Indoor Positioning for Visually Impaired People Based on Smartphones

Moder, Thomas; Hafner, Petra; Wieser, Manfred

doi:10.1007/978-3-319-08596-8_68

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…Therefore, localisation accuracy is not sufficient (3 to 30m) for visually impaired navigation. Moreover, the proposed systems for visually impaired navigation need to be supported by Inertial Measurement Units (IMUs) and Near Field Communication (NFC) tags [11], [12]. Bluetooth is a short-range wireless technology for exchanging data between devices over short distances.…”

Section: Rf-based Navigation Technologies For the Visually Impairedmentioning

confidence: 99%

Radar-based indoor navigation system for visually impaired

Akaydin¹,

Onireti²,

Ahmad³

et al. 2023

IET Conf. Proc.

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The absence of the Global Positioning System (GPS) inside buildings makes indoor navigation more difficult compared to outdoor environments. This paper provides a summary of visually impaired indoor navigation technologies to identify the needs of the visually impaired to safely navigate indoors. Radar as a sensor is preferred because it works in conditions where other systems cannot work properly and has unique features. In particular, this paper reviews different radar SLAM and ego-motion estimation approaches that can aid indoor navigation. This paper concludes with a brief insight into the future direction for a novel radar-based wearable indoor navigation system.

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Section: Rf-based Navigation Technologies For the Visually Impairedmentioning

confidence: 99%

Radar-based indoor navigation system for visually impaired

Akaydin¹,

Onireti²,

Ahmad³

et al. 2023

IET Conf. Proc.

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“…They observed that the vibration patterns of three different behaviors (walking on a flat road, descending steps, and waiting at stoplights) are apparently different, meaning that motion recognition of the blind people using the tri-axial accelerometer data is quite practicable. The primary contribution in [9] is the fusion of Pedestrian Dead Reckoning (PDR) data and the Wi-Fi fingerprinting data to achieve indoor positioning for visually impaired people, in which the outcome of PDR algorithm combines the accelerometer, gyroscope, magnetometer, and barometer data. The fusion is a two-step process: using Kalman filter to detect the floor on which the person is moving, and then using Particle filter to estimate the trajectory.…”

Section: B Motion Recognition and Indoor Positioning By Sensors In Smentioning

confidence: 99%

“…By analyzing the collected acceleration patterns, behaviors such as walking on a flat road, descending steps, or waiting at stoplights can be distinguished. Many others also use tri-axial accelerometers along with other sensors such as gyroscope, magnetometer, or even Wi-Fi fingerprinting in smartphones and/or inertial measurement units (IMUs) to assist indoor and outdoor navigation [9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

A crowdsourcing approach to promote safe walking for visually impaired people

Lin

Huang

Hsu

2014

2014 International Conference on Smart Computing

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Visually impaired people have difficulty in walking freely because of the obstacles or the stairways along their walking paths, which can lead to accidental falls. Many researchers have devoted to promoting safe walking for visually impaired people by using smartphones and computer vision. In this research we propose an alternative approach to achieve the same goal -we take advantage of the power of crowdsourcing with machine learning. Specifically, by using smartphones carried by a vast amount of visually normal people, we can collect the tri-axial accelerometer data along with the corresponding GPS coordinates in large geographic areas. Then, machine learning techniques are used to analyze the data, turning them into a special topographic map in which the regions of outdoor stairways are marked. With the map installed in the smartphones carried by the visually impaired people, the Android App we developed can monitor their current outdoor locations and then enable an acoustic alert whey they are getting close to the stairways.

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“…Then, a compressive sensing-based positioning scheme was applied to obtain a refined position estimate. Moder et al [ 21 ] focused on the abilities of an indoor positioning system purely based on sensors that are already present in smartphones. Algorithms were designed to process the accelerometer, gyroscope, magnetometer and barometer data, and Wi-Fi fingerprinting; the results were then passed through a mathematical filter to obtain a final position and heading information.…”

Section: Related Workmentioning

confidence: 99%

Study on an Indoor Positioning System for Harsh Environments Based on Wi-Fi and Bluetooth Low Energy

Blasio

Quesada-Arencibia

García

et al. 2017

Sensors

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This paper presents a study of positioning system that provides advanced information services based on Wi-Fi and Bluetooth Low Energy (BLE) technologies. It uses Wi-Fi for rough positioning and BLE for fine positioning. It is designed for use in public transportation system stations and terminals where the conditions are “hostile” or unfavourable due to signal noise produced by the continuous movement of passengers and buses, data collection conducted in the constant presence thereof, multipath fading, non-line of sight (NLOS) conditions, the fact that part of the wireless communication infrastructure has already been deployed and positioned in a way that may not be optimal for positioning purposes, variable humidity conditions, etc. The ultimate goal is to provide a service that may be used to assist people with special needs. We present experimental results based on scene analysis; the main distance metric used was the Euclidean distance but the Mahalanobis distance was also used in one case. The algorithm employed to compare fingerprints was the weighted k-nearest neighbor one. For Wi-Fi, with only three visible access points, accuracy ranged from 3.94 to 4.82 m, and precision from 5.21 to 7.0 m 90% of the time. With respect to BLE, with a low beacon density (1 beacon per 45.7 m2), accuracy ranged from 1.47 to 2.15 m, and precision from 1.81 to 3.58 m 90% of the time. Taking into account the fact that this system is designed to work in real situations in a scenario with high environmental fluctuations, and comparing the results with others obtained in laboratory scenarios, our results are promising and demonstrate that the system would be able to position users with these reasonable values of accuracy and precision.

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Indoor Positioning for Visually Impaired People Based on Smartphones

Cited by 10 publications

References 3 publications

Radar-based indoor navigation system for visually impaired

Radar-based indoor navigation system for visually impaired

A crowdsourcing approach to promote safe walking for visually impaired people

Study on an Indoor Positioning System for Harsh Environments Based on Wi-Fi and Bluetooth Low Energy

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