Smart cane location guide for blind using GPS

Hapsari, Gita Indah; Mutiara, Giva Andriana; Kusumah, Dicky Tiara

doi:10.1109/icoict.2017.8074697

Cited by 17 publications

(9 citation statements)

References 1 publication

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“…Concerning the walking assistants, the studies [16], [100], [102], [134], [135], [137], [143], [144], [161] report smart sticks or white canes for disabled people using sensors and computer vision techniques according to the convention pointed out in the review about walking assistants in reference [29]. It is important to notice that the authors interchange the terms smart stick or white cane in their works.…”

Section: ) Visual Disability and Impairmentsmentioning

confidence: 99%

“…Besides, the authors added some ultrasonic sensors to detect obstacles and agile the navigation of the person. Some analogous ATs with navigation through GPS and ultrasonic sensors are described in the references [134], [144]. Furthermore, two wearable navigation assistants composed of ultrasonic sensors, voice commands, and vibration motors to provide feedback to the user are explained in documents [142], [147].…”

Section: Educational Devices and Materialsmentioning

confidence: 99%

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Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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Disabled People deal with a series of barriers that limit their inclusion, empowerment, wellbeing, and role in society with a special emphasis in low and medium-income countries. One of these barriers is concerning the accessibility and affordability of assistive technologies (ATs) that help to enhance the quality of life of these persons. In this context, this systematic literature review (SLR) analyzes and describes how free and open-source hardware (OSHW) and open software (OSS) are employed in the design, development, and deployment of low-cost ATs. In the SLR process, different ATs were analyzed for disabilities such as visual, mobility, upper body, prostheses, hearing & speaking, daily living, and participation in society. The ATs were designed with diverse OSHW and OSS technologies such as Arduino, Raspberry Pi, NVidia Jeston, OpenCV, YOLO, MobileNet, EEG and EMG signal conditioning devices, actuators, and sensors such as ultrasonic, LiDar, or flex. 809 studies were collected and analyzed from the database Web of Science, GitHub, and the specialized journals in OSHW HardwareX and the Journal of Open Hardware during the years 2013-2022. In the first part of the SLR, the bibliometric trends and topic clusters regarding the selected studies are described. Secondly, the ATs identified with open source technologies, e.g., sensor-based or computer vision-based, are described along with a complete state-ofart about these based on each disability recognized. Finally, the issues and challenges to this approach are explored including technical factors, documentation, government policies, and the inclusion of disabled people in open source co-creation. The purpose of this study is to inform practitioners, designers, or stakeholders about low-cost (frugal) ATs with OSHW and OSS, and thus promote their development, accessibility, and affordability, contributing to benefit the community of disabled people. INDEX TERMS Assistive technologies, disabled people, persons with disabilities, adaptive aids, open hardware, open-source hardware, open-source software, systematic literature review.

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Section: ) Visual Disability and Impairmentsmentioning

confidence: 99%

Section: Educational Devices and Materialsmentioning

confidence: 99%

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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“…In this paper, we train the network with 30 classes, including 29 kinds of objects and backgrounds. Eq (1) and Eq (2) shows the loss function used to train the network.…”

Section: Object Detection Networkmentioning

confidence: 99%

“…The GPS-based technique [2] uses location information to guide the user to his destination. It can guide the user to any destination with location information without training the route in advance.…”

Section: Introductionmentioning

confidence: 99%

GuideDogNet: A Deep Learning Model for Guiding the Blind in Walking Environments

2021

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A guide dog is a critical companion for the blind, which enables independent travel of the blind. However, due to the costly and time-consuming training process, only 1.7% of the blind who wish to adopt a guide dog can take it. In order to alleviate this social problem, previous studies have suggested several blind guiding systems heavily based on hardware devices, such as GPS(Global Positioning System), RFID(Radio-Frequency Identification), and ultrasonic devices. However, those techniques lack administrative feasibility to use in real-world environments. Moreover, those techniques are deficient in warning of obstacles, which makes the system non-user-friendly. To guide the blind universally and provide accurate information about the obstacles without cumbersome devices, we propose a novel deep learning-based blind guiding system, GuideDogNet. The proposed system consists of an object detection network, depth prediction network, and post-processing module. To provide user-friendly outputs for the blind, we propose a rule-based post-processing module that outputs the label, direction, and distance of the obstacles by combining the results of the object detection network and the depth prediction network. We achieved an mAP of 67.8 on the AI Hub Sidewalks dataset which is publicly available. To the best of our knowledge, this is the first attempt at a deep learning-based blind guiding system. The code will be available on https://github.com/Yunseo-Hwang/AI-GuideDog

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“…Previously, our team researchers have been made some research related to smart cane as a smart tool for the visually impaired. Starting with the visually impaired's cane which can be determined the location of the visually impaired based-on GPS [16] and also can determine the direction of qibla using an interactive voice command based on Raspberry Pi [17]. The limitation of our previous research is there is no function that can support the social life of visually impaired.…”

Section: Introductionmentioning

confidence: 95%

Face recognition smart cane using haar-like features and eigenfaces

Hapsari¹,

Mutiara²,

Tarigan³

2019

TELKOMNIKA

Self Cite

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Visually impaired has the limitation in interacting with another human. They usually use the sense of hearing and touching their face to recognize human. Face recognition is a technology that can be used to solve this problem. This paper develops a smart cane function by integrated face recognition feature on the cane using Haar-Like features and Eigenfaces. This paper proposed a portable, real time, and wearable product. Raspberry Pi supports portability that affects the delay and computing speed of face recognition algorithms. Utilization of Raspi camera on the eyes glasses is for wearable purposes. Voice output provides information on whether the face is caught on camera or not. This prototype works well during the face detection and recognition process. It needs 3 seconds for one-face recognized in range 0.25 until 1.5 meters from the camera, until the sound and information are generated. It needs is me 5 second for two faces recognized and 10 seconds for 3 faces recognized by a system in the same range between face and the camera. The accuracy reaches 91.67% for the upright position face but for other position the accuracy is only 18% until 32%.

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Smart cane location guide for blind using GPS

Cited by 17 publications

References 1 publication

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

GuideDogNet: A Deep Learning Model for Guiding the Blind in Walking Environments

Face recognition smart cane using haar-like features and eigenfaces

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