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<aside> 🔒 No third-party will ever see or own your raw biometric data again


<aside> 🧠 Gain advanced biometric insights from the holistic analysis of data from multiple devices


<aside> 💎 Choose to interact with third-parties for personalised experiences and tokenised rewards


💫 The Opportunity

Leveraging data from wearable devices is a key next step for human-computer interaction and will be vital for frictionless metaverse experiences. The market for seamless, user-governed custodial solutions for wearables holds outsized impact and commercial upside.

🔮 Long-Term Vision

All human wearable data will be owned and controlled by individual users through our privacy-preserving infrastructure, which will power safe and rewarding user experiences, including within and beyond the metaverse.

⚡ The Friction We Solve For

Highly personal wearable data (including brain data) is currently managed and stored by hardware providers and sometimes sold to third-parties and affiliates, with no way for users to provide detailed permissions on this, or govern commercialisation. This raises ethical concerns and stifles innovation; data is only analysed with proprietary algorithms. Also, data from different devices is not interoperable so it is difficult to collate wearable signals from different devices for holistic analysis.

💡 Our Solution

LYNX is a backend data management system for real-time biometric user data collected through wearables and interfaces (including brain-computer interfaces (BCIs), fitness trackers and eye-tracking technologies).

LYNX is able to amalgamate data from multiple wearables to create a true "digital twin". Users retain ownership over their raw data, while compute-to-data technology ensures safe interactions with third-parties — including independent algorithm providers to revolutionise wearable UX (user experience), and Web3 marketplaces for tokenised rewards.

Users will be able to browse a marketplace of experiences and rewards, personalised to the wearables they use.

🧩 Why wearables?

Wearables collect biological, physiological, or behavioural data and digitally stream this for analysis to provide insights. The wearable market is growing rapidly and the revenue forecast for 2028 is USD 120 billion (up from USD 40.65 billion in 2020). Growth is being driven by gaming, fitness enthusiasm, the Internet of Things (IoT), and the increasing prevalence of chronic diseases.

Our initial focus is on neurotech wearables (i.e. non-invasive BCIs) as this data is highly personal. BCIs are making the most intimate and personal types of data (our thoughts) commercially available. Therefore an ethical, user-centric solution (where users own and control their own data) is key to establish before this technology advances further.

Our ultimate goal is for humanity's brain data (including from invasive BCIs) to not be owned or controlled by private companies. This is particularly important as traditional Big Tech business models exist around data farming. Therefore, there is a great danger to personal privacy and security if we leave this section of the market to these companies.

EEG sensors in particular are increasingly integrated with headphones to provide brain activity measures such as "attention" and "stress", and for training "mental commands". Current neurotech wearables are constrained by the quality of EEG signals and algorithm providers. However, sensors are becoming increasingly sophisticated (see further reading), and our solution enables AI companies to competitively develop algorithms to further advance neurotech.

The integration of wearable signals for holistic analysis has been missing from current solutions. Currently, "biohackers" attempt to hack together such integrations, as the data infrastructure does not currently exist commercially. Both UX (user experience) and mind-body understanding would be greatly improved by developing algorithms that integrate signals from multiple devices. For example, heart rate and physical activity data analysed alongside BCI signals could provide personalised in-depth insights into sports performance and training. The integration of such data is also important for general health monitoring in addition to "digital medicine" (e.g. algorithms for the treatment conditions such as depression and chronic pain delivered through neurostimulation via BCI).