The Human Intelligence Layer

A decentralized protocol for sourcing, processing, and distributing the world's first large-scale dataset of embodied human behavior.

Abstract

We propose the Human Intelligence Layer, a decentralized protocol designed to source, process, and distribute the world's first large-scale dataset of embodied human behavior. This foundational data layer addresses the critical bottleneck in next-generation AI—the missing human experience.

Through decentralized contribution, behavioral annotation, and cryptographic validation, Sentess creates a new category of AI-ready datasets that capture the full spectrum of human movement, emotion, and interaction patterns essential for the next paradigm of artificial intelligence.

The Human Experience Gap

Current AI systems lack fundamental understanding of human behavior, emotions, and embodied experiences. While AI excels at processing text, images, and structured data, it remains disconnected from the rich tapestry of human movement, interaction patterns, and emotional responses that define our species.

The proliferation of mobile devices has created an unprecedented opportunity to capture embodied human behavior at scale. However, this valuable data remains fragmented, unprocessed, and inaccessible—representing "human digital dark matter" that could unlock the next generation of AI.

Current Limitations

✗
Missing Human Context: AI lacks understanding of embodied human behavior, emotions, and interaction patterns.
✗
Fragmented Data: Human behavior data remains scattered across platforms, unprocessed and inaccessible.
✗
No Behavioral Foundation: No large-scale dataset exists for training AI on human movement and emotional responses.

The Sentess Solution

✓
Human Behavior Dataset: Creates the world's first large-scale dataset of embodied human behavior.
✓
Decentralized Collection: Leverages mobile devices worldwide to capture authentic human experiences.
✓
AI Foundation Layer: Provides the essential data layer for next-generation AI that understands humans.

Our Ecosystem

A decentralized network of human contributors, blockchain validators, and AI startups building the foundation for human-aware AI.

👥

Human Contributors

Individuals using the Sentess mobile application to capture their natural movements, behaviors, and emotional responses in daily activities.

🧠

Behavioral Processors

Smart contracts and AI systems that process, validate, and structure human behavior data into training datasets.

🤖

AI Consumers

AI companies, research institutions, and developers acquire human behavior datasets to train next-generation AI systems.

Protocol Workflow

1

Data Capture

Contributors capture "annotated moments" using their mobile devices, creating rich multimodal datasets and earn SENT.

2

Data Ingestion

Raw data is encrypted, hashed, and submitted to Solana network with privacy preservation.

3

SENT Network

Smart contracts process data, performing object recognition, scene segmentation, and motion analysis.

4

Verification & Consensus

Solana validators verify transactions from the SENT Network for final settlement on the blockchain.

5

Human Behavior Licensing

Verified human behavior datasets get licensed to AI companies and researchers. Revenue gets distributed to SENT holders and contributors.

Behavioral Intelligence Framework

Modeling human behavior using tensor products of behavioral spaces and probabilistic emotional mapping.

The State-Space of Human Behavior

We model a "behavioral moment" as a state in a high-dimensional behavioral space, ℋ_behavior. This space is constructed as the tensor product of individual behavioral spaces, each representing a different aspect of human experience:

ℋ_behavior = ℋ_movement ⊗ ℋ_emotion ⊗ ℋ_context ⊗ ℋ_interaction

Behavioral Dimensions

ℋ_movement: Physical movement patterns from accelerometer and gyroscope data
ℋ_emotion: Emotional state inferred from voice patterns, heart rate, and movement
ℋ_context: Environmental context from location, time, and activity patterns
ℋ_interaction: Social interaction patterns from communication and proximity data

Complete Representation

A complete behavioral moment |Ψ⟩ is a vector in ℋ_behavior. This representation captures complex correlations between different behavioral dimensions, enabling rich understanding of human experience.

Behavioral Annotation as Probabilistic Mapping

The core task of the Human Intelligence Engine is to map a raw behavioral state |Ψ⟩ to behavioral labels L = {l₁, l₂, ..., lₙ}. We model this as a probabilistic process using Bayes' theorem:

P(L|Ψ⟩) = P(|Ψ⟩|L) × P(L) / P(|Ψ⟩)

Components

P(|Ψ⟩|L): Likelihood of observing behavioral data given activity labels, learned by validator nodes using deep learning
P(L): Prior probability of behaviors, learned from the entire dataset forming a "human behavior model"
L*: Optimal behavior label set that maximizes posterior probability

Economic Model

Blockchain's consensus mechanism ensures network integrity and security.

Future Work

Sentess is exploring a novel consensus and incentive mechanism called Proof-of-Contribution. This mechanism rewards participants for actions that add value to a sovereign layer-1.

V_i = Σ_j=1^N (q_j × u_j × c_j)

Quality Score (q_j)

Determined by verification process, measuring annotation accuracy and data fidelity

Uniqueness Score (u_j)

Calculated using KL divergence, rewarding novel information compared to existing network data

Consensus Score (c_j)

Validation work consensus, ensuring distributed agreement on annotation quality

Proposal: Sentess (SENT)

The network's native token, Sentess (SENT), is used to reward participants proportional to their value contribution V_i. This creates a self-regulating economic system where participants are financially motivated to improve the overall intelligence of the network.

Token Utility

Contribution Rewards: Contributors earn SENT for providing high-quality sensor data
Validation Incentives: Validators earn SENT for accurate annotation and verification work
Market Access: Consumers purchase datasets using SENT tokens
Governance Rights: SENT holders participate in protocol governance decisions

Economic Mechanisms

Staking: Validators stake SENT to participate in consensus
Slashing: Malicious behavior results in stake reduction
Protocol Fees: Transaction fees distributed to active contributors
Inflation Schedule: Controlled token emission rewards network growth

Use Cases

Transformative applications across domains unlocked by structured, verifiable spatial data.

