Development of an APHNSIA patient mobile communication system utilizing Electromyography (EMG) signals.

Abstract

Aphasia is a language disorder caused by damage to either the temporal lobe or the frontal lobe of the brain that controls language expression and comprehension. It could arise from various conditions such as stroke, dementia, brain tumor, and accidents (head injury). It leaves a person unable to communicate effectively hence aphasia patients may live an otherwise avoidable low quality of life or take longer to heal. The available technologies to aid the patients’ communication like virtual reality and speech-generating devices are expensive and also need time for the patient to learn how to use them. This leaves a gap in communication between aphasia patients and the caregiver especially when the two parties are not in the same location or vicinity. Yet about one-third of stroke cases result in Aphasia. -according to Aphasia Awareness Statistics. In this project, we set out to develop a low-cost system that enables remote aphasia patients to alert their caregivers that they are in need of assistance by sending a notification to the caregiver’s mobile device. The proposed system consists of electrodes (attached to the patient’s arm using electrode pads), an EMG sensor (for sensing the electric activity in the muscles), lithium-ion batteries (as the power supply to the EMG sensor), Arduino Uno (as an interface between the Global System for Mobile communication (GSM) and the EMG sensor) and a GSM module (for sending the notification). The CoolTerm software and Arduino IDE were used for data collection and the Matrix Laboratory (MATLAB) was used to develop the signal processing and feature extraction algorithms. We built the circuit to obtain the surface EMG (sEMG) signals from the Biceps Branchii. Algorithms were then developed in MATLAB to process the signals and also extract features. The feature extraction was done in time domain analysis using Root Mean Square and Mean Absolute Value. The developed algorithms were then integrated with the hardware by programming them to the Arduino Uno. Lastly, we evaluated the functionality of the prototype by testing it on healthy volunteers. The system was tested on 3 individuals and it worked as expected i.e., the individuals(patients), by flexing their arm were able to send a notification to a mobile device (the caregiver’s) alerting them that they needed assistance. Therefore, this system is a quick, immediate and effective way for aphasia patients to communicate with their caregivers. In the future, identification of flexion of the arm in different positions and relating it to specific notifications like “I need to use the restroom” and other messages specific to the patient could be added to the system to further aid the communication of aphasia patients.