Fig. 2. The decision-making unit and its working principles.

a | Conceptualization of the data pipeline. The combination and processing of multiple wearables with multiple sensing strategies provides access to physiologically relevant parameters and biomarkers to better explain the non-linearity in human physiology. The black and red lines indicate the data processing and model training pathways, respectively. b | Overview of data-driven methods. Post-processing of big data to explore the complex links between the measured signals and physiological status of individuals is possible with machine learning algorithms. ANN, artificial neural network; DT, decision tree; GDBSCAN, generalized density-based spatial clustering of applications with noise; GM, Gaussian means; HC, hierarchical clustering; kNN, k-nearest neighbours; RF, random forest; SVM, support-vector machines. Panel a (top part) adapted from ref.¹⁴, Springer Nature Limited.