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Naman Kumar Yadav,
Amit Tiwari,
Saurabh Kumar,
Rachit Srivastava,
- Student, Department.of Electrical Engineering, Institute of Engineering and Rural Technology (IERT), Prayagraj, Uttar Pradesh, India
- Assistant Professor, Department.of Electrical Engineering, Institute of Engineering and Rural Technology (IERT), Prayagraj, Uttar Pradesh, India
- Assistant Professor, Department.of Electrical Engineering, Institute of Engineering and Rural Technology (IERT), Prayagraj, Uttar Pradesh, India
- Assistant Professor, Department.of Electrical Engineering, Bansal Institute of Engineering and Technology (BIET), Lucknow, Uttar Pradesh, India
Abstract
This research provides an ultra-low-latency intelligent control framework for wireless assistive wheelchair movement through single-channel surface electromyography (sEMG) and the ESP-NOW communication technology. The framework presented incorporates sEMG acquisition, simple real-time signal processing, determinable decision logic, and wireless communication for controlling the devices using inexpensive ESP32/ESP8266 microcontrollers. It also allows a system to function as an effective interface between a person and a machine suffering from severe motor disability. The processing system consists of simple processes such as full-wave rectification, using exponential moving average for envelope detection, and adaptive thresholding, allowing reliable interpretation of voluntary muscle contractions with less computation than using conventional machine learning technologies and without requiring training on a particular user. The motion commands are transmitted through connection-less ESP-NOW protocol providing the efficient peer-to-peer mode of communication with minimum traffic and latency. The experimental investigation shows that the end-to-end latency of the system is below 10 seconds with more than 90% reliability of signal detection, stable performance of the system, and low power consumption. The suggested approach has attained great responsiveness and reliable wheelchair movement with a remarkable decrease in computing complexity, cost of implementation, and hardware requirements concerning conventional brain-machine interface and machine learning-based control using EMGs. Because of its simplicity, scalability, and energy-efficient embedded implementation, the system can be used in various real practical situations, especially in healthcare and rehabilitation environment with little means.
Keywords: Assistive Wheelchair, Biomedical Signal Processing, Embedded Systems, ESP-NOW Protocol, Human-Machine Interface (HMI), and Ultra-Low Latency
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Recent Trends in Sensor Research & Technology
| Volume | 13 | |
| 02 | ||
| Received | 23/07/2026 | |
| Accepted | 05/10/2026 | |
| Published | 10/10/2026 | |
| Publication Time | 79 Days |