Browsing by Author "Lo, Chi-Chung"

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A Walking Velocity Update Technique for Pedestrian Dead-Reckoning Applications
(2011)
Lo, Chi-Chung
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Chiu, Chen-Pin
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Tseng, Yu-Chee
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Chang, Sheng-An
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Kuo, Lun-Chia
Inertial sensors for pedestrian dead-reckoning (PDR) have been attracting considerable attention recently. Since accelerometers are prone to the accumulation of errors, a "Zero Velocity Update" (Z-UPT) technique [1], [2] was proposed as a means to calibrate the velocity of pedestrians. However, these inertial sensors must be mounted on the bottom of the foot, resulting in excessive vibration and errors when measuring speed or orientation. This paper proposes a self-calibrating PDR solution using two inertial sensors in conjunction with a novel concept called "Walking Velocity Update" (W-UPT). One inertial sensor is mounted on the lower leg to identify a point suitable for calibrating the walking velocity of the user (when its pitch value becomes zero), while another sensor is mounted on the upper body to track the velocity and orientation. We have developed a working prototype and tested the proposed system using actual data.
Adaptive radio maps for pattern-matching localization via inter-beacon co-calibration
(2012-04-01)
Lo, Chi-Chung
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Hsu, Lan-Yin
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
A growing number of location-based applications are based on indoor positioning, and much of the research effort in this field has focused on the pattern-matching approach. This approach relies on comparing a pre-trained database (or radio map) with the received signal strength (RSS) of a mobile device. However, such methods are highly sensitive to environmental dynamics. A number of solutions based on added anchor points have been proposed to overcome this problem. This paper proposes an approach using existing beacons to measure the RSS from other beacons as a reference, which we call inter-beacon measurement, for the calibration of radio maps on the fly. This approach is feasible because most current beacons (such as Wi-Fi and ZigBee stations) have both transmitting and receiving capabilities. This approach would relieve the need for additional anchor points that deal with environmental dynamics. Simulation and experimental results are presented to verify our claims. (C) 2012 Elsevier B.V. All rights reserved.
Design of a Self-Portrait Application with Sensor-Assisted Guiding for Smart Devices
(2013)
Lo, Chi-Chung
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Huang, Sz-Pin
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Ren, Yi
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Tseng, Yu-Chee
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資訊工程學系
Taking self-portrait on a smart device, such as a smart phone and a digital camera, is a lot of fun and can be easy when we know how. However, there are rare cases that we can get satisfied snapshots at our first try. Furthermore, the snapshots are usually stored on the camera. The user would not able to check the snapshots immediately on the remote side since she stands away the camera. In this paper, we propose a self-portrait application which enables a smart device to prevent faces from being cut out of the camera frame by giving suggestions to users until they are in a suitable position in the frame. In the same time, we enable the smart device to share the photos automatically to the remote device by machine-to-machine technique. The remote device can also control the camera to take photos again. The proposed application has been implemented on an Android smart phone.
Pedestrian Activity Detection in a Multi-Floor Environment by a Smart Phone
(2012)
Lo, Chi-Chung
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Chen, Yi-Hsiu
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Tseng, Yu-Chee
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Huang, Shang-Ming
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Hung, Yu-Neng
In door localization has attracted considerable attention recently. One approach is to use inertial sensors mounted on pedestrians to characterize the users' motions. However, few studies have focused on a multi floor environment where users' activities may include walking, running, and going up/down stairs. This paper proposes a lightweight activity detection system using inertial sensors on a smart phone to detect the behaviors of a pedestrian in the multi-floor indoor environment. The system first identifies strides using the accelerations values. It then uses the displacement, duration, and acceleration to classify their types. Our experimental results show that the stride detection accuracy is about 99%. In addition, the types of strides, namely walking, running, going upstairs, and going downstairs, can be detected with the accuracy of 94%, 91%, 95%, and 92%, respectively
Self-Contained Localization without Auxiliary Signals on Smart Devices
(2014-01-01)
Peng, Hsin-Hsien
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Lo, Chi-Chung
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Lin, Tsung-Ching
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Tseng, Yu-Chee
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資訊工程學系
Wearable technologies have attracted considerable attention in mobile computing. This paper proposes a system for localization on wearable devices, such as the smart glasses. The existing approaches to localization, such as AoA, ToA, TDoA, GPS, and RF-based solutions, rely on auxiliary signals at set distances. This limitation constrains the availability of LBS when a mobile device is unable to receive the auxiliary signals. For example, GPS is not available in indoor environments. This paper proposes a system for "self-contained" localization, which refers to the capability of a device to determine its location without having to rely on auxiliary signals, and demonstrates the feasibility of using inertial measurement unit (IMU) and visual sensors in a smart phone to achieve this goal. Based on a concept called inertial sensor-assisted localization (ISAL), IMU sensors are triggered by user motions and visual cues are taken from existing objects. Using augmented reality (AR) techniques, objects captured by the smart phone camera are "tagged" manually or automatically by image processing tools. The angles of these objects relative to the user are then measured by IMU sensors. Based on these angles, we then develop an angulation algorithm to determine the location of the user.
Wireless Location Tracking by a Sensor-Assisted Particle Filter and Floor Plans in a 2.5-D Space
(2018-01-01)
Lo, Chi-Chung
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Chiang, Ting-Hui
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Lee, Tsu-Kuang
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Chen, Ling-Jyh
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Tseng, Yu-Chee
Indoor localization systems have attracted considerable attention recently. A lot of works have used wireless signals from existing base stations to track users' locations. The major challenge to such systems is the signal-drifting problem. A promising direction to conquer this problem is to fuse the tracked wireless signals with inertial sensing data. In this work, we consider location tracking in a multi-floor building, which we call a 2.5-D space, by taking wireless signals, inertial sensing data, and indoor floor plans of a 2.5-D space as inputs and building a SPF (sensor-assisted particle filter) model to fuse these data. Inertial sensors are to capture human mobility, while particles reflect our belief of the user's potential locations. Our work makes the following contributions. First, we propose a model to partition a 2.5-D space into multiple floors connected by stairs and elevators and further partition each floor, according to its floor plan, into logical units connected by passages. Second, based on the 2.5D space model, we then propose particle sampling and resampling mechanisms over the logical units using wireless signal strengths and inertial sensing data to adjust our beliefs of the user's potential locations. Third, to conquer the signal-drifting problem, we propose a weighting mechanism to control the distribution of particles based on user's activities of walking on grounds/stairs and taking elevators. A prototype has been developed and tested to verify the model and its accuracy.