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Related Experiment Video

Updated: Oct 25, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Fuzzy Guided Autonomous Nursing Robot through Wireless Beacon Network.

K Lakshmi Narayanan¹, R Santhana Krishnan², Le Hoang Son³

¹Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, Tamilnadu India.

Multimedia Tools and Applications

|August 4, 2021

Summary

This summary is machine-generated.

This study introduces an autonomous nursing robot for smart hospitals, enhancing patient care through precise medicine delivery and vital sign monitoring. The robot utilizes a fuzzy controller for advanced obstacle avoidance, ensuring safe and efficient operation in dynamic healthcare environments.

Keywords:

Bluetooth low energy beacon Fuzzy logic Raspberry pi Robotics Smart hospital Ultrasonic sensor Wireless beacon network

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Published on: March 2, 2015

Area of Science:

Robotics and Artificial Intelligence
Healthcare Technology
Sensor Networks

Background:

Robots offer significant potential in smart hospital environments, but require enhanced vision and navigation for obstacle detection and avoidance.
Autonomous Nursing Robots (ANRs) need effective path planning, patient monitoring, and patient interaction capabilities.

Purpose of the Study:

To develop a comprehensive Autonomous Nursing Robot system for smart hospitals.
To enable ANRs to identify patient rooms, deliver medicines, measure vital signs, and interact with patients.

Main Methods:

Utilized Bluetooth Low Energy (BLE) beacons for room identification and navigation via the Angle of Arrival (AOA) method.
Developed a fuzzy controller with ultrasonic sensors and a camera for optimal path planning and collision avoidance.
Integrated sensors for measuring patient body parameters and a Google Assistant for patient interaction.

Main Results:

The fuzzy controller demonstrated high accuracy in obstacle detection and avoidance with minimal computational time.
The robotic system achieved superior accuracy in obstacle detection/avoidance compared to existing systems.
Patient data is securely updated to a private cloud database.

Conclusions:

The proposed Autonomous Nursing Robot system effectively supports essential nursing tasks in smart hospitals.
The fuzzy controller-based navigation system provides robust and efficient obstacle avoidance.
This technology promises to enhance patient care and hospital operational efficiency.