1. Precision Health
1.1. Personalize Health
1.1.1. Omics Technologies
1.1.2. Telemedicine
1.1.3. computational methods
1.1.4. biomarkers and discovery
1.1.5. ML and AI
1.2. Environmental Health
1.3. Social Health
2. Approach
2.1. 4E and Evolutionary Biology
2.1.1. Embodied
2.1.1.1. Interoception
2.1.1.1.1. parts
2.1.1.1.2. disorders
2.1.1.1.3. Interoceptive Illusion
2.1.1.1.4. Entrainment
2.1.1.1.5. Artificial sensation
2.1.1.1.6. Emotional Augmentation
2.1.1.2. Exteroception
2.1.1.3. Proprioception
2.1.2. Embedded
2.1.3. Enactive
2.1.4. Extended
2.2. Gaia hypothesis
2.3. Decentralized Science
3. Tools
3.1. Metaverse
3.1.1. VR
3.1.2. Digital Twins
3.1.3. Augmented reality
3.2. Data
3.2.1. Biodata
3.2.1.1. Untitled
3.2.1.1.1. Demographic data
3.2.1.1.2. vital signs
3.2.1.1.3. lab result
3.2.1.1.4. medical history
3.2.1.1.5. Omics
3.2.1.1.6. Biosignals
3.2.1.1.7. Microbiome data
3.2.2. Sociology Data
3.2.2.1. Economy
3.2.2.2. Finance
3.2.2.3. Demography
3.2.2.4. Health
3.2.2.5. Education
3.2.2.6. Crime
3.2.2.7. Culture
3.2.2.8. environment
3.2.2.9. Agriculture
3.2.3. Biosphere Data
3.2.3.1. air quality data
3.2.3.1.1. measure pollutants such as particulate matter, ozone, and nitrogen dioxide
3.2.3.2. water quality data
3.2.3.2.1. pH, dissolved oxygen, turbidity, and temperature
3.2.3.3. climate data
3.2.3.3.1. measure temperature, precipitation, humidity, and other meteorological parameters
3.2.3.4. biodiversity data
3.2.3.4.1. identify plant and animal species using techniques such as acoustic monitoring, camera traps, or DNA sequencing
3.2.3.5. soil data
3.2.3.5.1. pH, temperature, and other parameters
3.2.3.6. energy sensors or dataaaaaaa
3.2.3.6.1. measure energy consumption, production, and prices of different sources
3.3. Data Devices
3.3.1. Bio-Data Devices
3.3.1.1. IoT wearables
3.3.1.1.1. Biosensing
3.3.1.2. Medical imaging
3.3.1.2.1. MRI
3.3.1.2.2. X-ray
3.3.1.2.3. CT scans
3.3.1.3. Lab-on-chip
3.3.1.4. Microfluidic
3.3.2. IoT Environmental Devices
3.3.3. IoT Social Devices
3.4. AI and Modeling
3.4.1. predictive modeling
3.4.2. computer vision
3.4.3. machine learning
3.4.4. FEP
3.4.4.1. For environment
3.4.4.2. For human
3.4.4.2.1. emotion
3.4.4.2.2. Schizophernia
3.4.4.3. For Society
3.4.5. Quantum
3.5. Intervention
3.5.1. Bio-Intervention
3.5.1.1. Physical
3.5.1.1.1. IoT wearable
3.5.1.2. Chemical
3.5.2. Environmental Intervention
4. Goals
4.1. Augmentation
4.1.1. Cognitive augmentation
4.1.2. Emotional augmentation
4.1.3. Sensory augmentation
4.2. Rehabilitation
4.2.1. Anxiety
4.2.2. PTSD
4.2.3. Depression
4.2.4. Insomnia
4.2.5. Eating and weight disorders
4.2.6. Chronic Pain
4.2.7. Addictions
4.2.8. Autism
4.2.9. OCD
4.3. Treatment
5. Application
5.1. Physical Rehabilitation
5.1.1. spinal cord injuries
5.1.2. amputations
5.2. Mental health
5.2.1. depression
5.2.2. anxiety
5.3. Performance enhancement
5.3.1. Artistic performance
5.3.2. Cognitive performance
5.3.3. Athletic performance
5.4. Sleep disorders
5.5. Emotion biofeedback
5.6. neurological disorders
5.6.1. Parkinson's disease
5.6.2. multiple sclerosis