AI-Based Elderly Health Monitoring and Emergency Assistance Robot

Abstract

The increasing demand for elderly healthcare has encouraged the development of smart assistive technologies that support independent living and improve patient safety. This review paper examines existing research on intelligent healthcare solutions, focusing on vital sign monitoring, fall detection, medication management, and robotic assistance. It evaluates the techniques used in these systems, highlighting their major benefits, technical limitations, and areas requiring further investigation. The review identifies the need for a unified solution capable of combining multiple healthcare functions rather than relying on separate devices or applications. To address this gap, an AI-based elderly health monitoring and emergency assistance robot is proposed, integrating health sensors, intelligent event detection, and IoT-enabled communication. The proposed approach aims to facilitate timely emergency notifications, improve caregiver accessibility, and provide a more reliable support system for elderly individuals.

Introduction

The research proposes an AI-Based Elderly Health Monitoring and Emergency Assistance Robot that integrates Artificial Intelligence (AI), the Internet of Things (IoT), sensors, and a mobile application to improve elderly care. The system aims to address the limitations of traditional healthcare by providing continuous health monitoring, medication reminders, fall detection, emergency alerts, and remote caregiver communication through a unified platform.

1. Introduction and Objectives

The growing elderly population has increased the demand for accessible and reliable healthcare solutions. Older adults may experience chronic illnesses, reduced mobility, and age-related health complications that require regular monitoring and timely assistance.

Existing healthcare systems often depend on hospital visits and manual caregiving, which may delay emergency responses. Although current smart healthcare technologies provide individual functions, many do not combine all essential services into one platform.

The main objectives of the proposed system are to:

• Monitor vital signs such as heart rate, blood oxygen saturation (SpO?), and body temperature.

• Provide timely medicine reminders.

• Detect falls and other potential emergencies.

• Notify caregivers through IoT connectivity.

• Support obstacle-aware robotic movement.

• Promote independent living while reducing caregiver workload.

2. Literature Review

Previous studies have explored several technologies for elderly assistance:

• MediBOT: Uses a robotic platform to provide medication reminders.

• Socially assistive robots: Investigate companionship, emotional intelligence, and human–robot interaction for older adults.

• Medication adherence systems: Combine companion robots and wearable devices to remind users, confirm medication intake, and maintain medication histories.

• Smart medicine reminder robots: Automate medication schedules and alarms.

• Long-term assistive robot studies: Examine user acceptance, usability, and challenges associated with adopting robots for elderly care.

• AI- and IoT-based monitoring systems: Explore health surveillance, motion sensing, emergency detection, and wireless communication.

These studies demonstrate the potential of assistive technologies but also highlight the need for a unified system combining health monitoring, medication management, emergency response, and caregiver communication.

3. Proposed Methodology

The proposed system follows a sequence of sensing, processing, decision-making, actuation, and communication.

1. Medicine reminder module: Uses predefined medication schedules and a timing mechanism to activate audible or visual reminders.

2. Health monitoring module: Collects vital-sign measurements through sensors and processes the readings using a microcontroller.

3. Obstacle detection and movement: Uses proximity sensors, a motor driver, and DC motors to support robot movement while avoiding obstacles.

4. Emergency alert module: Detects predefined emergency conditions or responds to an SOS input and generates alerts for caregivers.

5. IoT monitoring: Sends selected health readings, medication status, and emergency notifications to a remote monitoring platform when network connectivity is available.

6. System integration and testing: Combines the modules into a robotic platform and evaluates sensor readings, reminder timing, fall detection, movement, alerts, communication, and usability.

The overall workflow is:

Sensors and medication schedules → Microcontroller → Data processing → Decision-making → Monitoring, reminders, and robotic movement → IoT communication and emergency alerts.

4. System Implementation

The proposed platform integrates the following hardware and software components:

Together, these components enable health monitoring, medication reminders, fall detection, robotic movement, and communication with caregivers through a single platform.

5. Discussion

The proposed system addresses the fragmentation of existing elderly healthcare solutions by combining multiple functions into one integrated platform. Continuous vital-sign monitoring can help caregivers observe changes in health parameters, while medication reminders support adherence to prescribed schedules. Fall detection and emergency notifications can help caregivers respond more quickly when assistance is needed.

Camera-based monitoring and IoT connectivity further support remote supervision. The mobile application provides a convenient interface for accessing relevant information and receiving alerts.

