6. Ecosystem Roles and Incentive System

6.1 Light Nodes

Light nodes are the fundamental units in the VitaWatch decentralized network, responsible for supporting data storage, data transfer, and basic computational tasks. They play a crucial role in ensuring the smooth operation of the VitaWatch network, participating in the management of health data through distributed computing and data validation.

• Data Storage and Distribution: Light nodes participate in the decentralized storage network, dispersing encrypted health data across multiple locations to ensure redundancy and high availability of the data.

• Data Validation and Integrity Maintenance: Light nodes are responsible for executing data integrity verification, ensuring that health data remains consistent and reliable during storage and use.

• Health Data Transfer: Light nodes assist in forwarding and circulating data, facilitating the efficient flow of data within the VitaWatch network.

• Basic Computational Support: Light nodes also handle some basic computational tasks, such as pre-processing health data and performing simple inferences, optimizing AI computation tasks.

To become a light node, users need to stake a certain amount of $Vita tokens and continuously provide storage and computational resources to participate in the VitaWatch network. As light node participants, they will receive $Vita token rewards for contributing their computing and storage resources.

6.2 Super Nodes

Super nodes undertake more complex and advanced computational tasks in the VitaWatch network, responsible for processing AI model training, health data analysis, and other advanced computational tasks. They are the core of the decentralized health data network, ensuring the efficiency of data analysis and computation, as well as data privacy protection.

• Advanced Computational Tasks: Super nodes participate in the decentralized training of AI health models and handle complex data analysis tasks, such as health trend prediction and personalized health optimization.

• Privacy Computing and Encrypted Analysis: Super nodes use Fully Homomorphic Encryption (FHE) schemes to perform advanced computations on health data without decrypting it, ensuring data privacy.

• Data Validation and Quality Management: Super nodes are responsible for verifying high-value data, ensuring its quality while protecting user privacy.

• Ecosystem Governance and Decision-Making: Super nodes hold greater governance weight, participating in the formulation and adjustment of network rules.

To become a super node, users need to stake $Vita tokens and provide advanced computational resources to handle more complex data computation tasks. Super nodes will receive $Vita token rewards based on their contribution to computation and data validation work.

6.3 Third-Party Data Seekers

In VitaWatch's decentralized health data marketplace, third-party data seekers include healthcare institutions, insurance companies, pharmaceutical companies, health research organizations, and more. With user authorization, they can access health data from the decentralized data marketplace for various applications such as health research, personalized healthcare, insurance risk management, AI model training, and others.

• Data Access and Privacy Protection: When accessing health data, data seekers process the data using Fully Homomorphic Encryption (FHE) schemes, ensuring that the necessary analysis and decision-making can be carried out without decrypting user data. This not only addresses data privacy concerns but also complies with global health data protection regulations, ensuring the lawful use of the data.

• Data Sharing and Market Incentives: After user authorization, health data can be used by third-party data seekers, promoting the development of health research and personalized health services. Data seekers pay corresponding fees to access the computational results, ensuring the fairness and transparency of the data transaction process.

• Health Research and Personalized Healthcare: Healthcare institutions and research organizations can utilize the health data acquired from the VitaWatch network to conduct epidemiological studies, clinical trials, and treatment plan evaluations, fostering innovation in medical science and technology. Insurance companies can use the data to optimize risk management models and create more personalized insurance plans.

6.4 Healthy to Earn Mechanism

The Healthy to Earn mechanism is a core feature of VitaWatch, aimed at rewarding users for healthy behaviors and data contributions, encouraging active participation in health management and data sharing. Through this mechanism, users can earn rewards for healthy behaviors (such as exercise, diet management, etc.) and for contributing their health data. This mechanism combines smart contracts and decentralized ecosystem design to ensure that reward distribution is fair, transparent, and efficient.

1. User Exercise Improvement Health Rewards

User exercise behavior not only contributes to health management but also earns them $Vita token rewards. Users will receive progressively increasing rewards based on their daily exercise goal completion and long-term health improvement progress. Rewards will be dynamically calculated based on the user's activity data and health progress, and the incentive formula below will determine the points and rewards for each reward cycle.

Incentive Formula

Where:

R: The number of $Vita tokens earned by the user in a specific period (e.g., daily, weekly, monthly).

A: The user's actual activity level (steps, exercise duration, calories burned, etc.).

C: The baseline reward coefficient for the activity level. This varies based on the activity type and intensity as set by VitaWatch.

S: The user's health improvement score (calculated based on exercise results, weight changes, health indicator changes, etc.).

F: The health improvement score coefficient, which measures the specific contribution of exercise to health. This coefficient increases as the user's health improves.

H: The user's health improvement percentage. Based on their long-term exercise data and health assessment model, it reflects the degree of improvement in the user's health status.

VitaWatch's incentive mechanism offers different rewards based on the user's exercise intensity, health improvements, and long-term efforts, ensuring that each user's input and reward are proportional.

2. User Data Contribution Rewards

In addition to improving health through exercise, users can also earn rewards by contributing their health data. Health data includes, but is not limited to, exercise data, sleep data, diet data, and more. The user's data will be contributed to health research, medical research, AI model training, and other scenarios in a decentralized manner, while ensuring privacy protection. The higher the quality of the health data contributed by the user, the greater the reward they will receive.

