Strategic Perspective: Digital Twins and the Emerging Internet of Humans (IoH)

Introduction

The rapid proliferation of connected devices and advanced analytics is giving rise to an “Internet of Humans” (IoH) built around detailed digital representations of ourselves. Digital twins—virtual replicas of physical entities—have moved beyond factories and smart cities into the realm of personal lives. Researchers and industry visionaries are now crafting human digital twins: data-driven models that mirror an individual’s body, behavior, and even personality in real time. These developments promise personalized services and predictive insights, but also raise profound ethical and societal questions. This whitepaper provides a rigorous overview of the technical infrastructure behind human digital twins and the emerging IoH, their predictive mechanisms and identity modeling, and the attendant ethical implications. We begin by clarifying how IoH extends concepts from the Internet of Things (IoT) and Internet of Bodies (IoB), then explore digital twin architectures, enabling technologies (from wearables to neural interfaces), predictive AI capabilities, and the risks to autonomy, privacy, and identity. We conclude with considerations for governance, including legal frameworks, digital sovereignty, transparency, and long-term societal effects. Throughout, we cite current research and known tech developments to ground our discussion in reality.

From IoT to IoB to IoH: Definitions and Evolution

The Internet of Things (IoT) refers to the network of physical “things” embedded with sensors, software, and connectivity, enabling these objects to collect and exchange data. IoT spans everything from smart thermostats to industrial machines, revolutionizing data collection and automation. Internet of Bodies (IoB) is a related concept focusing specifically on devices in, on, or intimately monitoring the human body. Coined around 2016, IoB describes connected sensors and gadgets that monitor the human body’s metrics (physiological, biometric, and behavioral data) and communicate that information over networks. Examples range from consumer wearables like smartwatches and fitness trackers to medical implants (insulin pumps, pacemakers), ingestible sensors, and even brain stimulation devices. IoB devices create a close synergy between humans and technology: they don’t just sit in our environment like IoT sensors, but rather integrate with our bodies, continuously collecting health and activity data and sometimes even altering bodily functions (for instance, neural implants that stimulate brain activity).

Internet of Humans (IoH) is an emerging paradigm that builds on IoB but with a broader human-centric scope. In one sense, IoH is used interchangeably with IoB – referring to the network of connected devices gathering and analyzing human health, behavior, and biometric data via invasive, wearable, or ingested sensors. IoH devices operate across multiple levels: they include hardware sensors on the body, connectivity via wireless networks, cloud or edge back-ends for data storage and analysis, and user-facing applications for visualization. This technical view of IoH essentially extends IoT to treat the human itself as a node on the network, surrounded by smart wearables and implants. In a wider sense, some researchers frame IoH as a human-centric evolution of the internet, emphasizing trust, inclusion, and personal agency. Unlike the thing-oriented IoT, IoH highlights that humans are not mere data points but stakeholders whose behaviors, emotions, and social contexts need to be integrated into digital systems in an ethical way. In summary, IoT connects our things, IoB connects to our bodies, and IoH aspires to connect to our very selves – blending physical, physiological, and even psychological realms into the network. This sets the stage for the rise of digital twins of humans, where a virtual counterpart of each person lives in cyberspace to enable new services and analyses.

Architecture of Human Digital Twins: Data, Mirrors, and Models

A human digital twin (HDT) is essentially a high-fidelity digital profile or replica of a person in the virtual world, continuously fed by real-world data. Architecturally, most frameworks split the HDT system into three layers or blocks:

• Physical World Data Acquisition: In the first layer, data about the person is captured from the real world via sensors and devices. This includes a data collection sub-block that aggregates various categories of human-related data. Key data streams fall into at least four broad categories: (1) External physical data (e.g. a person’s movements, location, or environment data around them), (2) Physiological data (vital signs, biometrics like heart rate, blood pressure, brain signals), (3) Human-to-human social interaction data (communications, social media or face-to-face interaction metrics), and (4) Human-to-environment data (how the person interacts with physical surroundings, context like ambient humidity or pollution). The sensing and perception sub-block handles this data capture, using wearable sensors, smartphones, implants, and IoT devices to continuously monitor the person’s state and environment. The goal is comprehensive coverage of the person’s attributes and context. Changes in the person’s state are sensed in real time and transmitted to the digital twin, so that the digital replica updates synchronously with the individual. In effect, the twin is a live mirror: every significant event (a spike in heart rate, a change in emotion, entering a new location) can be reflected by updating the twin’s data. This real-time mirroring is critical for the twin to remain an up-to-date proxy of the human. Modern connectivity (5G/6G networks, IoT platforms, etc.) and edge/cloud computing enable this low-latency data flow, ensuring the twin is never out of sync for long.
• Digital World Modeling and Simulation: In the second layer, the incoming data is ingested, processed, and fused to construct the actual digital twin model. Raw signals from sensors may be cleaned for noise or gaps (data cleaning to handle discontinuities). The system maintains a database of the person’s historical and current data, effectively a continuously updating profile identified by a unique ID for that individual. Crucially, disparate data streams (wearable sensor data, GPS location, social media feeds, etc.) are integrated and fused to form a unified representation. On this foundation, the twin’s core is built via modeling and simulation engines. The HDT has multiple facets of modeling: physical/physiological modeling (down to organs or body systems) and behavioral modeling (activities, routines, social interactions, lifestyle patterns). For instance, on the physical side, a heart model or full-body avatar might simulate the individual’s biomechanics or health state; on the behavioral side, models might encapsulate the person’s typical daily schedule, cognitive patterns, decision habits, and interactions. The modeling process uses techniques ranging from first-principles (biophysical models) to data-driven AI (machine learning, deep learning) to define the twin. Real-time simulation allows the twin to not just statically reflect the person, but to evolve dynamically in response to new data. The twin’s software can run predictive what-if scenarios or forward simulations. An analysis engine continually evaluates incoming data against the model, using predictive algorithms to forecast future states or detect anomalies. A prediction engine generates suggestions or decisions (for example, predicting a health risk or suggesting an intervention), and an evaluation engine checks these predictions against reality to refine the model’s accuracy. This forms a feedback loop: if the twin predicts an outcome (say elevated stress level) and the real person’s data later confirms or refutes it, the model is adjusted (optimized) to improve future predictions. Through such autonomous inference, the digital twin becomes smarter over time, learning an increasingly precise representation of its human subject. In summary, this layer transforms raw sensor inputs into a cognitive, actionable model of the person – effectively creating their “second self” in silico.
• Human–Digital Interface and Feedback: The third layer manages the interactions between the physical human and their digital counterpart. It provides the interfaces and communication tools for real-time exchange. One aspect is ensuring data flows to the twin (via IoT gateways, smartphones, or other interfaces that relay sensor data into the model). Equally important, the twin’s outputs and insights must flow back to the human or to external systems in an intelligible way. An intelligent interface (often a mobile app, dashboard, or even AR/VR visualization) acts as the bridge for this two-way interaction. For example, the person might receive feedback from their twin: notifications of predicted health issues, or suggestions derived from analyzing their behavior. Visualization engines using augmented or virtual reality can present the twin’s state and predictions to users in understandable form. This interface layer is critical for transparency and trust – a well-designed interface can help users see into their twin and understand why it’s making certain predictions, thereby increasing their confidence in the system. It also allows users or caregivers to provide corrections or additional input, further refining the twin. In essence, this layer closes the loop between flesh and data: it ensures that the digital twin not only listens and learns from the human, but also speaks back and influences real-world decisions.

