Wearable Device

Wearable devices are transforming healthcare and human-computer interaction by enabling continuous monitoring of physiological and behavioral data. Current research emphasizes personalized models, often employing machine learning techniques like convolutional neural networks, recurrent neural networks (LSTMs), and gradient boosting, to analyze data from multiple sensor modalities (e.g., accelerometers, PPG, EEG) for applications ranging from activity recognition and fall detection to stress and emotion monitoring and even pain assessment. This field is significant due to its potential for improving early disease detection, personalized healthcare interventions, and enhancing the independence of individuals with disabilities, while also raising important considerations around data privacy and algorithmic fairness.