Wearable electromyography (EMG) and accelerometer device based on human–computer interaction for tetraplegic users

• A low cost user friendly wearable headband that utilizes EMG and accelerometer sensors to facilitate human-computer interactions was designed and tested. • The proposed device is designed to assist tetraplegic individuals in interacting with technology by simply clenching their jaws or making slight head movements. • the device was programmed to control a prototype system consisting of a wheelchair, computer, and smart home devices. Bluetooth was used for communication. • The proposed device underwent testing with ten subjects to control smart home and wheelchair subsystems, achieving an accuracy rate of 95.95 % and a sensitivity rate of 97.64 %. According to the World Health Organization, the most common type of spinal cord injury (SCI) is tetraplegia, where individuals lose motor function in their limbs, leading to difficulties in adapting to their new lives. Assistive technologies are often used to help overcome these challenges. However, many available technologies are either expensive or abandoned due to issues such as inaccuracy, discomfort, or complexity. This work aims to develop a low-cost and user-friendly wearable headband that utilizes EMG and accelerometer sensors to facilitate human-computer interactions. The device is designed to assist tetraplegic individuals in interacting with technology by simply clenching their jaws or making slight head movements. Initially, the device was programmed to control a prototype system consisting of a wheelchair, computer, and smart home devices. Additionally, the open-source design allows users to integrate other Bluetooth-compatible devices they wish to control. This work will improve the quality of life for tetraplegic patient by being able to control the basic devices by himself. The proposed device underwent testing with ten subjects to control smart home and wheelchair subsystems, achieving an accuracy rate of 79.40 % and a sensitivity rate of 97.30 %.