Incentive formula:

Rd: The $Vita token reward that the user receives for contributing health data. Di: The score value of each health data contribution (the scoring criteria vary based on data type and quality). Wi: The weight of each type of data, depending on its scarcity and demand in application scenarios. For example, medical research institutions may have a higher demand for certain specific data (e.g., blood sugar, heart rate) and assign higher weight to such data. P: The overall contribution percentage of the data contribution, calculated based on the amount and quality of data contributed by the user. n: The number of data types, typically including steps, diet, sleep, weight, etc.

Reward Details

• Data Quality Evaluation: Each health data (e.g., exercise, sleep, diet) is scored based on its quality. The higher the quality of the data, the higher the score. High-quality data will be given a premium based on demand, ensuring that users who contribute valuable data receive more rewards.

• Data Demand and Contribution: Different data types will be assigned different weights based on demand and application scenarios. High-demand, scarce data (e.g., long-term disease management data, specific health data) will receive a higher reward coefficient.

• Long-term Contribution Incentive: Users who continuously contribute data will have their cumulative contribution increase their reward ratio. As the amount of contribution grows, users will enter higher reward tiers and receive more $Vita tokens.

6.5 Anti-Cheating Mechanism VitaWatch's anti-cheating mechanism is dedicated to maintaining the fairness and transparency of the Healthy to Earn reward system, ensuring that every user's efforts and contributions are rewarded fairly. Below are VitaWatch's anti-cheating strategies within the incentive system, covering data verification, behavior analysis, anomaly detection, and decentralized validation.

1. Data Authenticity Verification To prevent users from falsifying data or using improper methods to earn rewards, VitaWatch employs a multi-layered data authenticity verification mechanism. All health data (such as steps, exercise duration, heart rate, etc.) must undergo strict verification to ensure its authenticity and reliability.

• Data Source Verification

VitaWatch ensures that all user data comes from legitimate and valid sources that meet device specifications. Through device certification and data encryption, it guarantees that the transmitted data originates from the user's actual VitaWatch watch or certified smart devices. Any uncertified external data sources or falsified data will not pass verification.

• Data Consistency Check

The platform will conduct data consistency checks by comparing user-uploaded data with real-time data to detect any unusual fluctuations. For example, the increase in steps must match the exercise time and intensity. If any user-uploaded data does not align with their health status (such as abnormal step counts or fluctuating heart rates), the system will automatically flag it as suspicious, further subjecting it to manual review or automatic exclusion.

2. Behavioral Pattern Analysis and Machine Learning To identify and prevent fraudulent behavior, VitaWatch uses behavioral pattern analysis and machine learning algorithms to analyze user health activity data and detect abnormal behavior patterns.

• Anomaly Detection

Based on historical data and the user's personal health status, the VitaWatch system will monitor and identify whether the user's health activities are abnormal in real-time. For example, if a user's exercise or step count suddenly increases without corresponding health improvements (such as weight loss or increased endurance), it could be flagged as anomalous data. The system ensures that each user's activity data aligns with their actual situation through multi-dimensional health data comparison and analysis.

• Dynamic Health Baseline Adjustment

VitaWatch adopts dynamic health baselines to establish personalized health activity patterns for each user. As the user's health condition changes, the baseline will adjust accordingly. Through dynamic baseline adjustments, the system can more accurately identify normal health behaviors and avoid misjudging regular fitness users.

3. Decentralized Verification of Anomalous Data and Behavior To further ensure the fairness of the Healthy to Earn reward mechanism, VitaWatch employs decentralized verification nodes (DePIN nodes) to check the authenticity and integrity of health data.

• Decentralized Data Auditing

When the system detects anomalous data, multiple nodes in the decentralized network will audit and verify the data. These nodes use encryption algorithms, data validation, and behavioral pattern analysis to ensure the legitimacy of the data. Through joint review by multiple nodes, it ensures that no single node can influence the verification process, thus preventing malicious attacks or data manipulation.

• Data Validation Node Election

The election mechanism for decentralized validation nodes ensures the transparency and fairness of data verification. Each piece of health data, especially data related to rewards, will be randomly validated by verification nodes, ensuring no human intervention or cheating. Only when the data is validated by multiple nodes will it be accepted and reward calculations be made.

4. Real-Time Monitoring and Manual Intervention In addition to automatic detection, the VitaWatch system also provides a real-time manual intervention mechanism to ensure that every user's reward is fair.

• User Behavior Monitoring

VitaWatch will regularly review users' activity records and data contributions, using real-time monitoring tools in the system backend to detect any ongoing anomalous behavior or data submission. If abnormalities are found, the platform will notify the user immediately and pause reward calculations until the investigation is completed.

• Manual Review and Warning Mechanism

For behaviors flagged by the system as suspicious, VitaWatch will first conduct a manual review to determine if it constitutes fraudulent behavior. Until clear evidence of cheating is found, the system will freeze the related rewards and issue warnings or disable the reward eligibility for violators. In severe cases, users may be removed from the ecosystem network.

5. System Transparency and User Feedback VitaWatch ensures the transparency of all reward mechanisms and anti-cheating measures, and provides users with clear feedback channels.

• Transparent Data Reports

Users can view their health data, task progress, and reward history at any time, along with related validation and audit records. All processes related to reward calculations and data verification are publicly accessible to ensure the fairness of the platform.

• User Appeal Mechanism

If users have concerns about reward distribution or data review results, they can submit an appeal through the platform's provided mechanism. The platform will offer fair arbitration and feedback channels to ensure that the rights of each user are protected.