The above architecture enables a human digital twin to function as a real-time mirror and a predictive simulator of an individual. To illustrate the process flow: First, one must determine the purpose of the twin (health monitoring, workplace optimization, personal assistant, etc.), which dictates what data and models are most relevant. Next, sensors gather data on the person and environment, building a live “physical-world representation” of the human in data form. That data is shuttled through the interface into the digital model; as the person’s state (e.g. mood or activity) changes, new data streams in continuously to update the HDT. The virtual system processes and stores this data, then uses AI modeling techniques (including ML and continuous learning) to construct or update the twin’s models (both physical models like organ models and behavior models for activities and decisions). The twin then simulates forward, predicting future conditions or behaviors, analyzing outcomes, and evaluating its own predictions against reality to self-correct. Finally, the interface presents results or feedback to the user (or to connected services), completing the cycle. This cyclical loop can run continuously or at high frequency, effectively synchronizing the twin with the human and enabling autonomous inference and decision support in real time. The fidelity of this system depends on multi-modal, high-quality data. As researchers point out, incorporating not just internal bodily data but also social and environmental data provides richer context, avoiding a narrow view and improving the twin’s predictive accuracy. Modern HDT frameworks emphasize flexibility and generality, aiming to apply to many domains (industry, healthcare, daily life) by capturing both physical and behavioral facets of humans. In summary, the architecture of human digital twins fuses IoT sensor networks, AI modeling, and human-computer interaction into a unified system that mirrors, analyzes, and potentially augments a person’s identity in cyberspace.

Key Technologies Enabling IoH and Digital Twins

Achieving the vision of IoH and human digital twins requires weaving together multiple cutting-edge technologies. Below we discuss several key technological components and how they contribute to data acquisition, modeling, and interaction:

