Search Results (3)

Low-cost and sustainable technological systems are required to improve communication between deaf–mute and non-deaf–mute people. Herein, we report a novel low-cost and eco-friendly triboelectric nanogenerator (TENG) composed of recycled and waste components. This TENG can be connected to a smartphone using the internet of things (IoT), which allows the transmission of information from deaf–mute to non-deaf–mute people. The proposed TENG can harness kinetic energy to convert it into electrical energy with advantages such as a compact portable design, a light weight, cost-effective fabrication, good voltage stability, and easy signal processing. In addition, this nanogenerator uses recycled and waste materials composed of radish leaf, polyimide tape, and a polyethylene terephthalate (PET) sheet. This TENG reaches an output power density of 340.3 µWm⁻² using a load resistance of 20.5 MΩ at 23 Hz, respectively. This nanogenerator achieves a stable performance even after 41,400 working cycles. Also, this device can power a digital calculator and chronometer, as well as light 116 ultra-bright blue commercial LEDs. This TENG can convert the movements of the fingers of a deaf–mute person into electrical signals that are transmitted as text messages to a smartphone. Thus, the proposed TENG can be used as a low-cost wireless communication device for deaf–mute people, contributing to an inclusive society. Full article

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and presented in this manuscript. This dataset includes three sign language datasets, i.e., American Sign Language (ASL), Pakistani Sign Language (PSL), and Spanish Sign Language (SSL). The proposed system automatically detects the sign language and converts it into an audio message for the ND-M. Similarities between the three sign languages are also explored, and further research can be carried out in order to help create more datasets, which can be a combination of multiple sign languages. The ND-M can communicate by recording their speech, which is then converted into text and hand gesture images. The system can be upgraded in the future to support more sign language datasets. The system also provides a training mode that can help D-M individuals improve their hand gestures and also understand how accurately the system is detecting these gestures. The proposed system has been validated through a series of experiments resulting in hand gesture detection accuracy exceeding 95%. Full article

Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. The system is modular and can be enhanced by adding data to support more languages in the future. A supervised machine learning dataset is defined and created that provides automated multi-language communication between the DnM and NDnM people. It is expected that this system will support DnM people in communicating effectively with others and restoring a feeling of normalcy in their daily lives. The hand gesture detection accuracy of the system is more than 90% for most, while for certain scenarios, this is between 80% and 90% due to variations in hand gestures between DnM people. The system is validated and evaluated using a series of experiments. Full article