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Numerous studies have been conducted on indoor and outdoor seamless positioning and indoor–outdoor detection methods. However, the classification of real space into two types, outdoor space and indoor space, is difficult. One type of space that is difficult to classify is top-bounded space, which can be observed in commercial facilities, logistics facilities, and street-facing sidewalks. In this study, we designed a method for detecting stays in three spatial contexts: Outdoor, top-bounded space, and indoor. This method considers elongated top-bounded spaces covered with a roof and open on one of the sides. Specifically, we selected Global Positioning System (GPS) satellites for stay detection based on the simple extraction of the spatial characteristics of a top-bounded space and designed a decision flow using fuzzy inference based on the signal-to-noise ratio (SNR) of the selected GPS satellites. Moreover, we conducted an evaluation experiment to verify the effectiveness of the proposed method and confirmed that it could correctly detect the stay in three spatial contexts, outdoor, top-bounded space, and indoor, with a high probability of 93.1%.

ABSTRACT:This study presents a method for analyzing workers' behavior using indoor positioning technology and field test in the workplace. Recently, various indoor positioning methods, such as Wi-Fi, Bluetooth low energy (BLE), visible light communication, Japan's indoor messaging system, ultra-wide band (UWB), and pedestrian dead reckoning (PDR), have been investigated. The development of these technologies allows tracking of movement of both people and/or goods in indoor spaces, people and/or goods behavior analysis is expected as one of the key technologies for operation optimization. However, when we use these technologies for human tracking, there are some problem as follows. 1) Many cases need to use dedicated facilities (e.g. UWB). 2) When we use smartphone as sensing device, battery depletion is one of the big problem (especially using PDR). 3) the accuracy is instability for tracking (e.g. Wi-Fi). Based on these matters, in this study we designed and developed an indoor positioning system using BLE positioning. And, we adopted smartphone for business use as sensing device, developed a smartphone application runs on android OS. Moreover, we conducted the field test of developed system at Itoki Corporation's ITOKI Tokyo Innovation Center, SYNQA, office (Tokyo, Japan). Over 40 workers participated in this field test, and worker tracking log data were collected for 6 weeks. We analyzed the characteristics of the workers' behavior using this log data as a prototyping.

The semi-outdoor space covered with the upper part and open on the side is difficult to apply the existing indoor-outdoor seamless methods because the GPS positioning accuracy is reduced but some degree of GPS signal can be received. In order to detect a stay in such a special environment, we designed a method to select satellites for stay detection based on the spatial characteristics of the semioutdoor space, which consists of three parameters: height, depth and side opening direction. Furthermore, since it is difficult to set a specific threshold for stay detection, we designed a decision flow to determine the stay by using fuzzy inference based on the values of signal-to-noise ratio of the selected satellites. Using this method, we confirmed that we could detect the stay in the semi-outdoor space covered by the upper part of the space with 89.4% probability at three evaluation experimental locations. キーワード GPS, 信号対雑音比, ファジィ推論，半屋外空間