• Biometric Tracking and Wearable Sensors: The foundation of any human digital twin is continuous data about the person’s biological and physical state. Wearable biometric sensors (smartwatches, fitness bands, smart clothing, patches) and near-body devices provide this constant stream. They monitor heart rate, blood oxygen, activity levels, sleep patterns, temperature, and more. Advanced wearables can also measure galvanic skin response, ECG (electrocardiogram), blood glucose, or respiratory rate. These devices have become commonplace – for example, as of a Pew survey about 1 in 5 Americans regularly use a wearable fitness or health tracker. By integrating such data, a digital twin gains a window into the person’s moment-to-moment physiology. Additionally, ubiquitous sensors embedded in phones or environments (accelerometers, GPS, smart home devices) contribute behavioral and contextual data. Together, these biometric and ambient sensors form the “nervous system” of the IoH, capturing the raw signals that feed larger models.
• Neural Interfaces and Brain-Computer Integration: A more frontier technology driving IoH is the development of neural interfaces – devices that can record or even influence brain activity. Examples include non-invasive EEG headsets, implanted brain stimulators (like deep brain stimulation for Parkinson’s), and emerging Brain-Computer Interfaces (BCIs) such as Elon Musk’s Neuralink implant. These interfaces allow the digital realm to tap directly into neural signals, adding a new dimension to the data. In-ear EEG devices, for instance, place electrodes inside ear earbuds to measure EEG signals discreetly. Studies have shown that data from convenient setups like ear-EEG can be comparable to full scalp EEG, enabling continuous brain monitoring in daily life. This is a potential game-changer: by decoding aspects of a user’s mental state (stress, focus, fatigue) directly from neural or physiological signals, digital twins can incorporate cognitive and emotional context that goes beyond external behaviors. On the output side, neural interfaces also allow writing information into the nervous system – for example, a neurostimulator might adjust brain signals to affect mood or pain. IoH envisions a tight loop where the human brain itself becomes part of the network, raising both opportunities (e.g. neuroprosthetics, mental health monitoring) and deep ethical concerns. It is telling that policymakers are already preparing; Chile recently moved to amend its constitution to protect “neurorights” – treating personal brain data and mental integrity with the same status as bodily organs, so they cannot be bought, sold, or manipulated without consent.
• Behavioral Analytics and Social Data Integration: Beyond the body, IoH technologies capture the behavioral exhaust of our daily activities. Smartphones and internet services continually log our location trails, online clicks, purchasing habits, social media interactions, and more. This data, when aggregated, provides a rich picture of what we do, who we meet, and even how we think. Behavioral analytics involves using this digital footprint to infer patterns and propensities. For example, location and app usage data might reveal a person’s routine (when they commute, where they shop), while social media text might reveal mood or interests. In digital twin modeling, such data is invaluable for the behavioral models – the twin can learn an individual’s preferences, communication style, and typical decision patterns. Advanced analytics powered by machine learning can detect deviations (e.g., a change in routine that might indicate depression or illness) or segment people into categories for comparison. In IoH, the human is not an isolated node but part of a social and environmental graph; thus, human-to-human and human-to-environment interactions are considered first-class data. Research frameworks include social interaction modeling and lifestyle modeling as core parts of human digital twins. This means the twin doesn’t just know your vital signs, but potentially knows you go to the gym on Tuesdays, have a social circle that influences your diet, or tend to work late before project deadlines – all of which can feed into predictions about your future state. Behavioral analytics often leverages big data and cloud computing (analyzing months or years of logs), complementing the real-time sensor data from wearables. Together, these approaches allow an HDT to model both short-term states and long-term traits of an individual.
• Emotion Sensing and Prediction Algorithms: Emotions are a key part of the human condition that IoH technologies are beginning to gauge via affective computing. Emotion-sensing algorithms use a combination of signals – facial expressions (via camera), voice tone (via microphone), text sentiment, and physiological cues – to infer a person’s emotional state. For instance, wearables measuring electrodermal activity (skin conductance) and heart rate variability can detect stress or excitement, as changes in these signals correlate with arousal and sympathetic nervous response. Research has achieved promising accuracy: in one system, features from ECG signals were able to classify emotional reactions with about 94% accuracy under experimental conditions. Even without cameras or mics, just the biometric signals from a smartwatch can hint at mood; for example, patterns in daily heart rate and activity data have been used to predict self-reported stress or depressive states to some degree. Emotion prediction algorithms take this further by not only recognizing current mood, but anticipating shifts. Experimental AI models aim to forecast emotional fatigue or burnout before it manifests, by spotting subtle trends in physiological and behavioral data. In digital twins, an emotion model can be part of the feedback loop – e.g., if the twin senses rising anxiety, it might proactively advise a break or alert a caregiver. Some HDT researchers talk of modeling the “emotional state” as one of the human attributes in the twin’s profile. Embedding emotional intelligence into IoH systems could enable more empathetic responses (for example, a car that knows you’re stressed might adjust its alerts and driving assistance). However, it also poses risks of privacy invasion and manipulation (e.g., if a platform knows you’re sad, it might target you with certain advertisements). Nevertheless, the technology is quickly advancing – large tech companies and startups alike are integrating emotion AI into wearables, mobile apps, and customer analytics. As one report notes, emotion AI turns human behavioral attributes into data, allowing empathetic responses at scale in domains like sales or healthcare. In summary, algorithms to quantify and predict emotion are becoming an integral part of the IoH toolkit, adding a layer of psychological context to the raw sensor data.
• Autonomous Agents and Actuators: Rounding out IoH are the actuation technologies – systems that can take actions affecting the human or environment. A digital twin that only observes is limited; many IoH scenarios involve the twin (or IoH devices) doing something in response. Simple examples include wearable insulin pumps that automatically adjust dosage based on sensor readings, or smart home systems that change lighting/heating for comfort. More sophisticated are personal AI assistants that schedule your calendar or filter information for you. In the context of digital twins, researchers envision autonomous agents that act as proxies for the individual. For instance, a twin could participate in a virtual meeting on your behalf, or negotiate with other agents (perhaps managing your calendar or finances) while you focus elsewhere. NTT researchers describe creating a “personal agent to work on your behalf”, essentially duplicating one’s digital twin to handle multiple tasks simultaneously in cyberspace. Actuators can also be in the human body: neural implants might adjust signals to improve focus, or haptic devices could deliver real-time feedback (like a smart band that vibrates to alert you of high stress, effectively nudging you to breathe deeply). These interventions close the loop of IoH: sensing, analysis, prediction, and action. Through actuating technologies, the digital twin paradigm moves from mirror to actor, implementing decisions or changes. While this promises automation and extended capabilities (e.g., working in multiple virtual locations at once via twin copies), it also encroaches on human autonomy if not carefully governed. The technology blur the line between “user” and “device” – for example, when does a helpful nudge become an unwanted push? We discuss these ethical questions later, but from a technical standpoint, the trend is clear: IoH is not just about passive data collection, but about interactive cyber-physical systems that both monitor and modify human behavior and physiology.

Together, these technologies form the scaffold of the Internet of Humans and human digital twin systems. Ubiquitous sensors provide the eyes and ears, high-speed networks and IoT platforms provide the nervous system for data flow, AI/ML provides the brain for pattern recognition and prediction, and actuators/agents provide the hands and voice to influence the world. The convergence of these capabilities is what makes the IoH vision so powerful—and to many, unsettling. In the next section, we explore how these pieces come together in predictive infrastructures that anticipate our decisions and even shape our identity.

Predictive Mechanisms and Identity Modeling in IoH

One of the most groundbreaking (and controversial) aspects of digital twins and the IoH is their capacity for prediction and simulation regarding human behavior. By analyzing past and present data, these systems aim to anticipate future states—from health events to personal choices—and even emulate aspects of a person’s identity in silico. This section examines how ML/AI-driven infrastructures create such predictive and proxy capabilities, including the formation of “emotional proxies” and “identity shadows.”

