The field of mental health sensing and analysis is rapidly evolving, with a growing focus on developing innovative methods for detecting and monitoring mental health conditions such as depression and Parkinson's disease. Recent research has explored the use of multimodal approaches, combining data from various sources such as speech, text, and physiological signals to improve detection accuracy. Additionally, there is a increasing interest in using machine learning and deep learning techniques to analyze this data and provide personalized insights. Noteworthy papers in this area include the proposal of a Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for multimodal depression detection, which achieved a 10% improvement in accuracy over existing methods. Another notable paper presented a novel CustNetGC model for Parkinson's disease prediction, which achieved an accuracy of 99.06% and precision of 95.83%. Overall, these advancements have the potential to revolutionize the field of mental health sensing and analysis, enabling earlier intervention and more effective treatment.