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

Shih Yeh Chen, Chin Feng Lai*, Ren Hung Hwang, Ying Hsun Lai, Ming Shi Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

Original languageEnglish
Article number194
JournalJournal of Medical Systems
Issue number12
StatePublished - 1 Dec 2015


  • Cloud computing
  • Segments selection
  • Wearable health care


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