic signals. These factors often cause delay and the users may feel inconvenience while waiting at the bus stop. In the case of snowfall event, a large delay occurs, which greatly redu...

There is an inherent problem of error accumulation in Pedestrian Dead Reckoning (PDR). In this chapter, we introduce a PDR error compensation scheme based on the assumption that can obtain sparse locations. Sparse locations are discontinuous locations obtained by using an absolute localization method or passage detection devices (ex. RFID tag, BLE...

This demonstration shows an interactive urban flood damage prediction system "ARIA" that simulates urban flood, the sufferer, and network failure in an integrated manner. In terms of disaster mitigation, it is important to confirm an affected area and issue an evacuation advisory. ARIA predicts flood damages - the number of sufferers or the locatio...

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpuses and much improved methods to recognize activities and the context in which they occur. This workshop deals w...

Bus transportation service is more strongly influenced than other public transport modalities by various factors such as traffic congestion, weather conditions, number of passengers, and traffic signals. These factors often cause delays, and users may feel inconvenienced when waiting at a bus stop. Few studies have analyzed the relationship between...

Public transport bus service is an important means of transportation for commuting, schooling and daily life. However, many unpredictable problems arise, resulting in delays caused by traffic congestion or an increased number of passengers. Changing the operation schedule may alleviate these problems; however, determining the optimal schedule chang...

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 commo...

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....

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, de...

Inspection and repair of road infrastructures are important for safety. While highways and motorways are periodically inspected with specialized vehicles, the roads which are maintained by local governments are not inspected because of lack of budget and workforce. In the future, however, a large number of autonomous driving cars will run everywher...

In recent years, the importance of location information has increased due to the popularization of terminals such as smartphones. Our purpose is to estimate the 3D position of smartphones within several centimeters. This location information can reveal a person's behavior patterns and subject of interest. A method based on dynamic magnetism can est...

The bases of the approaches of UCLab(submission 1) towards SHL recognition challenge are using Random Forest and letting it select important features. Using accelerometer, gyroscope, magnetometer, gravity and pressure sensor as input data, features such as mean, variance, max, difference of max and min, and main frequency are calculated. We find th...

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpuses and much improved methods to recognize activities and the context in which they occur. This workshop deals w...

For high precision estimation with SHL recognition challenge, we use a deep learning framework based on convolutional layers and LSTM recurrent units (ConvLSTM). We, UCLab(submission 2), propose the model combined two different ConvLSTMs. One ConvLSTM of convolution layers has large kernel size and the other has small kernel size. We expect that th...

We have developed an indoor location estimation method using mobile Bluetooth Low Energy (BLE) tags carried by people and BLE scanners fixed to a building. By using the method, we can analyze the behavior of the attendees at some large-scale exhibition, such as the order of the visited booth and the duration of the stay. Using mobile BLE tags has s...

One of the indoor localization methods utilizing accelerometer and gyroscope is called PDR (Pedestrian Dead Reckoning). Various schemes have been proposed in PDR, however, sufficient precision has not been achieved because of the error accumulation. In this research, we propose a PDR error compensation scheme based on an assumption that can obtain...

Nearby event data, such as those for exhibitions and sales promotions, may help users spend their free time more efficiently. However, most event data are hidden in millions of webpages, which is very time-consuming for a user to find such data. To address this issue, we use web mining that extracts event data from webpages. In this paper, we propo...

Techniques for obtaining customers' behavior (dwell time, count, and flow) in a shopping mall or large exhibit are highly sought-after by organizers or shop owners. Additionally, ways of effectively directing customers from cyber space such as the Web or smartphone apps to physical retail stores are also in high demand. "Online to Offline (O2O) Mar...

Indoor location estimation is essential technology when we analyse the participants' activities in large-scale exhibition. There are some problems with existing methods such as PDR, ultrasound and laser range finder: installation location of measurement equipment at large site, cost for measurement equipment, and necessity of smartphone application...

There are many methods for indoor positioning. These methods are divided into the relative localization and absolute localization. In the relative localization, one widely used method is Pedestrian Dead Reckoning (PDR). Relative localization estimates the moving distance, orientation, and height of the pedestrian. However, relative localization has...

