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Table 2 Proposed methods in the literature for estimating the severity of PD represented by UPDRS III

From: Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III

Reference PwP Sensors
location
Method Unobtrusive Estimated
metric
Gold-standard
label
Validation
Method
r MAE
Griffiths et al. [8] 25 Wrist Statistical approach Yes Bradykinesia
score
UPDRS III Held-out
testing set
0.64 18
Parisi et al. [17] 34 Chest, left and right thighs Multiple k-Nearest
Neighbors models to estimate LA, S2S
and G.
No
(task-dependent)
Sum of leg
agility, sit-to- stand and gait items of UPDRS III
Sum of leg agility, sit-to- stand and gait items of UPDRS III LOOCV 0.79 -
Rodriguez-Molinero et al.
[15]
75 Waist Linear regression No
(task-dependent)
Gait item of
UPDRS III
UPDRS III Held-out
testing set
-0.56 -
Pulliam et al. [13] 13 Wrist and ankle Multiple linear regression models to estimate tremor, bradykinesia and dyskinesia Yes Radar chart
of PD tremor,
bradykinesia and
dyskinesia
UPDRS III - 0.81 -
Zhan et al. [18] 152 Smartphone Rank-based framework for disease severity score [30] No
(task-dependent)
Mobile PD score UPDRS III Held-out
testing set
0.88 -
Abrami et al. [28] 60 Both wrists Clustering and Markov-Chain Yes Multi-dimensional scale Sum of tremor, bradykinesia and gait items of UPDRS III Held-out testing set \(r^2\) = 0.64
in clinic
\(r^2\) = 0.43
at home
Butt et al. [25] 64 Wrist, fingers, and foot Adaptive neuro- fuzzy inference system No
(task-dependent)
UPDRS III UPDRS III Tenfold
cross
validation
0.81 -
The developed approach in this study 24 Wrist and ankle Ensemble of dual- Channel LSTM, CNN-LSTM using raw signals and CNN-LSTM using spectrogram Yes UPDRS III UPDRS III LOOCV 0.79 5.95