ML/AI for Anticipating Decisions: Human digital twins leverage machine learning algorithms to parse vast quantities of personal data and forecast what an individual might do or need next. In healthcare, for example, a digital twin of a patient can predict the onset of complications or suggest personalized treatments by running ahead of real time with the patient’s data. More generally, companies are already using AI to shape decision-making: recommender systems predict which product you’re likely to buy, or algorithms decide what route you might want to drive. A fully realized IoH extends this to deeper levels of our lives. Because an HDT is continually updated with real-world data, it can serve as a constantly learning predictive model of its human. If you have a digital twin personal assistant, it might anticipate your decision in contexts like scheduling (e.g., declining an invite it knows you would refuse based on past behavior) or lifestyle (e.g., pre-ordering a meal when it predicts you’ll be too busy to cook). On a larger scale, NTT’s Digital Twin Computing concept highlights combining many digital twins to simulate complex scenarios and yield “large-scale, highly accurate future predictions” that account for interactions between individuals, machines, and society. This could contribute to automated decision-making systems that pre-empt problems or optimize outcomes without direct human input. For instance, city planners might use an “Internet of Digital Twins” to simulate how people would move through a new urban design, or employers might use digital twins of staff to predict performance and allocate tasks accordingly. Such predictive infrastructure promises efficiency and prevention (solving issues before they occur). However, it also edges towards a world where algorithms make or influence many personal decisions, raising the question of whether this compromises free will. Indeed, ethicists note that as we delegate more choices to AI, we risk a decline in our own decision-making capacities and potential misalignment between algorithmic suggestions and our true preferences. The illusion of autonomy may arise – feeling in control while subtly being guided by an AI that “thinks it knows us” better than we know ourselves.

Emotional Proxies and Digital Avatars: Beyond predicting external decisions, advanced digital twins aim to replicate a person’s internal disposition and interactive style, essentially acting as an emotional or social proxy. A vivid example is having a meeting between digital twins instead of the actual people. Researchers have demonstrated that if a digital twin is imbued with the personality and communication patterns of its human counterpart, it can “react as if it were the real person” to others in cyberspace. In other words, your twin could carry out a conversation or negotiation on your behalf, responding in ways you likely would. Conversely, if allowed some autonomy, it might initiate interactions—perhaps networking with other digital agents to further your interests. This effectively creates an emotional proxy: the twin can mirror not just factual data but the tone, affect, and decision heuristics of the individual in interactions. Such proxies could be useful – for instance, a twin might handle routine social obligations or filter communications, sparing you time while preserving your personal touch. Another proposed use is personal cognitive assistants that know your values and preferences so well that they serve as a second self in digital realms. Some have even suggested future scenarios where you could “dialogue with your own digital twin” or those of others, including simulations of deceased individuals, for the sake of self-reflection or therapy. An emotional proxy could allow you to, say, talk to a simulation of your younger self or a lost loved one, as a way to gain insight or closure. These ideas, once science fiction, are now active research areas in human-computer interaction and AI. At their core is the notion of modeling individual identity with such fidelity that it can be projected independently. We see early versions of this in chatbots fine-tuned on a person’s communications to act in their style. The ultimate emotional proxy would not only mimic outward behavior but also maintain an inner model of “what it feels like to be you” – though achieving that depth crosses into philosophical territory of consciousness. Even without reaching that extreme, emotional proxies raise serious ethical and legal issues: who is responsible for a digital twin’s actions if it’s acting autonomously? Can a person be held to a commitment their twin made? And what if the proxy gets it wrong and misrepresents you in an important situation? These concerns will need addressing as the tech matures.

Identity Shadows and Data Doubles: Perhaps the most pervasive yet hidden aspect of IoH is the creation of identity shadows – digital profiles or data doubles built from our data that can profoundly shape how we’re perceived and treated. Long before full-fledged HDTs are common, we already each have numerous “algorithmic identities” compiled by social media platforms, advertisers, credit bureaus, and governments. These data doubles are essentially shadow selves constructed by aggregating and analyzing our data trails. They may include our consumer preferences, health indicators, personality traits inferred from posts, risk scores, etc. Importantly, they are not always accurate or complete, but they are used to make decisions about us – what ads we see, how high our insurance premiums are, or whether a bank approves a loan. In the context of digital twins, this concept of identity shadow becomes even more concrete and sophisticated. An HDT is like a highly developed data double that you might even interact with. Scholars have noted that people are broken down into series of discrete informational flows, which are then reassembled to serve institutional agendas. The data double is “a shadow self created by the capture of data” that may or may not align with the actual person. For example, an algorithm might label someone as a potential health risk or as having certain psychological traits based on data correlations, even if in reality those traits are not accurate – the person becomes diagnosed by their data. This has been observed in social media contexts where algorithms target users with “diagnostic ads” for conditions like depression or ADHD based on their browsing, effectively assigning identities or conditions to them without clinical basis. The danger is that these identity shadows can influence the person’s own self-concept and opportunities. If every digital system you encounter treats you as, say, a likely addict or a high-value customer or a security threat (depending on what your shadow profile says), those expectations can become self-fulfilling or at least seriously affect your autonomy. In a fully developed IoH, identity shadows could be even more influential: your HDT might negotiate job offers or medical care on your behalf, so any biases or errors in its model of “you” could directly impact your life. Moreover, there is the risk that the simulation overtakes reality in authority. If a predictive model says you are likely to commit some action or develop some illness, institutions might act on that (such as denying insurance or increasing surveillance) before you’ve done anything – a scenario reminiscent of “pre-crime” analytics. Observers have warned that already today, algorithms can misdiagnose or mislabel individuals in ways that disrupt their sense of self and may cause real harm. We must grapple with the prospect that a digital twin or data double, created for ostensibly benevolent reasons, might cast a long shadow – one that confines people to algorithmically defined identities. This is essentially identity commodification: turning the fluid, contextual self into a package of data points to be bought, sold, and acted upon. As Gaeta writes, our data doubles are continually being “transported to centralized locations to be reassembled… in ways that serve institutional agendas” – meaning tech companies, marketers, insurers, or governments use these shadows to maximize profit or control, not necessarily to empower the individual. One outcome is behavioral nudging at scale: if your identity shadow suggests you are persuadable in certain ways, systems can target you with tailored nudges (for shopping, voting, lifestyle changes) without you ever realizing the extent of personal data driving it. The IoH raises this to a new level of precision.

