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Table 3 Overview of accelerometry-based methods

From: Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review

Authors Population Method Validity Quality Quantity Activity
Lau et al [34] Stroke SVM, MLP, RBF Leave-one-subject-out method - - Walking
Barth et al [31] PD Boosting with decision stump as weak learner, LDA, and SVM with linear and RBF kernel Leave-one-subject-out method x x Walking, foot circling, and heel-toe tapping
Cancela et al [32] PD k NN, Parzen, Parzen density, binary decision tree, Bpxnc train NN by back-propagation, and SVM Cross-validation x - Daily activities (i.e. walking, lying, sitting, drinking a glass of water, opening and closing a door)
Salarian et al [32] PD Logic Regression model with Mamdani fuzzy rule-based classifier Cross-validation - x sit-to-stand and stand-to-sit
Zwartjes et al [45] PD Decision tree Leave-one-subject-out method x - lying, sitting, standing, and walking
Yang et al [43] PD Autocorrelation method Video recordings - x Walking
Motoi et al [39] Stroke Sagittal angle changes   - - Walking and sit-to-stand
Moore et al [38] PD Mathematical step-length algorithm Pen techniques and video recordings x x Walking
Dobkin et al [33] Stroke Naive Bayes classifier in combination with Gaussian discretization followed by a maximum likelihood estimation Stopwatch - x Walking
  1. Abbreviations: LDA Linear Discriminant Analysis, SVM Support Vector Machines, RBF radial basis function neural network, K-NN K-nearest neighbour, NN Neural Network, MLP multi-layer perception; Quality, methods assessing severity levels, Quantity, methods able to distinguish healthy from non-healthy subjects.