Development of multifunctional glove for obstacle detection and health monitoring using ESP32
Advances in wearable technology coupled with Internet of Things (IoT) innovations allow developers to make mobility and healthcare assistive equipment. Current assistive devices do not effectively address the needs of the visually impaired or people with mobility impairments, especially in detecting...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
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College of Computing, Informatics, and Mathematics
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/128001/1/128001.pdf https://ir.uitm.edu.my/id/eprint/128001/ https://fskmjebat.uitm.edu.my/pcmj/ |
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| Summary: | Advances in wearable technology coupled with Internet of Things (IoT) innovations allow developers to make mobility and healthcare assistive equipment. Current assistive devices do not effectively address the needs of the visually impaired or people with mobility impairments, especially in detecting obstacles or providing real-time fall detection. In this research, the development process of an Obstacle Detection and Health Monitoring Glove using the ESP32 microcontroller is demonstrated. The glove integrates an ultrasonic sensor for real-time obstacle detection and a MAX30100 sensor for heart rate and blood oxygen level (SpO2) monitoring. Haptic feedback via a vibration motor warns the user of nearby obstacles, benefiting visually impaired individuals and those with mobility limitations. The Blynk and ThingSpeak IoT platforms support remote health monitoring and instant notifications for caregivers. Hardware testing confirmed that the glove has an obstacle detection range of up to 30 cm, provides accurate health measurements, and maintains a reliable network connection. User testing validated its usability, realtime feedback, and user-friendly web interface. However, challenges such as network delays and lower accuracy of the MAX30100 sensor compared to professional devices were identified. Future improvements will focus on enhancing sensor precision, optimizing data transfer, and incorporating AI-based medical analytics for predictive healthcare. This research highlights the potential of IoTbased assistive technology to enhance autonomous mobility and health monitoring for individuals with disabilities. |
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