Gesture Controlled Robot for Elderly Assistance

Abstract

The most effective technology is the one that disappears into the background allowing people to accomplish more with less.\" Inspired by this, we suggest a gesture-activated robotic assistant to aid senior citizens with daily tasks. Age-related mobility problems can make seemingly simple tasks challenging and dangerous. Our technology detects hand gestures—wirelessly sent to a four-wheeled mobile robot—using a glove with flex sensors and an MPU6050 accelerometer. Driven by Bluetooth and an Arduino microcontroller, the robot reads gestures as movement directions, therefore allowing safe and autonomous interaction. In gesture recognition with real-time response, the system attained a 95% accuracy rate. By offering a reasonably priced, scalable solution, this work advances the more general objective of human-centered robots beyond only satisfying pragmatic demands. Its modular architecture lets future modifications like robotic arms or obstacle detection possible, therefore allowing it to be flexible in changing user needs.

Introduction

With the global elderly population increasing, ensuring their safe and independent living is a critical challenge. Many seniors face mobility issues due to conditions like arthritis or chronic pain, making everyday tasks difficult or hazardous. To address this, a gesture-activated robotic assistant is proposed that allows elderly users to control a robot via natural hand movements detected by a wearable glove equipped with flex sensors and an MPU6050 accelerometer.

The glove wirelessly sends commands via Bluetooth to a four-wheeled robot, enabling real-time, intuitive control to assist with tasks like object retrieval. This system emphasizes simplicity, affordability, and user comfort, designed specifically for home environments. The architecture integrates three main components: gesture recognition via sensors processed by an Arduino Nano, Bluetooth communication using HC-05 modules, and robot navigation controlled by an Arduino UNO with an L298N motor driver.

The design includes calibration and smoothing algorithms to improve gesture accuracy and responsiveness. Experimental tests demonstrated a 95% recognition accuracy and fast robot response (150–200 ms), confirming effective real-time control across varied indoor surfaces. The modular system is scalable for future features like robotic arms or voice commands.

This solution highlights how accessible embedded technology and sensor integration can empower elderly users, promoting autonomy and safety through easy, natural interaction with assistive robots.

Conclusion

Particularly in tackling daily physical chores that many seniors find more difficult due to age-related limits including decreased mobility, joint discomfort, and reduced strength, the growing elderly population poses serious issues in the field of independent life. The goal of this study was to create a practical and human-centered solution by designing and implementing a gesture-controlled robotic system that helps elderly people with object retrieval and basic navigation tasks. The suggested system uses simple hand gestures picked up by a worn glove to wirelessly interact with a four-wheeled mobile robot to execute the intended physical motions.This study unequivocally shows in home-assistance situations the feasibility and efficiency of gesture-based human-robot interaction. Key needs for real-world applications targeted for senior users include high precision, low latency, and ease of operation; they are achieved by using low-cost components including flex sensors, MPU6050 accelerometer, Arduino microcontrollers, and Bluetooth modules. Unlike camera-based gesture systems, which are sensitive to ambient conditions and require suitable lighting, our sensor-based approach is durable across a variety of indoor environments and does not rely on external installations. Furthermore, the choice to apply wearable technology gives individuals with little to no expertise in engaging with digital systems more personal, ergonomic, and accessible interface. The robot showed consistent performance across development and testing; its gesture recognition accuracy exceeded 95% and its movement response times fell well within reasonable bounds for human-robot interaction. Functional testing carried out in realistic home-like surroundings revealed that the robot could correctly comprehend tilt and hand position commands, therefore enabling forward, backward, left, right, and stop operations. Essential for geriatric comfort and mobility, the use of Bluetooth as a communication medium proved consistent inside an interior range and the complete system ran wirelessly free from tethered restrictions. One of the main lessons from this work is the need of creating assistive technologies from a user-first point view. The complexity of contemporary digital systems adds to the challenges the elderly experience not simply from their physical constraints. Their quality of life can be much enhanced by a system that is straightforward, responsive, and easy to operate—without depending on smartphones, apps, or sophisticated interfaces. Supported by a lightweight control logic and motor driver system, our glove-based gesture input offers users a direct and natural approach to connect with technology in a way that replics human intuition and motion. Apart from attending to a particular use-case for object retrieval and mobility aid, this study opens a spectrum of future opportunities. The system\'s modular design allows smooth integration of other capabilities including robotic arms for object lifting, ultrasonic sensors for obstacle detection and avoidance, and even voice command modules for multi-mode control. Furthermore extending the current structure with machine learning capabilities will help to personalize gesture detection or match a user\'s movement behavior over time. Dealing with degenerative diseases, when motor skills decrease progressively, this adaptability will be very helpful. Natural characteristics of this project are also scalability and adaptability. The whole system may be replicated and customized for particular user demands at a reasonable cost since open-source platforms and off-the-shelf components are used in construction. This makes it particularly fit for use in developing areas, where access to assistive technologies and specialist healthcare could be restricted. A s a basic project to investigate embedded systems, robotics, and human-machine interaction in a useful, practical environment, educational institutions and makerspaces can also adopt this technology. Still, the system has certain limits. Although Bluetooth communication is enough for short-range use, it could suffer interference in surroundings including several electrical gadgets. The present gesture set consists on simple directional commands, which although enough for navigation does not yet enable intricate object handling. Especially to enable longer lifetime for continuous usage, power optimization still has room for development. Notwithstanding these limitations, the system provides a workable prototype and evidence-of-concept proving gesture-based interaction has great potential in the field of assistive robotics. Finally, by means of a meaningful, efficient, and user-friendly integration of gesture detection with robotic control, this research effectively addresses a real-world issue. It shows how profoundly low-cost embedded solutions, when created with sensitivity and accuracy, can improve the quality of life of people who most require help. Systems like the one shown in this study will not only become more capable but also become increasingly important in helping elderly people to lead independent and dignified life as technology develops. Future studies and multidisciplinary cooperation will enhance the opportunities even more, therefore human-centered robotics becomes a useful and strong friend in elderly care.

References

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Copyright

Copyright © 2025 G. D. Meivezhi, Advika Seetharaman, Anika Seetharaman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.