Real-Time Kinematic Intent Mapping to Acoustic Output via Embedded Sensing and Edge Connectivity

Abstract

Human communication is essential for expressing needs and ensuring timely assistance, especially for elderly, bedridden, or physically challenged individuals. Traditionally, communication relied on verbal interaction or manual signaling methods. Over time, assistive technologies such as call bells, wired alert systems, and basic remote controls were introduced to support such users. However, these conventional systems often require physical effort, are limited in functionality, or fail to provide realtime remote communication, making them less effective in critical situations. The primary problem addressed here is the difficulty faced by individuals who are unable to speak or move freely to convey their basic needs like food, water, or emergency help. Existing systems either lack portability, depend heavily on manual input, or do not integrate modern communication technologies, leading to delays in response and reduced reliability. To overcome these limitations, a system is developed that utilizes gesture recognition through a MEMS accelerometer to detect hand movements and convert them into predefined voice messages. The system is built around a microcontroller with integrated WiFi capability, enabling it to send real-time alerts to a remote server. It also provides immediate feedback through an LCD display and voice output, ensuring both local and remote awareness. This approach enhances accessibility, reduces dependency on others, and ensures faster response during emergencies. By combining gesture sensing, audio output, and IoT connectivity, the system offers a practical and efficient solution for assistive communication in healthcare and home environments.