Fig. 2: Overview of our domain-knowledge-guided self-supervised contrastive learning (SSCL) framework for decoding wearable time series data. | Communications Engineering

Fig. 2: Overview of our domain-knowledge-guided self-supervised contrastive learning (SSCL) framework for decoding wearable time series data.

From: Transforming label-efficient decoding of healthcare wearables with self-supervised learning and “embedded” medical domain expertise

Fig. 2

a Offline domain feature extraction and clustering. Before training, domain features are extracted from raw input signals and clustered using k-means to assign a prototype index to each sample. b Augmentation and feature extraction. During training, two augmented views (Aug 1 and Aug 2) are generated per sample and passed through a shared encoder to obtain deep features. c Instance-level contrast. In addition to treating paired augmented views as a positive pair (marked in black), the nearest neighbor in the domain feature space is selected, and its two augmented views (marked in purple) are included as additional positive. d Prototype-level contrast. Each sample is encouraged to align with its domain-informed prototype representation, which is updated online using the encoded features of all assigned samples.

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