It is estimated that between 250,000 and 500,000 individuals suffer from spinal cord injuries globally every year. This project aims to provide a lower cost, EMG controlled method of locomotion for tetraplegics to regain some level of independence in their lives. The team will design an ML classification model to detect and classify a tetraplegic user’s muscle activity in their arm using EMG sensors along with an electrified wheelchair with wheels connected to motors controlled by an embedded system interacting with the ML model. The model will detect and classify five possible arm movements so users will be able to stop, move forwards, backwards, or turn left or right with the two motor-controlled wheels. This system is designed to be low-cost relative to current market electric wheelchairs and attachable to any manual wheelchair.