Smart Assistive Technology Using Machine Learning (ML) and Internet of Things (IOT) for Healthcare and Communication.
[1] M. Baritha Begum, [2]Subha Shree. S, [3]Shivani. M, [4] Yogeshwari. V, [5] Swetha. R
[1]B.E Associate Professor, Department of Electronics and Communication, Saranathan College of Engineering, Tiruchirapalli, Tamilnadu, India.
[2]B.E Student, Department of Electronics and Communication, Saranathan College of Engineering, Tiruchirapalli, Tamilnadu, India.
[3]B.E Student, Department of Electronics and Communication, Saranathan College of Engineering, Tiruchirapalli, Tamilnadu, India.
[4]B.E Student, Department of Electronics and Communication, Saranathan College of Engineering, Tiruchirapalli, Tamilnadu, India.
[5]B.E Student, Department of Electronics and Communication, Saranathan College of Engineering, Tiruchirapalli, Tamilnadu, India.
[1]barithaegum-ece@saranathan.ac.in,[2] subha1414shree@gmail.com, [3]Shivanimuruganantham@gmail.com, [4]yogavasu7373@gmail.com, [5]swetharajagopal04@gmail.com
Abstract- Communication is a very difficult task for those who are not able to speak and hear properly. In many situations they are not able to express their requirements or request assistance during emergency situations. Therefore, to assist such people our work is proposing two simple assistive systems using IoT and Machine Learning techniques.
The first proposed system is an IoT- based communication glove. In this glove a motion sensor is fixed on the glove or mount on the glove to detect hand movement. When a specific movement is made by the hand it recognizes the movement and displays a specific message on the screen. At the same time it sends the message to the caregivers through mobile application,here we use blynk. The proposed system also has a fall-detection feature. If a person falls down or makes an unusual movement while in unconscious situation it detects the movement and sends the message to the caregivers like the person is in emergency situation.
The second proposed system is based on machine learning techniques for hand gesture recognition using a webcam. The machine learning model recognizes the hand gesture by identifying the hand landmarks. The model predicts the gesture and displays the corresponding alphabet on the screen with a voice
Keywords:- Assistive Communication System, Internet of Things (IoT), Machine Learning, Hand Gesture Recognition, Fall Detection, Caregiver Alert Notification.
