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Sensor-based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: a Survey

Authors:

Samaneh Zolfaghari, Sumaiya Suravee , Daniele Riboni , Kristina Yordanova

Research group:

Health-promoting engineering

Publication Type:

Article, review

Venue:

ACM Computing Surveys

Publisher:

Association for Computing Machinery (ACM)

DOI:

10.1145/3603495

Abstract

Locomotion characteristics and movement patterns are reliable indicators of neurodegenerative diseases (NDDs). This survey provides a systematic literature review of locomotion data mining systems for supporting NDDs diagnosis. We discuss techniques for discovering low-level locomotion indicators, sensor data acquisition and processing methods, and NDDs detection algorithms. The survey presents a comprehensive discussion on the main challenges for this active area, including the addressed diseases, locomotion data types, duration of monitoring, employed algorithms, and experimental validation strategies. We also identify prominent open challenges and research directions regarding ethics and privacy issues, technological and usability aspects, and availability of public benchmarks.

Bibtex

@article{Zolfaghari6904,
author = {Samaneh Zolfaghari and Sumaiya Suravee and Daniele Riboni and Kristina Yordanova},
title = {Sensor-based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: a Survey},
volume = {1},
month = {August},
year = {2023},
journal = {ACM Computing Surveys},
publisher = {Association for Computing Machinery (ACM)},
url = {http://www.es.mdu.se/publications/6904-}
}