User-Centered Design and Implementation of an Intelligent Wearable Device for Real-Time Health Monitoring in IoT Ecosystems

This paper focuses on the user-centered design and development of an intelligent wearable device for real-time health monitoring, within an IoT ecosystem. Emphasizing ergonomics, aesthetic appeal, and ease of use, the product design process incorporates human factors engineering and material selection to ensure a comfortable and durable device. The wearable integrates advanced biosensors for continuous monitoring of key health metrics and employs wireless communication for seamless data transmission. A key innovation lies in the integration of machine learning algorithms that enhance the device's predictive capabilities, offering users valuable health insights. This paper outlines the design iterations, prototyping process, and usability testing that led to a market-ready product, ensuring the device meets both functional and user experience goals.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/eiecc64539.2024.10929262
Citations
2
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.50
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article

User-Centered Design and Implementation of an Intelligent Wearable Device for Real-Time Health Monitoring in IoT Ecosystems

Zhengdong Li
2 citations
Context-Aware Activity Recognition Systems
0.50
article

User-Centered Design and Implementation of an Intelligent Wearable Device for Real-Time Health Monitoring in IoT Ecosystems

Zhengdong Li
article en
2 citations

Abstract

This paper focuses on the user-centered design and development of an intelligent wearable device for real-time health monitoring, within an IoT ecosystem. Emphasizing ergonomics, aesthetic appeal, and ease of use, the product design process incorporates human factors engineering and material selection to ensure a comfortable and durable device. The wearable integrates advanced biosensors for continuous monitoring of key health metrics and employs wireless communication for seamless data transmission. A key innovation lies in the integration of machine learning algorithms that enhance the device's predictive capabilities, offering users valuable health insights. This paper outlines the design iterations, prototyping process, and usability testing that led to a market-ready product, ensuring the device meets both functional and user experience goals.

Wuhan University (CN), Wuhan Institute of Bioengineering (CN)
Openalex Percentile: Top 32%
Context-Aware Activity Recognition Systems
0.50
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