In summary, the predictive infrastructure of the Internet of Humans uses AI to anticipate our actions and needs, while the modeling of identity yields both helpful proxies and problematic shadows. We stand to gain decision support, personal agents, and foresight into health or behavior – if these systems truly understand us and remain under our control. But we also risk ceding too much authority to imperfect models, potentially eroding personal agency and privacy. The next section delves into these ethical risks, examining autonomy, consent, simulation bias, nudging, and commodification in detail.

Ethical Implications and Risks

As digital twins of humans and the IoH paradigm advance, they present a double-edged sword. For every promised benefit (personalization, efficiency, preventive care) there is a potential harm (loss of autonomy, surveillance, bias, manipulation). This section analyzes key ethical risks and societal dilemmas arising from IoH and human digital twin technologies:

• Erosion of Autonomy: A critical concern is that pervasive algorithmic assistance could undermine human autonomy and free will. When AI predicts and optimizes our decisions, it might narrow our choices or push us toward certain behaviors. Over-reliance on algorithmic decisions can lead to a decline in the user’s own decision-making capacity. For example, if a digital twin always schedules your activities for “optimal productivity” as defined by data, you might gradually lose the practice of making spontaneous choices or reflecting on your priorities. Furthermore, algorithms often carry embedded values or assumptions. They may steer individuals in self-reinforcing ways – creating “filter bubbles” or feedback loops that narrow one’s self-perception. A person who is consistently nudged to pursue certain careers or relationships by their digital twin’s advice might find their life course subtly but profoundly shaped by an AI’s logic rather than their own explorations. Scholars have argued that in algorithmic decision-making, the sense of autonomy users experience can be an illusion, as the system channels them down particular paths while giving a false impression of choice. With human digital twins, this could be even more insidious: the twin, perceived as “another you,” might be trusted deeply, so its suggestions are taken as one’s own inner voice. If that twin’s model is biased or its goals aligned more to a company’s interests (e.g., keeping you engaged online) than your own, autonomy is compromised. Decisional privacy – the freedom to make choices without undue external influence – could erode under constant personalized nudging. This autonomy issue is so pronounced that some ethicists argue challenges to user autonomy are inevitable and difficult to eliminate in personalized AI systems. To mitigate this, a human-in-the-loop approach is often suggested, keeping users explicitly involved and in control of critical decisions. Ensuring that digital twins serve as tools to augment human agency, rather than replace it, will be key. Design choices like requiring user confirmation for significant actions, providing explanation for recommendations, and allowing easy override can help preserve autonomy.
• Consent and Privacy Bypassing: The IoH thrives on data – much of it highly sensitive personal data. This raises obvious privacy issues: who owns and controls this deluge of intimate information? One major risk is the bypassing of informed consent. When sensors continuously collect data and AI systems derive new insights (some of which the user never explicitly provided), it becomes murky whether individuals meaningfully consent to each use of their data. Studies of health apps and IoT devices show that often users are not fully aware of what data is collected or where it’s going, making genuine informed consent “often missing”. For example, a fitness app might share activity data with advertisers unbeknownst to the user. In the context of digital twins, the issue intensifies: by their nature, HDTs integrate multi-source data and repurpose it for simulations and predictions, potentially beyond the original collection purpose. Without robust data governance, a person could effectively lose oversight of their digital self. There is also the question of scope creep – data collected for one benefit (say medical monitoring) might later be used in other contexts or combined with other datasets to infer things the person never agreed to share (like cognitive ability or propensity for addiction). If a twin’s insights (e.g. “this individual is often depressed on weekends”) leak or are sold, it could be highly intrusive. Indeed, researchers highlight that when the scope of data collection isn’t clear, meaningful consent cannot be given. Additionally, if HDT services become common, there’s a worry that people might be coerced or manipulated into participation (for instance, an employer or insurer might favor those who use approved digital twins, indirectly pressuring individuals to consent to extensive monitoring). Data brokerage and commodification of personal digital twin data is another threat: service providers might engage in selling the rich profiles for profit. Cases are already documented: a review found that out of top health apps for depression and smoking cessation, 81% were transmitting user data to third-party analytics or advertising firms like Facebook/Google, often without explicit user awareness or consent. This violates the expectation of privacy and fails to show respect for the individual’s rights. IoH devices embedded in our bodies make this even more concerning – if something like a smart pacemaker or brain implant collects data, the user might not even have the ability to turn off data sharing. The ramifications of privacy breaches are severe: sensitive health or identity data in the wrong hands can lead to discrimination, stigmatization, or exploitation. A particularly harmful scenario is identity theft or coercion via hacked digital twins. The more detailed our digital models become, the more attractive they are to attackers. If someone can steal or manipulate your HDT, they could impersonate you or blackmail you with private information. Cybersecurity is thus a vital component of IoH ethics. Robust consent management, transparency, and security safeguards (encryption, authentication, etc.) are needed to handle these risks. Policy is catching up slowly – frameworks like GDPR give individuals rights over their data, and new proposals (like Europe’s AI Act) aim to regulate high-risk AI uses – but the complexity of IoH data flows demands stronger, context-specific protections.
