Bone Conduction Auditory Navigation Device for Blind People

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

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.

Smart Cane Developed with DFSS, QFD, and SDE for the Visually Impaired

This study shows an application of the Design for Six Sigma (DFSS) Methodology in the field of medical engineering. This research aims to demonstrate the application of a systematic design approach in the development of the “Ocane”, an innovative concept of smart cane for visually impaired patients which was thought of in answer to the end user’s needs, deploying an easy to transport, locate, and adjust element with ultrasonic sensors and tactile feedback. DFSS is an analytical design methodology meant to organize project workflow in a sequence of specific steps. Other standardized design procedures such as Quality Function Deployment (QFD) and Stylistic Design Engineering (SDE) have been used to support DFSS in terms of targeting customer requirements and focusing on aesthetics for ergonomics analysis, respectively. First, the QFD process is introduced and applied to gather the final customer needs, completing the analysis with benchmarking and similar-thought products on the market. Afterwards, a description of the DFSS methodology and application to the case study was deployed. Thereafter, the SDE procedure is exposed by identifying the “Ocane” concept and development, and moving towards the completion of an inventive product with a creative design and careful attention to visually impaired clients’ requirements.