Fig. 5: Data classification via machine learning and estimation of metabolic costs and physical effort. | npj Flexible Electronics

Fig. 5: Data classification via machine learning and estimation of metabolic costs and physical effort.

From: Soft wearable flexible bioelectronics integrated with an ankle-foot exoskeleton for estimation of metabolic costs and physical effort

Fig. 5

a A confusion matrix showing data from the IMU sensor classifying six different motions (running, elevated running, walking, elevated walking, standing, and squatting) with an overall accuracy of 88%. b Flow chart representing a spatial CNN model with five layers of convolutions with filters of decreasing the dimension size and two layers of average pooling. c Metabolic rate from oxygen intake and carbon dioxide exhale measured with the calorimetric respiratory mask. Raw data are filtered with a bandpass filter, and steady-state values are used to compare with normalized HRV-RMSSD. d Normalized physical effort (PE) compared with normalized HRV-RMSSD where PE represents the quantitative description of hardness for each trial by subjects measured using the Borg perceived exertion scale (6–20). The measured Pearson R correlation is −0.689 (p-value: 6.6e−6).

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