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The 1D CNN architecture for the pretext task.

Journal: Applied sciences (Basel, Switzerland)

Article Title: Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

doi: 10.3390/app132112035

Figure Lengend Snippet: The 1D CNN architecture for the pretext task.

Article Snippet: They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [ ].

Techniques:

The 1D CNN architecture for fine-tuning the pre-trained network towards the downstream stress-prediction task.

Journal: Applied sciences (Basel, Switzerland)

Article Title: Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

doi: 10.3390/app132112035

Figure Lengend Snippet: The 1D CNN architecture for fine-tuning the pre-trained network towards the downstream stress-prediction task.

Article Snippet: They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [ ].

Techniques:

Number of days in the sample, MASH model used, and sleep–wake interval size

Journal: Journal of activity, sedentary and sleep behaviors

Article Title: Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep–wake cycles

doi: 10.1186/s44167-023-00017-5

Figure Lengend Snippet: Number of days in the sample, MASH model used, and sleep–wake interval size

Article Snippet: The AUC-ROC for both 1D CNN models developed for MASH (ActiGraph + Actiwatch or ActiGraph-only) were considered excellent ( ) with values of 0.991 and 0.983 for the ActiGraph + Actiwatch and ActiGraph-only models, respectively.

Techniques: