Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease.
Luigi BorziMarilena VarrecchiaStefano SibilleGabriella OlmoCarlo Alberto ArtusiMargherita FabbriMario Giorgio RizzoneAlberto RomagnoloMaurizio ZibettiLeonardo LopianoPublished in: IEEE open journal of engineering in medicine and biology (2020)
Goal: In this paper we investigated the use of smartphone sensors and Artificial Intelligence techniques for the automatic quantification of the MDS-UPDRS-Part III Leg Agility (LA) task, representative of lower limb bradykinesia. Methods: We collected inertial data from 93 PD subjects. Four expert neurologists provided clinical evaluations. We employed a novel Artificial Neural Network approach in order to get a continuous output, going beyond the MDS-UPDRS score discretization. Results: We found a Pearson correlation of 0.92 between algorithm output and average clinical score, compared to an inter-rater agreement index of 0.88. Furthermore, the classification error was less than 0.5 scale point in about 80% cases. Conclusions: We proposed an objective and reliable tool for the automatic quantification of the MDS-UPDRS Leg Agility task. In perspective, this tool is part of a larger monitoring program to be carried out during activities of daily living, and managed by the patients themselves.
Keyphrases
- deep learning
- artificial intelligence
- neural network
- machine learning
- lower limb
- big data
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- ejection fraction
- prognostic factors
- peritoneal dialysis
- cross sectional
- electronic health record
- mass spectrometry
- patient reported outcomes
- clinical practice
- atomic force microscopy