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Abstract

A ZigBee-based indoor localization system is designed for monitoring the care of patients with mental illnesses such as post-stroke. Due to the severe cognitive impairment of these patients, continuous monitoring by healthcare professionals is essential to ensure their safety and well-being. It is crucial to track their precise location coordinates to prevent any potential accidents. The localization system utilizes the location fingerprint localization method, which involves collecting a location fingerprint database and conducting real-time localization using Received Signal Strength (RSS). To minimize RSS fluctuations, Kalman filter combined with Mean filter is employed. Additionally, an improved Nearest Neighbor algorithm is implemented to enhance the accuracy of localization. C# is used to build the upper computer interface to realize the visualization function of indoor localization, which is convenient for users to view the coordinate location of the mobile nodes and the action trajectory. Ultimately, the localization system undergoes error analysis via a localization experiment. Mobile nodes are deployed to follow a predetermined route within the localization area, and their movement patterns are visualized using the localization interface.

Keywords

indoor fingerprinting localization care monitoring Kalman filter combined with mean filter improved nearest neighbor algorithm

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Research and application of an indoor fingerprinting localization system for rehabilitation care monitoring

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