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. 2022 Jun 13;24(6):e34307. doi: 10.2196/34307

Table 8.

Studies classified by machine learning (ML) algorithms (N=15).

ML algorithm and accuracy References
Linear SVMa

Health: trunk compensation in 3 directions (AUC)b=99.15% [61]

Stroke (F1 score): NCc=0.88; SEd=0.86; TRe=0.80; LFf=0.81 [77]

Healthy group (AUC): NC=0.86; SE=0.68; TR=0.74; LF=0.98 and stroke group (AUC): NC=0.63; SE=0.27; TR=0.82; LF=0.92 [48]

Healthy participant (F1 score): NC=0.87; SE=0.15; TR=0.5; LF=0.74 and stroke survivor (F1 score): NC=0.94; SE=0; TR=0; LF=0 [49]

Healthy group (AUC): NC=0.98; SE=1.00; TR=0.99; LF=0.97 and stroke group (AUC): NC=1.00; SE=0.98; TR=0.85; LF=0.90 [53]

Stroke (F1 score): NC=0.990; SE=0.975; TR=0.983; LF=0.975 [54]

Stroke: offline (F1 score): NC=0.984; SE=1.000; TR=0.995; LF=0.963 and on the web: participant 1 (F1 score): NC=0.978; SE=1.000; TR=0.929; LF=1.000; participant 2 (F1 score): NC=0.994; SE=1.000; TR=1.000; LF=0.984 [45,91]

Stroke: trunk flexion (AUC)=78.2% [55]
k-NNg

Health: trunk compensation in 3 directions (AUC)=97.9% [61]

Stroke (F1 score): NC=0.79; SE=0.78; TR=0.70; LF=0.73 [77]

Health: correct vs incorrect (involving typical compensatory movements) upper limb exercises (sensitivity and specificity): garment 1: 86%, +6% to –6% vs 79%, +7% to –7%; garment 2: 89%, +6% to –6% vs 93%, +5% to –5%; garment 3: 87%, +4% to –4% vs 84%, +4% to –4% [78]

Health: 3 incorrect compensatory positions (not specified) in UEh adduction exercise (k value): pos_run1=0.78, pos_run2=0.82, pos_run3=0.79, pos_run4=0.81 [79]

Stroke (F1 score): NC=0.989; SE=0.970; TR=0.983; LF=0.981 [54]
Naïve Bayes

Health: trunk displacement (precision and Recall)—non-compensatory=92.7% and 90.5% and compensatory=88.6% and 91.2% [43]

Stroke: trunk compensatory movements in anterior and posterior direction (precision)—Horizontal Reach: unaffected arm=100%, affected arm=87.5%; Vertical Reach: unaffected arm=87.5%, affected arm=100%; Card Flip: unaffected arm=62.5%, affected arm=66.7%; Jar Open: unaffected arm=71.4%, affected arm=71.4% [44]
Logistic regression

Healthy: trunk compensation in 3 directions (AUC)=83% [61]

Health: 3 incorrect compensatory positions (not specified) in UE adduction exercise (k value): pos_run1=0.82, pos_run2=0.85, pos_run3=0.88, pos_run4=0.89 [79]
Random Forest Healthy: trunk compensation in 3 directions (AUC)=96% [61]
Multilabel k-NN Stroke (F1 score): NC=0.73; SE=0.53; TR=0.67; LF=0.69; insufficient elbow extension=0.73 [74]
Multilabel decision tree Stroke (F1 score): NC=0.69; SE=0.50; TR=0.60; LF=0.68; insufficient elbow extension=0.80 [74]
Generative adversarial network k-NN Stroke (F1 score): NC=0.94; SE=0.95; TR=0.93; LF=0.96 [77]
Sequential minimal optimization Stroke: trunk compensatory movements in anterior and posterior direction (precision)—horizontal reach: unaffected arm=85.7%, affected arm=87.5%; vertical reach: unaffected arm=100%, affected arm=100%; Card Flip: unaffected arm=62.5%, affected arm=66.7%; Jar Open: unaffected arm=57.1%, affected arm=57.1% [44]
Decision tree J48 Health: 3 incorrect compensatory positions (not specified) in UE adduction exercise (k value): pos_run1=0.64, pos_run2=0.81, pos_run3=0.82, pos_run4=0.81 [79]
Recurrent Neural Network Healthy group (AUC): NC=0.87; SE=0.79; TR=0.84; LF=0.98 and stroke group (AUC): NC=0.66; SE=0.27; TR=0.81; LF=0.77 [48]
Weighted random Forest Healthy participant (F1 score): NC=0.87; SE=0.15; TR=0.5; LF=0.74 and stroke survivor (F1 score): NC=0.94; SE=0; TR=0; LF=0 [49]
Cost sensitive Healthy participant (F1 score): NC=0.83; SE=0.09; TR=0.19; LF=0.68 and stroke survivor (F1 score): NC=0.94; SE=0; TR=0; LF=0 [49]
Random Undersampling Healthy participant (F1 score): NC=0.71; SE=0.29; TR=0.48; LF=0.72 and stroke survivor (F1 score): NC=0.69; SE=0.04; TR=0.20; LF=0.07 [49]
Tomek links Healthy participant (F1 score): NC=0.79; SE=0; TR=0; LF=0 and stroke survivor (F1 score): NC=0.94; SE=0; TR=0; LF=0 [49]
SMOTEi Healthy participant (F1 score): NC=0.72; SE=0.3; TR=0.49; LF=0.82 and stroke survivor (F1 score): NC=0.83; SE=0.06; TR=0.25; LF=0.01 [49]
SVM SMOTE Healthy participant (F1 score): NC=0.66; SE=0.28; TR=0.49; LF=0.73 and stroke survivor (F1 score): NC=0.8; SE=0.04; TR=0.24; LF=0.05 [49]
Random oversampling Healthy participant (F1 score): NC=0.77; SE=0.32; TR=0.51; LF=0.63 and stroke survivor (F1 score): NC=0.8; SE=0.04; TR=0.23; LF=0.07 [49]
Binary classification Healthy participant (AUC)—good example: SE=0.94; TR=0.97; LF=0.92; bad example: SE=0.37; TR=0.63; LF=0.52 [50]

aSVM: support vector machine.

bAUC: area under the curve.

cNC: no compensation.

dSE: shoulder elevation.

eTR: trunk rotation.

fLF: lean forward.

gk-NN: k-nearest neighbor.

hUE: upper extremity.

iSMOTE: synthetic minority oversampling technique.