II. Data preprocessing and Feature Extraction:
II.1 Hand Crafted Features for both ECG and GSR signals:
Table. I. Hand crafted features are extracted from ECG and GSR data.
II.2 Deep Features for ECG signals:
Fig.1 Framework of 1D convolutional layer based Autoencoder.(Conv1D-Autoencoder)
Fig. 2. Framework of LSTM based Autoencoder. (LSTM-Autoencoder)
Fig.3 Framework of Transformer based Autoencoder (Transformer-Autoencoder)
Example of reconstruction from Transformer-based Autoencoder
Example of reconstruction from Conv-1D based Autoencoder
Fig. 4 Example of reconstruction: red the original signal, green: the reconstructed signal
III. Other daily life stress and mental health related open datasets
Swell :The SWELL-KW dataset contains data from 25 participants (~3 hours each), for working under 3 conditions: neutral, interruptions and time pressure (plus a relax phase). The listed raw and pre-processed sensor data, as well as a feature dataset (aggregated per minute) are available: Besides sensor data, we provide the questionnaire ratings of the participants on task load (NASA-TLX), mental effort (RSME), emotion (SAM) and perceived stress for each working condition.
TILES: The Tracking Individual Performance with Sensors (TILES) is a project holding multimodal data sets for the analysis of stress, task performance, behavior, and other factors pertaining to professionals engaged in a high-stress workplace environments. Biological, environmental, and contextual data was collected from hospital nurses, staff, and medical residents both in the workplace and at home over time. Labels of human experience were collected using a variety of psychologically validated questionnaires sampled on a daily basis at different times during the day. The data sets are publicly available and we encourage researchers to use it for data mining and testing their own human behavior models. For full descriptions of the data sets, please refer to the following papers:
Student Life: StudentLife is the first study that uses passive and automatic sensing data from the phones of a class of 48 Dartmouth students over a 10 week term to assess their mental health (e.g., depression, loneliness, stress), academic performance (grades across all their classes, term GPA and cumulative GPA) and behavioral trends (e.g., how stress, sleep, visits to the gym, etc. change in response to college workload -- i.e., assignments, midterms, finals -- as the term progresses).
CrossCheck: The CrossCheck study collected year-long data from 75 patients with schizophrenia using smartphones. Information such as 3-axis acceleration, light levels, sound levels, GPS location, and call/SMS metadata was recorded. Stress labels were collected via EMA, and participants reported their stress levels in a 4-point scale.
Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano, "More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors", the 44th International Engineering in Medicine and Biology Conference, 2022 [arxiv]
Han Yu, Thomas Vaessen, Inez Myin-Germeys, Akane Sano, "Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild", 9th International Conference on Affective Computing & Intelligent Interaction (ACII 2021) [arxiv] [code] [IEEE]
Han Yu, Akane Sano, "Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild", [arxiv]
The SMILE dataset publication: To Be Announced.