Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera

Kourogi, Masakatsu; Kurata, Takeshi

doi:10.1109/ismar.2003.1240693

Cited by 142 publications

(112 citation statements)

References 8 publications

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“…An approach for estimating the step length was proposed in [11]. The walking speed can be estimated according to the difference between maximum and minimum for vertical and/or forward acceleration; the relationship can be easily linearized.…”

Section: System Dynamics and Measurement Modelsmentioning

confidence: 99%

“…The angle, which will be called IMU bearing, defines this rotation and is not easy to be estimated. Although some methodologies were proposed (ie [11]), the results of them are not satisfactory. In this work the IMU bearing will not be estimated and only vertical accelerations and angular rates will be further used.…”

Section: Pedestrian Dead Reckoning Tightly Fusedmentioning

confidence: 99%

“…The second approach is based on the pedestrian walking locomotion [11][12][13] and fits to the concept of this work better then SINS because the inertial sensors can be attached to the body of pedestrian or be worn in a pocket (some smart phones already includes inertial sensors and can be easily reused). The accelerometer measurements are used to detect the steps (the step length can be estimated, too) and together with gyro measurement constitutes a pedometer.…”

Section: Introductionmentioning

confidence: 99%

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Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

Kana¹,

Bradáč²,

Fiedler

2014

Journal of Electrical Engineering

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The work aims at a development of positioning algorithm suitable for low-cost indoor or urban pedestrian navigation application. The sensor fusion was applied to increase the localization accuracy. Due to required low application cost only low grade inertial sensors and wireless network based ranging were taken into account. The wireless network was assumed to be preinstalled due to other required functionality (for example: building control) therefore only received signal strength (RSS) range measurement technique was considered. Wireless channel loss mapping method was proposed to overcome the natural uncertainties and restrictions in the RSS range measurements The available sensor and environment models are summarized first and the most appropriate ones are selected secondly. Their effective and novel application in the navigation task, and favorable fusion (Particle filtering) of all available information are the main objectives of this thesis.

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Section: System Dynamics and Measurement Modelsmentioning

confidence: 99%

Section: Pedestrian Dead Reckoning Tightly Fusedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

Kana¹,

Bradáč²,

Fiedler

2014

Journal of Electrical Engineering

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“…Kourogi et al [19] estimated user location using GPS and an accelerometer. However, apart from the direction of movement, this approach suffers from a high level of noise in the acceleration value which limits where the sensor can be located.…”

Section: Location-based User Movement Estimationmentioning

confidence: 99%

Shaka: User Movement Estimation Considering Reliability, Power Saving, and Latency Using Mobile Phone

Kobayashi

Muramatsu

Kamisaka

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a method for using an accelerometer, microphone, and GPS in a mobile phone to recognize the movement of the user. Past attempts at identifying the movement associated with riding on a bicycle, train, bus or car and common human movements like standing still, walking or running have had problems with poor accuracy due to factors such as sudden changes in vibration or times when the vibrations resembled those for other types of movement. Moreover, previous methods have had problems with has the problem of high power consumption because of the sensor processing load. The proposed method aims to avoid these problems by estimating the reliability of the inference result, and by combining two inference modes to decrease the power consumption. Field trials demonstrate that our method achieves 90% or better average accuracy for the seven types of movement listed above. Shaka's power saving functionality enables us to extend the battery life of a mobile phone to over 100 hours while our estimation algorithm is running in the background. Furthermore, this paper uses experimental results to show the trade-off between accuracy and latency when estimating user activity.

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“…As the indoor positioning technology, Dead Reckoning has been widely studied [1], [2], [3]. In the Dead Reckoning, a user's movement is estimated by analyzing data obtained from various sensors (e.g., acceleration, gyroscope, etc.).…”

Section: Introductionmentioning

confidence: 99%

Android-based navigation system for elderly people in hospital

Aoki

Yamamoto

Yamazaki

2014

16th International Conference on Advanced Communication Technology

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Abstract-With the increase in the number of elderly people, a guidance system that can be used for medical examination in hospitals becomes to attract attention. As a position estimation technology for indoor guidance, a Wi-Fi positioning system has been studied. However, Wi-Fi radio waves are not available in the hospitals because that affect operations of medical equipment. In this paper, we propose a new realtime indoor guidance system using Personal Handy-phone System (PHS) and Android device for visitors in hospitals. Our proposed system utilizes a combination of Received Signal Strength Indication (RSSI) of PHS and Dead Reckoning based on sensors of an Android device in order to estimate a user's indoor position without affecting operation of medical equipment. Through the experimental evaluation, we have clarified that the proposed system has successfully guided subjects to the destinations with accuracy of 93.3[%].

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Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera

Cited by 142 publications

References 8 publications

Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

Shaka: User Movement Estimation Considering Reliability, Power Saving, and Latency Using Mobile Phone

Android-based navigation system for elderly people in hospital

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