Katsuhiko Kaji

Multi-algorithm on-site evaluation system for PDR challenge

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: a PDR algorithm category; a PDR Evaluation method category; and an exhibition. In this paper, we especially focus on several systems for the PDR algorithm category. A PDR skeleton was prepared for the participants. Using an Android skeleton, participants focus on implementing the PDR algorithm because of the skeletons various functions, such as sensor data acquisition, trajectory visualization, and sensor data upload. The evaluation server evaluates the accuracy of each PDR algorithm automatically as often as sensor data is uploaded to the server and provides a trajectory image file so that participants can compare their PDR algorithms in real time.

PIEM: Path Independent Evaluation Metric for Relative Localization

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 a problem caused by an accumulated error: the longer the path, the worse the accuracy of relative localization. There is another problem in the existing evaluating metrics: they compare only the actual location and the estimated location of the destination. Relative localization also has this evaluation problem. We propose PIEM: Path Independent Evaluation Metric for Relative Localization. PIEM is a path independent evaluation metric, considering the complexity of the path; distance, orientation, and height. Then we evaluate these three factors of relative localization in addition to the position. Our proposed method showed more consistent results for the complexity of the path than the existing methods of relative localization evaluation.

HASC-PAC2016: large scale human pedestrian activity corpus and its baseline recognition

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 activity recognition. In addition to HASC Challenge, we collected indoor pedestrian sensing data of 107 people with a balance of gender and age (HASC-IPSC). Through these data collection, we gathered 111,968 sensor files of 510 subjects. For the convenience of the future researchers in this field, we combined them as a single corpus named HASC-PAC2016 and make it public. Baseline recognition result of HASC-PAC2016 segmented data is 73.4% accuracy for overall, 81.4% for limited by terminal position, and 85.1% with file-based recognition. For sequence data, we only get 73.4% even for limited subjects. This shows we need further research of activity recognition using HASC-PAC2016.

Estimating 3D pedestrian trajectories using stability of sensing signal

A highly accurate estimation method of 3-D pedestrian trajectories from walking activity sensing data is proposed. This method uses data from an accelerometer, a gyrometer, and an air pressure sensor, and does not require detailed information on the building structure. In activity sensing using wearable sensors, higher accuracy can be expected from detection of zones in which there is continuously little change in the states of the sensor signals than from detection of zones in which there are large changes in the sensor signals within a short time. We focus on such stability of sensing signal and, as an application example, we used the concept to estimate walking direction on the basis of stable walking zone detection using a gyrometer and estimation of movement between the floors of a building by detection of stable floor zones with an air pressure sensor. We then integrated these estimations to obtain a 3-D pedestrian trajectory. The results of evaluation experiments using an indoor pedestrian sensing corpus (HASC-IPSC) showed that this method achieved higher accuracy for both walking direction and movement between floors than was achieved by a method based on large changes in the sensor signals. We also confirmed that the cumulative error rate for estimation of the 3-D pedestrian trajectory was 1 m per 10 seconds of movement.

A pedestrian passage detection method by using spinning magnets on corridors

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 detection methods by using magnets. We make the characteristic magnetic field as a marker. We detect the pedestrian passage by reading a marker by a smartphone device. We generate a magnetic field marker by rotating magnets. We can acquire a passage direction by rotating two magnets at different frequencies. We can detect a passage with an accuracy rate of 100 percent and a passage direction with an accuracy rate of 94 percent when the distance between magnets and a smartphone is less than 75cm.

A proposal of IndoorGML extended data model for pedestrian-oriented voice navigation system

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, at first, we conduct an experiment of voice navigation at an underground shopping area of Nagoya Station with two types of landmark descriptions. After that, we discuss what data structure is necessary to describe landmark information for voice navigation. Therefore, we propose to extend IndoorGML1.0 by adding landmark space as a new defined data model for voice navigation. The main contribution of this paper is that we conduct an experiment of voice navigation and research how different landmark descriptions affect users; furthermore, we discuss a IndoorGML extended data model for voice navigation.

Estimation of Person Existence in Room Using BLE Beacon and Its Platform

In this research, estimation of person existence in room using BLE beacon and that propose application method. Create a platform for that. For estimating of person existence, BLE beacons are used as individual identifiers. Install a receiver in the room. By doing this, we can estimation with high degrees of freedom not dependent on Devices. Acquire Information on estimation of person from the platform we created. Based on this information, we disclose real-time person’s stay information and person’s stay information history. We also propose applications such as notification systems and visit promotion systems.

Making Pier Data Broader and Deeper

Big data can be gathered on a daily basis, but it has issues on its quality and variety. On the other hand, deep data is obtained in some special conditions such as in a lab or in a field with edge-heavy devices. It compensates for the above issues of big data, and also it can be training data for machine learning. Just like a platform of pier supported by stakes, there is structure in which big data is supported by deep data. That is why we call the combination of big and deep data “pier data.” By making pier data broader and deeper, it becomes much easier to understand what is happening in the real world and also to realize Kaizen and innovation. We introduce two examples of activities on making pier data broader and deeper. First, we outline “PDR Challenge in Warehouse Picking”; a PDR (Pedestrian Dead Reckoning) performance competition which is very useful for gathering big data on behavior. Next, we discuss methodologies of how to gather and utilize pier data in “Virtual Mapping Party” which realizes map-content creation at any time and from anywhere to support navigation services for visually impaired individuals.

A location estimation method using mobile BLE tags with tandem scanners

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 some advantages: to collect a large amount of data easily, to provide location estimation without smart-phones. However, in a real environment, the BLE signal is unstable due to many people and obstacles. Fingerprinting is difficult because arranging booths finishes few hours before exhibition starts. Our previous trilateration method resulted in 10--30 meter accuracy with the data collected at Geospatial EXPO 2015 because of packet loss. We, to decrease packet loss, have developed tandem scanner that is equipped with multiple Bluetooth adapters. This paper presents the result of the improved method with the data collected by tandem scanners at Geospatial EXPO 2016. We also examined what improves accuracy: shorter advertising interval of the BLE tag and a larger number of tandem scanners available adapters. Average location estimation accuracy was 4.51 meters when using best settings.

Compensation scheme for PDR using sparse location and error model

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 sparse locations. Sparse locations mean discontinuous locations obtained by using absolute localization method or passage detection devices (ex. BLE, Magnetic Field). We attempt to compensate the trajectory of PDR estimation and improve estimation precision by sparse locations. Then we tune parameters describing errors. In our scheme, we define error models that represent errors in PDR, including moving distance error and orientation changing error. Moreover, we define parameters to describe these errors. As a result, proposed scheme improved position error rate approximately 10% and route distance error rate approximately 7%.