Quantified Self
See also: Quantified self, self-tracking with technology, self-tracking tools, self-knowledge through numbers, lifelogging
TLDR: Quantified Self is a movement that involves self-tracking through wearable devices and mobile apps to gather data about personal habits, health, and performance. By leveraging data analytics and cloud databases, it empowers users to make informed decisions for better health and productivity.
The concept of Quantified Self originated in 2007 and gained popularity with the rise of wearable devices like the Fitbit and Apple Watch. These devices collect data on metrics such as steps, heart rate, and sleep patterns, storing this information in key-value and SQL databases for analysis and visualization.
Quantified Self relies heavily on AI and data science techniques to extract meaningful insights from the data collected. Advanced platforms use predictive analytics to provide recommendations, helping users optimize their physical and mental well-being. These systems depend on robust query languages and cloud-based databases for seamless operation.
The integration of Azure services and platforms like Google Fit enhances the ability to synchronize and analyze self-tracking data across multiple devices. These systems ensure that the data is accessible and secure, making it valuable for personal use and collaboration with healthcare professionals.
The future of Quantified Self lies in the integration of IoT devices, biometrics, and advanced AI-driven solutions. By combining multiple data sources into unified cloud databases, users will gain deeper insights into their behaviors, leading to innovations in preventive healthcare and personal development.
Wearable: Quantified self - self-tracking with technology, self-tracking tools, self-knowledge through numbers, lifelogging. 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Bands, Pulse Volume Recording, Body Temperature Algorithms, Kinematic Activity Recognition, Augmented Body Performance, Skin-mounted Wearables, Remote Surgery Wearables, Wearable Health Clouds, Distributed Wearable Networks, Personalized AI Feedback, Long-term Health Monitoring, Adaptive Sport Wearables, Wearable Neural Interfaces, Medical-grade Data Integrity, Electrochemical Sensors, Gesture-triggered Commands, Haptic Adaptive Feedback, AI-enhanced Wellness, Precision Wearable Health Systems, Ecosystem-integrated Wearables, Real-time Signal Processing.
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