Health Crowd Sensing and Computing: From Crowdsourced Digital Health Footprints to Population Health Intelligence

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Population health monitoring and modelling is important and fundamental for public health operations for the control and intervention of Non-Communicable Diseases (NCD). Healthcare administrators often perform data collection for population health monitoring either by integrating records of hospital visits or conducting survey among a sample of residents, but both approaches are of high cost and time-consuming, which results in limited spatial coverage. The proliferation of devices embedded with multimodality sensors and digital health applications in our daily lives generates data at an unprecedented scale, providing valuable crowdsourced information about personal health status or health-related context. In this book chapter, we propose a new vision, called Health Crowd Sensing and Computing (HCSC), which leverages opportunistic and crowdsourced digital health footprints within a full lifecycle of data collection, linkage, integration, augmentation, and analytics, to realise the goal of more intelligent population health monitoring for NCD. Specifically, our own case study called Compressive Population Health will be introduced, where we combine AI techniques with HCSC to achieve cost-effective public health monitoring. Finally, existing gaps will be discussed with future research opportunities and proposal in this interesting and novel research area.

Original languageEnglish
Title of host publicationMobile Crowdsourcing
EditorsJie Wu, En Wang
PublisherSpringer Nature
Number of pages22
ISBN (Electronic)978-3-031-32397-3
ISBN (Print)978-3-031-32396-6
Publication statusE-pub ahead of print - 21 Apr 2023

Publication series

NameWireless Networks (United Kingdom)
VolumePart F1100
ISSN (Print)2366-1186
ISSN (Electronic)2366-1445

Bibliographical note

Funding Information:
This work was supported by EPSRC New Investigator Award under Grant No EP/V043544/1.


  • Health crowd computing
  • Health crowd sensing
  • Population health intelligence

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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