uTUG: An unsupervised Timed Up and Go test for Parkinson's disease

João Elison da Rosa Tavares*, Martin Ullrich, Nils Roth, Felix Kluge, Bjoern M. Eskofier, Heiko Gaßner, Jochen Klucken, Till Gladow, Franz Marxreiter, Cristiano André da Costa, Rodrigo da Rosa Righi, Jorge Luis Victória Barbosa

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


Inertial measurement units (IMU) are used diagnostically in the movement analysis of Parkinson's disease (PD) patients, allowing an objective way to assess biomechanical motion and gait parameters. The Timed Up and Go (TUG) is a standardized clinical gait test widely used in the monitoring of patient fall risk and disease progression. Gait tests performed at home have been applied as part of movement monitoring protocols, enabling a link to clinical supervised reference assessments. However, unsupervised gait tests in a real-world data context present challenges, mainly regarding the interaction between participants and the recording system. Therefore, we developed and evaluated a novel algorithmic pipeline called unsupervised TUG (uTUG). Our contribution is the automatic detection and decomposition of TUG tests into their subphases, performed at home with no clinician supervision. In contrast to related studies, we used only foot-mounted IMU with no additional markers or manual annotations, allowing the detection of TUG test frames for subsequent classification by machine learning Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes Classifier (NBC) algorithms. The evaluation comprised 96 daily recordings of real-world gait data and 81 clinical visits accumulating 300 real TUG test samples processed from 32 PD patients. A prefiltering sensitivity of 98.6%, followed by the precision of 90.6%, recall of 88.5%, and Fl-score of 89.6% for TUG test detection were achieved using RF for the automatic classification in continuous real-world gait data. Thus, uTUG simplifies the test for patients and avoids manual annotations for clinicians, automatically detecting TUG tests.

Original languageEnglish
Article number104394
JournalBiomedical Signal Processing and Control
Publication statusPublished - Mar 2023


  • Gait analysis
  • Gait test
  • IMU
  • Machine learning
  • TUG
  • Wearable sensors


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