Some people cannot effectively utilize pedestrian navigation systems due to their limited spatial ability. To provide guidance in consideration of an individual's spatial ability, measuring spatial ability is necessary. In this paper, we propose measurement methods for spatial ability using a virtual reality system. We studied spatial ability in te...

PDR (Pedestrian Dead Reckoning) is a very promising technology for indoor positioning. We held a technical challenge, entitled the UbiComp/ISWC 2015 PDR Challenge, consisting of the following three categories: Algorithm, Evaluation, and Exhibition. In this paper, we specially focus on collected data for PDR algorithm category. UbiComp/ISWC particip...

Human activity recognition by wearable sensors will enable a next-generation human-oriented ubiquitous computing. However, most of the existing research on human activity recognition is based on a small number of subjects, and lab-created-data. To overcome this problem, we hold HASC Challenge as a technical challenge to collect the data for activit...

We present a scheme that improves accuracy of 2.4GHz RF tag based indoor positioning. The accuracy of indoor positioning using 2.4GHz RF tags is affected by propagation loss caused by human body shielding, especially in crowded situations. This paper proposes a RSSI compensation scheme that estimate a crowd density level based on detected 2.4 GHz R...

We propose Landmark-Conscious Voice Navigation as one type of a pedestrian navigation system, which navigate users by only voice guidance. It is necessary to standardize data model in order to use this system widely. In a previous paper[1], we constructed a basic voice navigation system, which uses Open Street Map based data model. In this paper, a...

In this work, we implement a mobile system called Velobug to measures 3D data of the environment. Velobug could generate dense and colored point cloud to reconstruct the environment for 3D mapping with long effective range. Velobug is mainly consisted of a Velodyne HDL-32e LiDAR senor and a Point Grey Research Ladybug3 panoramic camera. The LiDAR s...

Since the advent of smartphones equipped with sophisticated sensing hardware, human activity recognition research has moved from utilizing dedicated sensing devices to using commercial smartphones. This paper presents the design of an algorithm to recognize and estimate travel distance when riding an elevator and its corresponding implementation wi...

The passage event on the specific spot is one of the useful information for position estimate. If we can detect the passage of the specific spot, we could contribute to the field of the position estimate because it is available for movement course identification, and the correction of the position estimate error. We suggest pedestrian passage detec...

In this paper, we propose a high accuracy indoor positioning method that uses residual magnetism in addition to Pedestrian Dead Reckoning (PDR) and WiFi-based localization methods. Our proposed method needs WiFi and magnetic field fingerprints, which are created by measuring in advance the WiFi radio waves and the magnetic field in the target map....

This research addresses improvement of the accuracy of pedestrian dead reckoning (PDR), which is one effective technique to estimate indoor positions using smartphone sensors. Even though various techniques using step lengths and their number have been previously proposed for PDR, insufficient accuracy is gotten from smartphone sensors. In this res...

In this research, we have designed and implemented an Event Information Summarization System (EISS) for collecting Event Info as a web-service. EISS collects mass event data from several non-uniform event website APIs and data sources. The Collected event data is visualized by some user-friendly user interfaces for consumer. EISS can summarize the...

In cuisine recommender service, online user review is an important data source avoiding a cold-start problem. Cuisine-domain named entity recognition(NER) can be used as an entrance to comprehend the semantic information of reviews. This paper describes a supervised approach recognizing Japanese dish name entity (DNE) from online reviews of Japanes...

In this study, we focus on the accurate and early prediction of Localized Heavy Rain (LHR) using multiple sensors. Traditional sensors, such as rain gauges and radar, cannot detect LHR until cumulonimbus clouds cover the sensors. In contrast, Surface Meteorological Monitoring Networks (SMMNs) can accurately measure rainfall in the vicinity of the s...

This research develops an Autonomous Distributed and Cooperative disaster information system based on the Internet technologies which distribute sensor information such as meteorological information. It is essential that disaster prevention systems collect and classify information or deliver to local residents rapidly. However, there are some probl...

We propose a method of in situ (in vitro) analysis of the chemical bonding state of DNA in aqueous solution by infrared absorption spectroscopy in the multiple internal reflection geometry (MIR-IRAS) in conjugation with electrophoresis. We demonstrate that MIR-IRAS is capable of detecting in vitro DNA hybridization. In our method, a Si prism, throu...