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Found 4 papers, 1 papers with code
Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned
The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior.
A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors
In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0. 863 for the detection of puffs and an F1-score/Jaccard index equal to 0. 878/0. 604 towards the temporal localization of smoking sessions during the day.
A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches
The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior.
Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses.