• Simulation Bias and “Digital Determinism” (Simulation Dominance): We use the term “simulation dominance” to describe the risk that the simulated model of a person overrules or distorts the reality of the person. This can happen when digital twin predictions or profiles are taken as more true or authoritative than the individual’s own voice. Consider a scenario where a predictive model flags someone as likely to quit their job soon. An employer might preemptively pass them over for a promotion, effectively making a decision based on the simulation’s output, not the person’s actual intentions. The person could protest, “I’m not planning to quit,” but the data-derived “shadow” identity (in this case, a predicted quitter) dominated the outcome. This is a form of algorithmic bias – the model may be wrong or over-generalized, yet it holds power. A concrete example comes from social media algorithms making mental health predictions. Algorithms might diagnose or label users as at-risk for certain disorders purely from online behavior patterns that have tenuous links to real conditions. Those predictions, in turn, drive targeted ads or content that can cause the person to question their own identity or health (e.g., being fed content about depression could make one introspective about being depressed even if they are not). Thus, the model’s view of you starts to shape your view of yourself, an unsettling feedback loop. Another dimension is error and bias within the data: digital twins are only as good as the data and assumptions they are built on. If certain populations are underrepresented or misrepresented in training data, the twins will encode those biases. For example, an emotion recognition algorithm might misinterpret expressions of people from a culture not seen in its training set, leading to systematically wrong “emotional twin” models for those individuals – but if treated as fact, those people could be judged or treated inappropriately. The opacity of AI models adds to the problem: if a twin’s prediction is wrong, will anyone (including the user) realize, or will it quietly propagate through decisions? Experts warn that even well-intentioned predictive systems can create self-fulfilling prophecies or trap people in algorithmic determinism, where one’s data past fixes one’s future opportunities (e.g., a low credit score data double making it impossible to get loans to improve one’s situation). This is why transparency and contestability of digital twin outputs are ethically required. Users should have the right to see, challenge, or correct their digital twin’s records and predictions – essentially having a say in their “algorithmic identity.” Without that, we risk a future where the simulation of a person – which might be incomplete or biased – becomes more influential than the person themselves, a high-tech echo of the classic philosophical fear of simulacra supplanting reality.
• Behavioral Nudging and Manipulation: IoH platforms, by virtue of intimate knowledge, can be extremely effective at shaping user behavior, for better or worse. On the benign side, nudges can encourage healthy habits (a twin might gently remind you to hydrate or exercise based on predictive need) or improve safety (alerting you if you’re getting drowsy while driving). However, the line into manipulation is thin. If nudges serve the agenda of the platform or third parties rather than the individual’s own goals, this becomes problematic “dark nudging” or outright coercion. For instance, a retail company partnering with an IoH app might nudge users towards impulse purchases when their mood data suggests they’re vulnerable to “retail therapy.” Because IoH devices might even alter mood (imagine a VR environment that subtly calms or excites you), the potential for emotional manipulation exists. The loss of self-determination can be gradual: people might simply follow what their app or twin suggests in most things, especially if it’s usually helpful. Over time, that could condition behavior in line with what algorithms deem optimal. On a societal level, there’s concern that these technologies could be used to exert social control. A government could, for example, distribute wearables for public health but also use them to monitor and nudge civic behavior (rewarding certain movements, discouraging gatherings deemed undesirable). Indeed, researchers note the dual-use risk: the same HDT tools that empower individuals can be abused to “exercise social control and suppress civic protest” if wielded by authoritarian interests. Targeted nudging, personalized propaganda, and exploitative advertising are all forms of manipulation supercharged by IoH’s data. An analysis by the Alan Turing Institute on online nudging highlights how decisional privacy is at stake when every prompt we receive is micro-targeted based on predicted susceptibilities. If left unchecked, this could undermine the democratic process (voters nudged in specific ways) or lead to new forms of discrimination (some people get nudged towards opportunities, others towards harmful choices). Ethically, using IoH data to influence behavior demands a delicate approach: ideally, only transparent, user-benefiting nudges with opt-out options should be allowed. Anything secretive or profit-driven runs afoul of personal autonomy and dignity.
• Identity Commodification and Inequity: Finally, a broad ethical issue is the commodification of human identity and emergence of new inequalities. As discussed, the data constituting a digital twin can become a product – bought and sold in markets for advertising, insurance, employment screening, etc. This treats aspects of personhood (health status, personality metrics, habits) as commercial assets, potentially without the person’s meaningful consent or benefit. One worry is that individuals will lose sovereignty over their digital selves under current models of data economics. Without strong rights, a person’s digital twin (or its data feeds) could be exploited in ways they cannot control. There’s also the risk of a “digital divide” or new inequality based on who has access to their twin and who doesn’t. If digital twins become important for accessing services (say healthcare optimization or custom education plans), those who opt out or are left out (due to cost or distrust) might be disadvantaged. Conversely, if someone’s twin is of poor quality (perhaps due to less data collected), they might receive suboptimal or biased treatment from automated systems – a form of algorithmic marginalization. Another facet is reinforcing existing biases: if marginalized groups have historically sparse data or if the algorithms are trained on majority populations, the resulting twin services might work better for some demographics than others, exacerbating disparities. Power imbalances could deepen: corporations or states with the resources to analyze masses of digital twins could gain unprecedented influence over consumer behavior or societal trends, while individuals become more transparent and predictable to those entities. As one paper notes, even comprehensive consent and risk assessments at an individual level won’t eliminate certain risks at the collective level – problems like biased outcomes or social stratification require governance beyond individual choice. We might see the rise of what some call “algorithmic elites” vs. “data-subjugated” classes: those who control the data and models versus those who are controlled by them. Protecting digital self-determination is therefore a critical ethical imperative. This involves not just privacy, but the idea that a person should be able to define and present their identity, and not have it unilaterally defined by data aggregators. Approaches like data trusts, personal AI assistants that truly work for the user, and legal recognition of digital personhood rights (as Chile’s neurorights begin to do for brain data) are ways to combat pure commodification.

In overview, the ethical landscape of IoH and digital twins is complex. Autonomy, privacy, and identity are under pressure in novel ways. These technologies challenge us to extend existing ethics (like medical consent, privacy rights, fairness in AI) to much more intimate and continuous forms of data use. The stakes are high: mishandled, IoH could lead to a loss of human agency and equality; handled with foresight, it could empower individuals with unprecedented self-knowledge and personalized support. The next section turns to how policy and governance might rise to this challenge.

