Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Figures

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  Figure 1. Data acquisition workflow—from the human participant (left) to raw sensor data (right).

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  Figure 2. Data pre-processing phase—from raw sensor data (left) to sensor data ready to analyse (right).

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  Figure 3. Data analysis workflow—from sensor data ready to analyse (left) to visualizations of exploratory plots and quantitative analysis results to deriving conclusions (right).

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  Figure 4. Participant RP 5–14: time plot (a), scatter plot (b), and cross-correlation plot (c), and cross-correlation plot of moving averages (d) of heart rate HR [beats per minute] measured by Bioharness3 BH sensor and VarioPort VP.

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  Figure 5. Participant RP 3–8: time plots (a), scatter plot (b), cross-correlation plot (c), and cross-correlation plot of moving averages (d) of galvanic skin response GSR measured by Empatica E4 and VarioPort.

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  Figure 6. R2 matrix of pairs of parameter and participants; detail (a) complements Figure 4, detail (b) complements Figure 5; total average of individual pairs among participants is shown in the last column; total average of participants among individual parameter pairs is shown in the last row of each parameter group; colour: red indicates high correlation, blue indicates low correlation.

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  Figure 7. Cross-correlation matrix of pairs of parameters and participants; detail (a) complements Figure 4, detail (b) complements Figure 5; colour: orange indicates positive cross-correlation, blue indicates negative cross-correlation; a cell detail shows lags as small horizontal bars: lag −15 at top, and lag +15 at bottom.

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  Figure 8. Maximum Information Coefficient (MIC) cluster matrix of pairs of parameters (size show averages among all participants, moving averaged versions only); colors: blue (cluster 1): low correlations; orange (cluster 2): moderate correlations; green (cluster 3): high correlations; symbol size in a matrix cell: average MIC among participants; (a–f) highlight special characteristics described in the text.

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  Figure 9. MIC participant-level matrix of pairs of parameters (moving averages only); details of a matrix cell show participants as small vertical bars (order of participants is shown in the legend).

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  Figure 10. MIC–R2 cluster matrix of pairs of parameters (moving averages only); colors: green (cluster 1): “false” linear relationships; yellow (cluster 2): “true” linear relationships; red (cluster 3): functional but not linear relationships.

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  Figure 11. MIC–R2 individual matrix of pairs of parameters (moving averages only); details of a matrix cell show participants as small vertical bars (order of participants is shown in the legend); back arrow points to pairs of parameters with very weak association.

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  Figure 12. Global Fréchet distance matrix of pairs of parameter and participants; detail (a) complements Figure 4, detail (b) complements Figure 5; color: green indicates low distance thus high similarity, blue indicates high distance thus low similarity.