Fig. 2: Augmentation and normalization improve the performance of the tapping accelerometer model. | Communications Biology

Fig. 2: Augmentation and normalization improve the performance of the tapping accelerometer model.

From: Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson’s disease

Fig. 2

a represents the model structure for the accelerometer data tapping model with seven convolutional layers, seven max-pooling layers, and 1 fully connected layer. b “Plain Model” represents the model using raw data without augmentation or normalization. “Norm” represents the model with Z-score normalization. “Quaternion Rotation” (QR), “Magnitude”, and “Time” denote the three types of augmentation methods. The data were pre-processed in sequential order of the methods shown above the plot. “All record” indicates the performance evaluated on the record level; “Average” and “Maximum” represent the individual level performance by using the average or the maximum prediction of all records of the same individual. The models with normalization and all augmentation methods showed the top performance.

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