Policy and Governance Implications

Managing the revolution of the Internet of Humans and digital twin technology will require proactive governance, new legal frameworks, and multi-stakeholder oversight. Below we outline some key implications for policy and societal governance:

Legal and Regulatory Frameworks: Current data protection laws (such as the EU’s GDPR) and medical privacy laws (like HIPAA in the US) provide a starting point, but IoH may demand more specialized regulations. Personal digital twins blur lines between medical device, data processor, and even autonomous agent. Regulators will need to clarify issues like: Is a comprehensive digital twin considered part of one’s person (and thus protected akin to one’s body or mind)? Chile’s move to enshrine mental data rights and neuroprivacy in its constitution sets an interesting precedent, treating brain data with the sanctity of organs. Similarly, one could imagine laws declaring that one’s digital twin data cannot be used against the individual in discriminatory ways – e.g., banning insurance or employment discrimination based on predictive health or behavior data, similar to genetic non-discrimination acts. Algorithmic transparency and accountability requirements are also crucial. For high-stakes use of digital twins (like healthcare or criminal justice), algorithms might need certification and auditing for bias, much as medical devices or drugs are evaluated for safety and efficacy. The EU’s proposed AI Act is one example, classifying “remote biometric and emotion analysis” or predictive policing as high-risk AI that will face strict oversight; human digital twins could easily fall in that category if they influence life opportunities. Additionally, regulators may require explicit informed consent and opt-in for the creation of a digital twin, with granular controls for the user to decide which aspects of their data are included. Another concept is data portability – individuals should be able to move their digital twin data between services or revoke it, similar to moving medical records or phone numbers today. Legal frameworks should also impose duty of care on digital twin providers: for instance, if an HDT is used for medical advice, it might need to meet standards akin to a licensed professional (or at least not give harmful advice). Liability is a big question: if an autonomous twin acting on my behalf causes harm, who is liable – the user, the developer, both? Clarifying this will influence how freely such systems are deployed.

Digital Sovereignty and Self-Ownership: The notion of digital sovereignty comes into play on both individual and national levels. Individually, digital sovereignty means each person maintains agency and ownership over their digital identity and data. Policymakers and technologists are discussing personal data stores and decentralized identity frameworks that let users control access to their info. For IoH, one idea could be a “personal digital twin license” – the twin is considered the intellectual property or extension of the person, and any use by third parties must be under license/consent terms the individual sets. On a national level, countries are increasingly concerned about where citizens’ data (including IoB/IoH data) is stored and processed – a trend of data localization and national cloud initiatives to ensure sovereignty over sensitive data. For example, health data or genomic data might be required to stay within certain jurisdictions. Digital twin networks might thus need architecture that respects geopolitical boundaries or risk running afoul of sovereignty concerns. International standards could emerge to harmonize how digital twin data is protected and exchanged cross-border (similar to how biomedical research has frameworks for sharing data ethically). Open standards and interoperability will be important so that no single company has an oligopoly on digital twin services; otherwise, we risk consolidating too much power in a handful of tech giants. Sovereignty also implies giving communities a say: for instance, involving patients in decisions about how digital twins are used in public health, or workers in decisions about workplace digital twin monitoring programs. The governance of IoH should include public dialogues and perhaps new institutions – e.g., an independent “Digital Twin Ethics Board” at hospitals or city governments to oversee deployments that affect citizens.

Transparency and Explainability: To maintain trust and fairness, IoH systems must be more transparent than today’s tech black boxes. This means at multiple levels: algorithmic transparency (users should be able to know what data is being used and on what logic a twin’s prediction is based, especially in sensitive areas like hiring or credit), data provenance (clear records of where data came from and who it was shared with, ideally accessible in a user-friendly dashboard), and right to explanation (if an automated decision is made about a person using their digital twin, they should be able to get an explanation in human terms). For instance, if a health digital twin suggests a particular treatment plan, the patient and doctor should be able to see which indicators or simulations led to that suggestion. Auditability by third parties is also key – regulators or independent researchers should be allowed to audit digital twin algorithms for biases or errors, under appropriate confidentiality. Some scholars propose “AI nutrition labels” or fact sheets for algorithms; a digital twin service could come with a transparency report about its accuracy rates, the data it collects, and known limitations. Another crucial aspect is communication transparency: systems should clearly signal when you are interacting with a digital twin or AI agent rather than a real human, to avoid deception. For example, if you’re chatting with what you think is a colleague but it’s actually their autonomous twin handling routine calls, you should be informed. Lack of transparency can also harm accountability: if a twin misbehaves and no one can trace why, it’s hard to assign responsibility or correct it. Hence, building logs and traceability into these systems from the start is a governance must.

Addressing Long-Term Societal Effects: Policymakers need to take a long-term view on how IoH might reshape society. One effect to watch is social stratification: Will those with state-of-the-art digital twins (perhaps costly or provided by elite institutions) gain major advantages in life outcomes? Could there emerge an “underclass” of people who are effectively invisible to the digital twin infrastructure (by choice or exclusion) and thus marginalized in systems that come to expect digital profiles for everything? Ensuring equitable access will be important if, say, having your health twin is needed for the best medical care. Another effect is on human behavior and relationships. As we offload cognitive tasks to digital proxies, how does that change us? There’s potential for both positive (more time for creativity, more personalized social connections facilitated by matching of digital twin insights) and negative (reduced human-to-human interaction, increased dependence on AI validation for self-worth). Society may need new norms, such as etiquette around using someone’s digital twin versus interacting with them directly, or norms for employers about respecting boundaries of employee IoB data. Psychological impacts are also a concern – for instance, continuous self-tracking can lead to anxiety or obsessive behavior in some, a phenomenon known as “dataism” or the Quantified Self burden. Education systems might need to teach digital literacy around these tools: how to interpret your digital twin’s output, how to understand its limitations, etc. On a larger scale, if IoH technology leads to labor shifts (some jobs replaced by personal AI agents, new jobs in managing digital selves), economic policies must adjust (retraining programs, considering how value created by personal data is shared, perhaps even mechanisms like data dividends). Governance frameworks should strive for human-centric innovation, echoing the concept of Society 5.0 that Japan has promoted, where the goal is technology in harmony with social well-being. That means evaluating each new IoH application through the lens: does this truly benefit humans or merely automate for efficiency at their expense? Participatory policy-making is advised: involve diverse stakeholders (technologists, ethicists, lay citizens, marginalized groups) in drafting guidelines and laws for IoH to ensure all perspectives are considered. For example, a city deploying an IoH-driven smart citizen digital twin program should engage citizens in design and oversight to build trust and align with community values.

