This paper is in the following e-collection/theme issue:

Foundations of AI (47) Artificial Intelligence (1556)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52171, first published .
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

Authors of this article:

Joe Li1 Author Orcid Image ;   Peter Washington1 Author Orcid Image
Article Authors Cited by (6) Tweetations (1) Metrics

Journals

  1. Mohajelin F, Sheykhivand S, Shabani A, Danishvar M, Danishvar S, Lahijan L. Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks. Sensors 2024;24(18):5883 View
  2. Mounesi Rad S, Danishvar S. Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks. Biomimetics 2024;9(9):562 View
  3. Li S, Fan C, Kargarandehkordi A, Sun Y, Slade C, Jaiswal A, Benzo R, Phillips K, Washington P. Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing. AI 2024;5(4):2725 View
  4. Benouis M, Andre E, Can Y. Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study. JMIR Mental Health 2024;11:e60003 View

Books/Policy Documents

  1. Mitra U, Rehman S. Wearable Devices and Smart Technology for Educational Teaching Assistance. View