International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | IOT Based Electric Vehicle Energy Consumption Predicting Device |
|---|---|
| ABSTRACT | Mobility challenges faced by individuals with physical disabilities and elderly users significantly affect independence and quality of life. Conventional manual wheelchairs demand physical effort, while many existing powered wheelchairs are expensive and lack adaptive, user-friendly control mechanisms. This paper presents a Voice Controlled Smart Wheelchair using ESP32 with Gesture Control and Obstacle Detection, designed as a low-cost, intelligent, and accessible mobility solution. The system integrates a Python-based speech recognition module for voice commands, flex sensor-based gesture control for alternate input, and IR sensor-based obstacle detection to ensure safety. The ESP32 microcontroller acts as the central processing unit, communicating wirelessly via Wi-Fi using the UDP protocol. Experimental evaluation demonstrates reliable command recognition, smooth motor control, and effective obstacle avoidance, validating the system’s suitability for real-world assistive mobility applications. |
| AUTHOR | VARSHA MR, VARAMAHALAKSHMI MS, ASHWINI GS, THANUJA M, DR. MALATESH SH Student, Dept. of Computer Science and Engineering, MS Engineering College, Bengaluru, Karnataka, India HOD, Dept. of Computer Science and Engineering, MS Engineering College, Bengaluru, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401042 |
| pdf/42_IOT Based Electric Vehicle Energy Consumption Predicting Device.pdf | |
| KEYWORDS | |
| References | 1. Sensor Fusion & Gesture Recognition C. Zhang, Y. Li, and J. Wang, “Hand Gesture Recognition Using Sensor Fusion and Deep Learning for Assistive Devices,” IEEE Access, vol. 11, pp. 22345–22358, Jan. 2025. 2. Voice Recognition & Embedded Systems A. Sharma, R. Gupta, and P. Kumar, “Real-Time Voice Controlled Wheelchair Using Embedded Systems and IoT,” Int. J. Intelligent Robotics and Applications, vol. 8, no. 3, pp. 102–112, Mar. 2024. 3. Embedded Control & Obstacle Avoidance J. Lee and H. Kim, “Autonomous Navigation and Obstacle Avoidance in Smart Wheelchairs,” Journal of Robotics and Mechatronics, vol. 37, no. 4, pp. 411–423, Aug. 2024. 4. IoT&RemoteMonitoring M. Patel, A. Desai, and R. Shah, “IoT-Enabled Smart Wheelchair for Real-Time Monitoring and Remote Assistance,” Int. J. IoT and Smart Technology, vol. 10, no. 2, pp. 88–99, Feb. 2025 5. Hardware-BasedGestureControl S. K. Rathore, M. Singh, and D. Tiwari, “Smart Wheelchair Control Using Flex Sensors and IMU-Based Gesture Detection,” Int. Conf. on Human-Computer Interaction, 2023, pp. 65–75. 6. Dr. Malatesh S.H., S. Kattimani, P.Pallabavi, V. A. P., &D. M. G., (2025)”Intruder detection protection system”. and International Journal of Innovative Research in Technology (IJIRT), vol. 11, no. 12, p. 6287, May 2025, ISSN: 2349-6002. |
