This paper presents an indoor pedestrian navigation solution relying on motion recognition in an office environment utilizing the existing WLAN infrastructure.2.?MotivationRelated research indicates that utilizing opportunistic selleck inhibitor signals of, e.g., WLAN, is an efficient locating alternative in GPS-denied environments. However, in order to minimize a smartphone’s battery drain, the WLAN scanning interval is always limited. For instance, most of the Nokia mobile phones refresh the scanned WLAN information proximately every 8�C10 s. The default scanning interval of most Android devices is 15 s. On the other hand, other built-in sensors such as accelerometers are always turned on, in order that the physical orientation of the smartphone is always known to the system.
These sensors provide an alternative for positioning while WLAN positioning is unavailable.During the gaps where no wireless signal is updated, the most essential elements for navigation are the movement speed and orientation (i.e., heading). As long as they are determined, it is possible to estimate the position of the user every second using dead-reckoning. Therefore, this paper presents a method to use the built-in tri-axial accelerometer and magnetometer on a smartphone to recognize the user’s movement parameters. The proposed solution detects the physical movements using simple acceleration and orientation features throughout the navigation process. With the recognized motions, it is possible to reasonably estimate the speed and position over the period between wireless scans.
Human motion has been widely studied for decades, especially in recent years using computer vision technology. Poppe gives an overview of vision based human motion analysis in [30]. Aside from vision-based solutions, sensor-based approaches are also extensively adopted in biomedical systems [31�C34]. Most of the previous motion recognition related research assumed that the Micro-Electro-Mechanical Systems (MEMS) inertial sensors used are fixed on a human body [35�C38] (e.g., in a pocket, clipped to a belt or on a lanyard) and that an inference model can be trained according to a handful of body positions. Some of them use phones as a sensor to collect activities for off-line analysis purposes [39].
Compared to the daily activities, such as ��Sitting��, ��Walking��, ��Running��, ��Jumping��, the motions of a pedestrian who is using a smartphone for navigation in three-dimensional indoor structures are far more complicated due to the arbitrary gestures while a phone is kept in hand. Hence this paper primarily focuses on the possible motion states of a user with a phone in hand while navigating. References [28] and [29] have briefly presented the preliminary results of our previous research in this topic.3.?Motion StatesUnlike Anacetrapib the solution with sensors fixed Volasertib cancer on the body, a smartphone in hand has more degrees of freedom (DOF) during the navigation process.