Smartphone-based obstacle detection for visually impaired people

Patel, Samir; Kumar, Amit; Yadav, Pradeep; Desai, Jay; Patil, Dipali

doi:10.1109/iciiecs.2017.8275916

Cited by 18 publications

(4 citation statements)

References 7 publications

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“…Different types of sensors embedded on smartphones are utilized in [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] to record real-world environment data and, subsequently, employ the available smartphone processors to interpret the data and signal events.…”

Section: Introductionmentioning

confidence: 99%

An Extended Usability and UX Evaluation of a Mobile Application for the Navigation of Individuals with Blindness and Visual Impairments Outdoors—An Evaluation Framework Based on Training

Theodorou

Tsiligkos

Μηλιώνης

et al. 2022

Sensors

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Navigation assistive technologies have been designed to support the mobility of people who are blind and visually impaired during independent navigation by providing sensory augmentation, spatial information and general awareness of their environment. This paper focuses on the extended Usability and User Experience (UX) evaluation of BlindRouteVision, an outdoor navigation smartphone application that tries to efficiently solve problems related to the pedestrian navigation of visually impaired people without the aid of guides. The proposed system consists of an Android application that interacts with an external high-accuracy GPS sensor tracking pedestrian mobility in real-time, a second external device specifically designed to be mounted on traffic lights for identifying traffic light status and an ultrasonic sensor for detecting near-field obstacles along the route of the blind. Moreover, during outdoor navigation, it can optionally incorporate the use of Public Means of Transport, as well as provide multiple other uses such as dialing a call and notifying the current location in case of an emergency. We present findings from a Usability and UX standpoint of our proposed system conducted in the context of a pilot study, with 30 people having varying degrees of blindness. We also received feedback for improving both the available functionality of our application and the process by which the blind users learn the features of the application. The method of the study involved using standardized questionnaires and semi-structured interviews. The evaluation took place after the participants were exposed to the system’s functionality via specialized user-centered training sessions organized around a training version of the application that involves route simulation. The results indicate an overall positive attitude from the users.

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Section: Introductionmentioning

confidence: 99%

An Extended Usability and UX Evaluation of a Mobile Application for the Navigation of Individuals with Blindness and Visual Impairments Outdoors—An Evaluation Framework Based on Training

Theodorou

Tsiligkos

Μηλιώνης

et al. 2022

Sensors

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“…The white cane is the most common tool for visually impaired people to assist them in navigating their environments; however, it has several disadvantages. For example, it occupies one hand, it requires physical contact with the environment [2], it cannot sense obstacles above the ground such as ladders, scaffoldings, tree branches, and open windows [8], and it causes neuromusculoskeletal overuse of injuries and syndromes, which could require rehabilitation [9]. In addition, the person who uses a white cane is often stigmatized due to social reasons [2].…”

Section: Introductionmentioning

confidence: 99%

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

et al. 2022

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Smart wearable technologies such as fitness trackers are creating many new opportunities to improve the quality of life for everyone. It is usually impossible for visually impaired people to orientate themselves in large spaces and navigate an unfamiliar area without external assistance. The design space for assistive technologies for the visually impaired is complex, involving many design parameters including reliability, transparent object detection, handsfree operations, high-speed real-time operations, low battery usage, low computation and memory requirements, ensuring that it is lightweight, and price affordability. State-of-the-art visually impaired devices lack maturity, and they do not fully meet user satisfaction, thus more effort is required to bring innovation to this field. In this work, we develop a pair of smart glasses called LidSonic that uses machine learning, LiDAR, and ultrasonic sensors to identify obstacles. The LidSonic system comprises an Arduino Uno device located in the smart glasses and a smartphone app that communicates data using Bluetooth. Arduino collects data, manages the sensors on smart glasses, detects objects using simple data processing, and provides buzzer warnings to visually impaired users. The smartphone app receives data from Arduino, detects and identifies objects in the spatial environment, and provides verbal feedback about the object to the user. Compared to image processing-based glasses, LidSonic requires much less processing time and energy to classify objects using simple LiDAR data containing 45-integer readings. We provide a detailed description of the system hardware and software design, and its evaluation using nine machine learning algorithms. The data for the training and validation of machine learning models are collected from real spatial environments. We developed the complete LidSonic system using off-the-shelf inexpensive sensors and a microcontroller board costing less than USD 80. The intention is to provide a design of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. This work is expected to open new directions for smart glasses design using open software tools and off-the-shelf hardware.

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“…A white cane is the most popular tool used by visually impaired individuals to navigate their environments; nevertheless, it has a number of drawbacks. It requires physical contact with the environment [ 12 ], cannot detect barriers above the ground, such as ladders, scaffolding, tree branches, and open windows [ 21 ], and generates neuromusculoskeletal overuse injuries and syndromes that may require rehabilitation [ 22 ]. Moreover, the user of a white cane is sometimes ostracized for social reasons [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed

Katib

Albeshri

et al. 2022

Sensors

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Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

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Smartphone-based obstacle detection for visually impaired people

Cited by 18 publications

References 7 publications

An Extended Usability and UX Evaluation of a Mobile Application for the Navigation of Individuals with Blindness and Visual Impairments Outdoors—An Evaluation Framework Based on Training

An Extended Usability and UX Evaluation of a Mobile Application for the Navigation of Individuals with Blindness and Visual Impairments Outdoors—An Evaluation Framework Based on Training

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

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