However, several challenges remain, including network reliability, implementation costs, data security, sensor accuracy, and the reliability of fall detection. These aspects require further testing and validation before the system can be considered dependable for real-world healthcare use.

Conclusion

This review paper presented an overview of recent developments in AI-based elderly healthcare systems, IoT-enabled monitoring technologies, assistive robotics, and emergency assistance solutions. The reviewed studies demonstrate considerable progress in health monitoring, fall detection, medicine reminder systems, and remote patient care. Nevertheless, most existing solutions are designed to perform limited functions and do not provide an integrated healthcare platform for elderly users. Based on the identified research gap, an AI-Based Elderly Health Monitoring and Emergency Assistance Robot is proposed to combine continuous health monitoring, emergency alert generation, medicine reminders, caregiver communication, and IoT-based remote monitoring into a single intelligent system. The integration of smart sensors, embedded controllers, cloud connectivity, and mobile applications can improve elderly safety, healthcare accessibility, and independent living. Future research may focus on advanced AI-based health prediction, voice interaction, enhanced cybersecurity, and energy-efficient system..

References

[1] R. Garg, S. Saxena, M. Singhal, V. Singh, and K. Shinghal, “A Review Paper on Smart Medicine Reminder Robot,” 2024. [2] G. Amabili, E. Maranesi, A. Margaritini, M. Benadduci, F. Barbarossa, S. Casaccia, H. H. Nap, and R. Bevilacqua, “Usability and Feasibility Assessment of a Social Assistive Robot for the Older People: Results from the GUARDIAN Project,” Bioengineering, vol. 11, no. 1, 2024. [3] F. Liang, Z. Su, W. Sheng, A. Bishop, and B. Carlson, “Medication Adherence Management for In-home Geriatric Care with a Companion Robot and a Wearable Device,” Smart Health, vol. 30, 2023, Art. no. 100434. [4] Y. Song, Z. Wang, and H. Liu, “Research on the Design of Elderly Accompanying Robots from the Perspective of Smart Wellness,” 2024. [5] D. H. S. Deepashree, M. S. Koti, and Y. C. Sen, “SOS Smart Reminder and Emergency Detection Device Using ESP8266 and MPU6050,” INTI Journal, 2024. [6] P. Rantanen, T. Parkkari, S. Leikola, M. Airaksinen, and A. Lyles, “An In-home Advanced Robotic System to Manage Elderly Home-care Patients’ Medications: A Pilot Safety and Usability Study,” Clinical Therapeutics, vol. 39, no. 5, pp. 1054–1061, 2017. [7] S. Logeshvaran, M. P. Jayasurya, S. Tharun, and V. Surya, “Smart AI-Driven System for Elderly Health and Safety Surveillance,” International Journal of Engineering Development and Research (IJEDR), vol. 14, issue 1, pp. 179–185, March 2026. [8] O. Zafrani, G. Nimrod, M. Krakovski, S. Kumar, S. Bar-Haim, and Y. Edan, “Assimilation of Socially Assistive Robots by Older Adults: An Interplay of Uses, Constraints and Outcomes,” Frontiers in Robotics and AI, vol. 11, 2024. [9] A. Elwaly, A. Abdellatif, and Y. El-Shaer, “New Eldercare Robot with Path-Planning and Fall-Detection Capabilities,” Applied Sciences, vol. 14, no. 6, Art. no. 2374, 2024. [10] H. Abdollahi, M. H. Mahoor, R. Zandie, J. Siewierski, and S. H. Qualls, “Artificial Emotional Intelligence in Socially Assistive Robots for Older Adults: A Pilot Study,” IEEE Transactions on Affective Computing, vol. 14, no. 3, pp. 2020–2032, 2023. [11] K. Sultana, M. A. Prioty, A. Rubaiya, A. Tasnim, and I. N. Mitu, “MediBot– Smart Medicine Reminder Robot,” Technical Report, 2026. [12] A. Ariani, V. Kapadia, A. Talaei-Khoei, J. Li, and P. K. Ray, “Challenges in Seniors Adopting Assistive Robots: A Systematic Review,” The International Technology Management Review, vol. 6, no. 2, pp. 25–36, 2016. doi: 10.2991/itmr.2016.6.2.1.

Copyright

Copyright © 2026 Prachi Bhatkar, Namrata Rakhonde, Tejaswini Bhorkhade, Shivraj Deshmukh, Sankalp Deshmukh, R. M. Gharat. 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.