In conclusion, the rise of the Internet of Humans and digital twin technology calls for vigilant governance balancing innovation with protection of human rights. Legal systems will need to adapt to treat aspects of our digital presence with the same respect as our physical bodies and mental autonomy. Transparency, consent, and accountability must be baked into system design and enforced via policy. And as this technology could profoundly alter how we live and relate, continuous societal dialogue is necessary to steer it toward socially beneficial outcomes rather than dystopian ones. With thoughtful governance, we can harness digital twins and IoH to augment human capabilities and welfare, while safeguarding the essence of what it means to be human in a digital age.

Conclusion

Digital twins and the Internet of Humans represent a new frontier in the digital revolution: one where technology maps and models human life itself with unprecedented fidelity. In this whitepaper, we have explored how IoH extends the IoT/IoB paradigm to encompass networks of human-connected devices and data, feeding into comprehensive digital replicas (HDTs) that mirror our bodies, behaviors, and perhaps one day, our minds. We detailed the architecture of these systems – from sensor-laden physical world interfaces to AI-driven modeling engines that simulate and predict our actions in real time. We surveyed enabling technologies, from wearables and biometric trackers to neural interfaces, affective algorithms, and autonomous agents, showing how each contributes a piece to the IoH puzzle. The capabilities emerging from this fusion are staggering: systems that can anticipate decisions, detect emotions, and create “shadow” profiles that may act as our second selves.

Yet, along with technical prowess come profound ethical and societal challenges. Our analysis identified serious risks – the erosion of personal autonomy under pervasive algorithmic guidance, the undermining of privacy and consent in a world of invisible data flows, the possibility that simulated identities could entrench biases or even supersede real individual agency, and the danger of behavioral manipulation and commodification of human identity on a grand scale. These are not far-fetched scenarios; they are extrapolations of trends already in motion. Addressing these issues demands an interdisciplinary effort bridging technology, ethics, law, and social science.

A recurring theme is the need for a human-centric approach. The IoH and digital twin technologies should be developed for human empowerment, not as intrusive surveillance or control tools. Ethical design principles – such as privacy-by-design, explainability, user agency, and equity – must guide innovation in this space. Promisingly, we see initial steps: companies incorporating transparency reports, governments like Chile recognizing neurorights, and researchers proposing frameworks for fair and accountable digital twins. But much work remains to ensure regulations keep pace with technology. Policymakers will need to craft adaptive frameworks that protect individuals’ digital self-determination without unduly stifling beneficial innovation. International cooperation may be required, given the borderless nature of data, to set standards on data sharing, algorithmic ethics, and cybersecurity for IoH systems.

In the long term, the societal impact of digital twins and IoH could be transformational. Optimists envision a future where personal digital twins act as guardian angels – averting health crises before they happen, optimizing our schedules to reduce stress, and extending our capabilities (even allowing us to be in multiple places virtually). Communities could use aggregated digital twin data to design smarter, more inclusive cities and services. Education could be tailored to each learner’s profile, and environmental sustainability could improve with human behavior simulations guiding policy. Pessimists, however, warn of a dystopia of hyper-surveillance, loss of individuality, and algorithmic determinism. The reality will depend on choices we make now.

Thus, this moment is pivotal. We stand at the cusp of an Internet of Humans – a paradigm that will define the relationship between our biological/social selves and the digital ecosystem. By rigorously examining the technical mechanisms and ethical implications, as we have attempted here, we can inform those choices. The conclusion is not to reject IoH for fear of its risks, but to engage with it conscientiously. It calls for groundbreaking interdisciplinary collaboration: engineers and AI scientists working with ethicists and sociologists, citizens and consumers having a voice alongside corporations and governments. The goal should be to shape a future where digital twins serve each person’s well-being and autonomy, where technology amplifies what is best in humanity rather than eroding it.

In summary, digital twins and the IoH hold immense potential to revolutionize healthcare, personalize experiences, and enhance understanding of ourselves. Realizing that potential while safeguarding human dignity and rights will be the true test. It is our hope that this article has provided a comprehensive, academically grounded foundation for understanding these emerging technologies and the stakes involved. Armed with such understanding, stakeholders can better navigate the path forward – one that keeps the “human” in the loop of the Internet of Humans.

References: (Selected inline citations)

• Lin, Y. et al. (2024). Human digital twin: a survey. Journal of Cloud Computing, 13:131.
• NTT R&D (2020). Human Digital Twins: Creating New Value Beyond the Constraints of the Real World.
• RAND Corporation (2020). What Is the Internet of Bodies? (Video transcript by M. Lee)
• ITRex Group (2022). What is the Internet of Bodies (IoB)?
• Medium/NeuroTechX (2023). Future of Wearable Sensors in the Internet of Humans.
• Gaeta, A. (2023). Your ‘For You’ Page is Analyzing Your ‘Data Double’. (Mad in America)
• Fontes, C. et al. (2024). Human digital twins unlocking Society 5.0? Ethics Inf. Technol. 26:54.
• Huang, P. et al. (2022). Ethical issues of digital twins for personalized health care service. J. Med. Internet Res. 24(1):e33081.
• Lu, W. (2024). Inevitable challenges of autonomy: ethical concerns in personalized algorithmic decision-making. Humanities & Soc. Sci. Comm. 11:1321.
• UNESCO Courier (2022). Chile: Pioneering the protection of neurorights.