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Table 4 Effect of sensor set on model performance

From: Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Sensor Set

Tremor

Bradykinesia

Binary

Multiclass

Binary

Multiclass

Combo

0.78 (0.70–0.86)

0.76 (0.68–0.83)

0.67 (0.61–0.74)

0.65 (0.59–0.71)

Accel

0.77 (0.67–0.87)

0.74 (0.65–0.82)

0.63 (0.57–0.70)

0.63 (0.57–0.68)

Gyro

0.79 (0.74–0.85)

0.77 (0.72–0.82)

0.68 (0.61–0.75)

0.64 (0.59–0.70)

Watch

0.79 (0.69–0.89)

0.77 (0.68–0.86)

0.63 (0.56–0.69)

0.61 (0.56–0.66)

  1. Average and 95% confidence intervals of model performance (AUROC) to classify PD symptoms using different sensor sets