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An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services.

Shih-Yeh Chen1, Chin-Feng Lai2,3, Ren-Hung Hwang4

  • 1Department of Engineering Science, National Cheng Kung University, Tainan, 701, Taiwan, Republic of China. n98014045@mail.ncku.edu.tw.

Journal of Medical Systems
|October 23, 2015
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Summary
This summary is machine-generated.

This study introduces an adaptive method for wearable healthcare services to manage sensor data, improving data transmission and extending device lifetime by optimizing sensing frequencies within cloud computing limits.

Keywords:
Cloud computingSegments selectionWearable health care

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Area of Science:

  • Cloud computing
  • Wearable devices
  • Healthcare services

Background:

  • Wearable devices are increasingly used in healthcare for monitoring physiological events.
  • Long-term healthcare monitoring faces challenges with computing power and energy constraints.
  • Existing systems require optimization for data sensing and transmission.

Purpose of the Study:

  • To propose an adaptive sensor data segments selection method for wearable healthcare services.
  • To address the challenges of computing power and energy limits in long-term monitoring.
  • To optimize sensing frequencies and data transmission for wearable health services.

Main Methods:

  • Developed an adaptive sensor data segments selection method.
  • Considered sensing frequency of human body signals and device data transmission.
  • Regulated device sensing frequency based on cloud computing environment and sensing variations.

Main Results:

  • The proposed method effectively transmits sensing data.
  • The service successfully prolongs the overall lifetime of healthcare services.
  • Optimized sensing frequencies led to improved energy efficiency.

Conclusions:

  • The adaptive method enhances the efficiency of wearable healthcare services.
  • Optimizing data sensing and transmission is crucial for sustainable long-term monitoring.
  • This approach supports the growing demand for reliable remote health monitoring solutions.