Academic Journal
LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino.
| Τίτλος: | LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino. |
|---|---|
| Συγγραφείς: | Busaeed, Sahar, Mehmood, Rashid, Katib, Iyad, Corchado, Juan M. |
| Πηγή: | Electronics (2079-9292); Apr2022, Vol. 11 Issue 7, p1076-N.PAG, 34p |
| Θεματικοί όροι: | People with visual disabilities, Electric wheelchairs, Assistive technology, Intelligent sensors, Smart devices, Electronic data processing, Object recognition (Computer vision) |
| Περίληψη: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Busaeed%2C+Sahar%22">Busaeed, Sahar</searchLink><br /><searchLink fieldCode="AR" term="%22Mehmood%2C+Rashid%22">Mehmood, Rashid</searchLink><br /><searchLink fieldCode="AR" term="%22Katib%2C+Iyad%22">Katib, Iyad</searchLink><br /><searchLink fieldCode="AR" term="%22Corchado%2C+Juan+M%2E%22">Corchado, Juan M.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Apr2022, Vol. 11 Issue 7, p1076-N.PAG, 34p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22People+with+visual+disabilities%22">People with visual disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+wheelchairs%22">Electric wheelchairs</searchLink><br /><searchLink fieldCode="DE" term="%22Assistive+technology%22">Assistive technology</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+sensors%22">Intelligent sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+devices%22">Smart devices</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/electronics11071076 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 1076 Subjects: – SubjectFull: People with visual disabilities Type: general – SubjectFull: Electric wheelchairs Type: general – SubjectFull: Assistive technology Type: general – SubjectFull: Intelligent sensors Type: general – SubjectFull: Smart devices Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Object recognition (Computer vision) Type: general Titles: – TitleFull: LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Busaeed, Sahar – PersonEntity: Name: NameFull: Mehmood, Rashid – PersonEntity: Name: NameFull: Katib, Iyad – PersonEntity: Name: NameFull: Corchado, Juan M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 11 – Type: issue Value: 7 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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