🤖

Embodied AI & Robotics

Training robots to navigate and interact with complex, real-world environments using rich spatial datasets that capture nuanced human-environment interactions.

🥽

Augmented Reality

Creating more realistic and context-aware AR experiences anchored to the physical world through comprehensive spatial understanding.

🏙️

Geospatial Analytics

Building high-fidelity, real-time 3D maps of cities for urban planning, simulation, and infrastructure optimization.

🌐

Decentralized AI

Providing foundational data infrastructure for transparent, unbiased AI models that understand physical reality.

Future Vision

The Human Intelligence Layer represents a paradigm shift in how we approach AI training data. By moving away from centralized, siloed data collection to a decentralized, incentivized protocol, Sentess creates a public good: the world's first large-scale dataset of embodied human behavior.

We don't just collect data. We capture human experience.

The Sentess protocol is not merely infrastructure for datasets; it is the foundational data layer for the next paradigm of AI—a future where intelligent systems truly understand human behavior, emotions, and embodied experiences.

Research Impact

Sentess establishes an entirely new category of decentralized infrastructure—The Human Intelligence Layer—that resolves the missing human experience bottleneck, transforming ubiquitous mobile device data into the world's first large-scale dataset of embodied human behavior for next-generation AI.

Foundational AI, Human Behavior, and Embodied Intelligence

SLAM (Simultaneous Localization and Mapping)

Durrant-Whyte, H., & Bailey, T. (2006). "Simultaneous Localization and Mapping (SLAM): Part I The Essential Algorithms." IEEE Robotics & Automation Magazine, 13(2), 99-110.

Provides foundational mathematical framework for spatial reconstruction and environment mapping from sensor data.

Visual SLAM Systems

Mur-Artal, R., Montiel, J. M. M., & Tardós, J. D. (2015). "ORB-SLAM: A Versatile and Accurate Monocular SLAM System." IEEE Transactions on Robotics, 31(5), 1147-1163.

Demonstrates feasibility of high-quality spatial reconstruction from ubiquitous camera sensors.

Embodied AI Platforms

Savva, M., Kadian, A., et al. (2019). "Habitat: A Platform for Embodied AI Research." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

Highlights the critical need for photorealistic, interactive environments and real-world data for training embodied agents.

Multimodal Sensor Fusion

Lahat, D., Adali, T., & Jutten, C. (2015). "Multimodal data fusion: An overview of methods, challenges, and prospects." IEEE Transactions on Signal Processing, 63(1), 1-29.

Comprehensive overview of mathematical techniques for combining information from different sensor types, supporting the tensor-based formulation.

Decentralized Systems, Cryptography, and Economics

Blockchain Foundations

Nakamoto, S. (2008). "Bitcoin: A Peer-to-Peer Electronic Cash System."

Foundational paper for decentralized, trustless systems and consensus mechanisms underlying Proof-of-Contribution.

Smart Contract Platforms

Buterin, V. (2014). "A Next-Generation Smart Contract and Decentralized Application Platform." Ethereum Whitepaper.

Introduces smart contracts essential for implementing decentralized marketplace and automated reward functions.

Cryptoeconomic Design

Zargham, M., et al. (2018). "Foundations of Cryptoeconomic Systems." arXiv preprint arXiv:1808.03634.

Formalizes design of incentivized blockchain systems, providing framework for analyzing rational agent behavior within protocols.

Privacy-Preserving Cryptography

Ben-Sasson, E., et al. (2019). "Zerocash: Decentralized Anonymous Payments from Bitcoin." IEEE Symposium on Security and Privacy.

Details advanced cryptographic techniques (zk-SNARKs) for validation without revealing underlying data, ensuring contributor privacy.

Data Markets and Incentive Mechanisms

Data Markets

Stahl, F., Schomm, F., Vossen, G., & Vomfell, L. (2016). "A classification framework for data marketplaces." Vietnam Journal of Computer Science, 3(3), 137-143.

Provides taxonomic foundation for decentralized data marketplace design.

Mechanism Design

Myerson, R. B. (1981). "Optimal auction design." Mathematics of Operations Research, 6(1), 58-73.

Game-theoretic principles for designing efficient auction mechanisms in decentralized markets.

Privacy-Preserving Data Sharing

Dwork, C. (2008). "Differential privacy: A survey of results." International Conference on Theory and Applications of Models of Computation, 1-19.

Mathematical framework for privacy-preserving data analysis essential for contributor privacy protection.

Federated Learning

McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). "Communication-efficient learning of deep networks from decentralized data." Artificial Intelligence and Statistics, 1273-1282.

Methodology for distributed machine learning that maintains data locality and privacy.

Philosophical and Ethical Frameworks

Embodied Cognition

Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

Seminal work on "enactivism" - theory that cognition arises from dynamic organism-environment interaction, supporting the need for interactive sensor data.

Extended Mind Theory

Clark, A. (2008). Supersizing the Mind: Embodiment, Action, and Cognitive Extension. Oxford University Press.

Argues that cognitive tools and environment are part of cognitive process, supporting the Sentient Layer as cognitive apparatus extension.

Consciousness and AI

Chalmers, D. J. (1995). "Facing up to the problem of consciousness." Journal of Consciousness Studies, 2(3), 200-219.

Articulates the "hard problem of consciousness," distinguishing Sentess goals (verifiable intelligence) from pursuit of true sentience.