Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Huan Zhao, Junyi Cao, Junxiao Xie, Wei Hsin Liao, Yaguo Lei, Hongmei Cao, Qiumin Qu, Chris Bowen

Research output: Contribution to journalReview articlepeer-review

3 Citations (SciVal)


Objective: Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. 

Methods: A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. 

Results: The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. 

Conclusions: This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

Original languageEnglish
JournalDigital Health
Early online date16 May 2023
Publication statusPublished - 31 Dec 2023

Bibliographical note

Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Key Research and Development Program of China (grant number 2021YFE0203400), and Innovation and Technology Commission under Mainland-Hong Kong Joint Funding Scheme (MHKJFS), the Hong Kong Special Administrative Region, China (Project No. MHP/043/20).


  • feature
  • health monitoring
  • machine learning
  • neurodegenerative disorders
  • Parkinson's disease
  • Wearable sensors

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics
  • Computer Science Applications
  • Health Information Management


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