Wearable sensor-based objective assessment of motor symptoms in Parkinson’s disease

Christiana Ossig, Angelo Antonini, Carsten Buhmann, Joseph Classen, Ilona Csoti, Björn Falkenburger, Michael Schwarz, Jürgen Winkler, Alexander Storch

Research output: Contribution to journalArticlepeer-review


Effective management and development of new treatment strategies of motor symptoms in Parkinson’s disease (PD) largely depend on clinical rating instruments like the Unified PD rating scale (UPDRS) and the modified abnormal involuntary movement scale (mAIMS). Regarding inter-rater variability and continuous monitoring, clinical rating scales have various limitations. Patient-administered questionnaires such as the PD home diary to assess motor stages and fluctuations in late-stage PD are frequently used in clinical routine and as clinical trial endpoints, but diary/questionnaire are tiring, and recall bias impacts on data quality, particularly in patients with cognitive dysfunction or depression. Consequently, there is a strong need for continuous and objective monitoring of motor symptoms in PD for improving therapeutic regimen and for usage in clinical trials. Recent advances in battery technology, movement sensors such as gyroscopes, accelerometers and information technology boosted the field of objective measurement of movement in everyday life and medicine using wearable sensors allowing continuous (long-term) monitoring. This systematic review summarizes the current wearable sensor-based devices to objectively assess the various motor symptoms of PD.

Original languageEnglish
Pages (from-to)57-64
Number of pages8
JournalJournal of Neural Transmission
Issue number1
Publication statusPublished - Jan 1 2016


  • Accelerometer
  • Clinical scores
  • Gyroscope
  • Motor symptoms
  • Objective measurement
  • Parkinson’s disease (PD)

ASJC Scopus subject areas

  • Biological Psychiatry
  • Neurology
  • Clinical Neurology
  • Psychiatry and Mental health


Dive into the research topics of 'Wearable sensor-based objective assessment of motor symptoms in Parkinson’s disease'. Together they form a unique fingerprint.

Cite this