Immediate attention, then the user will be notified through a wearable. If the sound event recognized is an important sound that needs Then using the trained model soundĮvents of real-world situations are identified. So, the MLP model was chosen as the classifier. MLP outperformed when it came to sound event classification in real-world situations. Theįollowing accuracies were obtained from the CNN, SVM and MLP as 96.66%,ĩ0.83%, and 91%, respectively when tested on the test data. (MLP) were used to build machine learning models to classify sound events. Neural Networks (CNN), Support Vector Machine (SVM) and Multilayer Perception Improve living standards of hearing-impaired elderly people. The research aims to design andĭevelop a real time sound event identification and notification system using IoT to Notifying them and their relatives through an app. Proposes a system that assist the hearing impaired by recognizing sound events and As a solution to this problem, this thesis At present there is no proper solution for people with hearing Of someone, sound of brakes in a car and many more. There are important daily sounds such as the sound of the fire alarm, yelling Sometimes the absence of these sounds can be veryĭangerous. While cooking the sizzle of the pan, the ringing of the telephone. The sounds of people laughing around you or listening to birds chirping around you, People with hearing take daily sounds for granted: Most important senses, and the ability to hear enables us to connect with the world for Hearing loss is a sudden or gradual decrease in how well you can hear. The result shows that the Random Forest classifier with proposed extract features performs better by achieving 92.15% accuracy as compared using input image directly with classifiers. Novel features are proposed to be extracted from the images to be input to the four different classifiers namely Random Forest (RF), Bagging classifier, Regression, and Random Tree (RT). The system is able to capture the picture of the sign language performed by a person who is deaf or hard of hearing and provide real-time translation of the hand gestures. In this paper, an automatic Artificial Intelligence (AI) based Arabic Sign Language Translator is proposed. With the widespread of software’s especially on mobile devices, researchers can now develop tools for the community to communicate, work, collaborate and deal normally with persons with disabilities. With the advent of new technologies, software development has also concentrated on the needs of persons with disabilities. Assistive devices are being designed, developed, and sold to provide persons with disabilities the tools to be able to become productive members. The needs of persons with disabilities are still being undermined even though persons with disabilities have proven to be productive members of the community when given the right education, support, and tools. An accuracy of 81.74% has been achieved for MAV dataset, whereas an accuracy of 67.90% is achieved for real time voice input as depicted in the analysed results. The designed system can be regarded as a rescue system for people suffering from various life threatening health conditions including bipolar disorder, hysteria, cardiac arrest, etc. Montreal Affective Voices (MAV) dataset has been utilized for training and testing of voice recognition. Cloud web services have been used to store the real time activity and performing voice analysis. The prototype has been developed by using Raspberry Pi single board along with voice recording module, Wi-Fi module and LCD Screen. The voice features are extracted from analysed voice by employing Discrete Cosine Transform (DCT), and classified through Support Vector Machine (SVM). The proposed standalone system consistently monitors voice activity of person and in case of any abnormality in analysis outcomes, the system automatically notifies concerned hospital or caregiver to prompt for the patient's situation. This paper introduces an IoT based real time analysis and alerting system for the disabled people. Real time and consistent caretaking for such handicapped people is a tedious task. It is usually observed that people with different disabilities face difficulties in all walks of life, and adequate caretaking measures are not adopted in most cases. This research emphasizes on Internet of Things (IoT) based affordable platforms to take proper and timely measures for disabled people.
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