empatica e4 bracelet  (Empatica Inc)

Journal: Biosensors

Article Title: Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review

doi: 10.3390/bios15040202

Figure Lengend Snippet: Biosensing papers reviewed focusing on stress. The data collection environment (Env.) is specified as IL (In-Lab) or IW (In-the-Wild).

Article Snippet: (Campanella et al., 2023) [ ] Stress detection , 29 , ❖ Empatica E4 bracelet HRV derived from PPG and EDA/GSR , ❖ Binary classification ● Supervised ML ● 71% precision, 60% recall, and 65% F1-score (random forest (RF)) , IL , ● Labels: stress/no-stress classifications during cognitive, social, and physiological stress tasks ● 2 labels/participant (stressed and non-stressed) ● Monitoring: approximately 15–20 min per task , ● Unbalanced data impacts classifier performance ● Small sample size and lab setting limit generalizability ● Noise from Empatica E4 and lack of real-life stressors reduce accuracy ● Larger, diverse samples and varied stressors needed for robustness.

Techniques: Derivative Assay, Activity Assay, Plasmid Preparation, Biomarker Discovery, Introduce, Battery, Transmission Assay, Control, Standard Deviation, Selection

Journal: Biosensors

Article Title: Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review

doi: 10.3390/bios15040202

Figure Lengend Snippet: Biosensing papers reviewed focusing on depression.

Article Snippet: (Campanella et al., 2023) [ ] Stress detection , 29 , ❖ Empatica E4 bracelet HRV derived from PPG and EDA/GSR , ❖ Binary classification ● Supervised ML ● 71% precision, 60% recall, and 65% F1-score (random forest (RF)) , IL , ● Labels: stress/no-stress classifications during cognitive, social, and physiological stress tasks ● 2 labels/participant (stressed and non-stressed) ● Monitoring: approximately 15–20 min per task , ● Unbalanced data impacts classifier performance ● Small sample size and lab setting limit generalizability ● Noise from Empatica E4 and lack of real-life stressors reduce accuracy ● Larger, diverse samples and varied stressors needed for robustness.

Techniques: Activity Assay, Expressing, Control, Introduce, Derivative Assay, Biomarker Discovery, Diagnostic Assay, Selection

Journal: Scientific Data

Article Title: Speech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals

doi: 10.1038/s41597-024-03991-w

Figure Lengend Snippet: Dataset summary.

Article Snippet: Technology Types(s) , ECG - FAROS sensor; EDA - EMPATICA E4 bracelet; speech - microphones SHURE SM-35XLR7 headset/XY microphone connected to Zoom H4n recorder; Video sequences - Panasonic HC-VX9805 cameras; Stress - PSS14/STAI Y2; Cognitive load - NASA TLX.

Techniques: Activity Assay

Journal: Scientific Data

Article Title: Speech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals

doi: 10.1038/s41597-024-03991-w

Figure Lengend Snippet: Available data streams.

Article Snippet: Technology Types(s) , ECG - FAROS sensor; EDA - EMPATICA E4 bracelet; speech - microphones SHURE SM-35XLR7 headset/XY microphone connected to Zoom H4n recorder; Video sequences - Panasonic HC-VX9805 cameras; Stress - PSS14/STAI Y2; Cognitive load - NASA TLX.

Techniques: Activity Assay

Journal: Data in Brief

Article Title: Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset

doi: 10.1016/j.dib.2024.110303

Figure Lengend Snippet: Empatica E4 data description.

Article Snippet: The Empatica E4 smart bracelet was employed to capture signals measuring individuals' physiological data.

Techniques: Activity Assay

Journal: Data in Brief

Article Title: Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset

doi: 10.1016/j.dib.2024.110303

Figure Lengend Snippet:

Article Snippet: The Empatica E4 smart bracelet was employed to capture signals measuring individuals' physiological data.

Techniques: Sampling, Activity Assay