LYNX

Biometric health data currently is collected from users of hardware sensors integrated into everyday devices, like watches, rings and headphones. 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, and the increasing prevalence of chronic diseases.

Highly personal biometric wearable data is currently stored and owned by the centralised companies that provide this hardware (e.g. Apple, Garmin, Oura). This personal health data is sometimes sold to third parties and affiliates, with no way for users to provide detailed permissions on commercialisation or participation in scientific research. Furthermore, data is only analysed with proprietary algorithms, and it is difficult to collate data from different devices for holistic analysis (e.g. signals from a brain-computer interface (BCI) and a watch). In return for data, users receive proprietary insights on health, wellbeing and performance. This current state raises ethical concerns for users and also stifles both software innovation and scientific research.

Considering global health in particular, over 50% of adults have at least one chronic health condition (likely to further increase due to post-COVID syndromes already affecting over 100 million people). Currently, many people with long-term conditions turn to specialist tracker apps to help identify triggers and monitor interventions. Current apps require users to manually input data in a diary-style format; however, research shows many people find this manual input difficult and stressful. When surveyed, 90% of people think a monitoring device would help them manage their condition. Unfortunately, due to the current model of wearable data ownership outlined above, it is difficult for scientists and businesses to develop these solutions, as they require large quantities of wearable data. Scientific research is particularly stifled by such unnecessary bottlenecks; resulting in underpowered studies despite leveraging wearable technologies worn by millions (e.g. Apple watch currently has over 100 million users worldwide).

Furthermore, brain-monitoring EEG sensors are increasingly integrated with headphones to provide brain activity measures such as "attention" and "stress", and for training "mental commands". An ethical, user-centric solution (where users own and control their own data) needs to be established before this most intimate and personal type of data (our thoughts) are owned, siloed and commercialised by Big Tech.

Our solution – LYNX – is blockchain-based data infrastructure for biometric wearable data. It enables the preservation of user privacy whilst simultaneously unleashing data from silos for amplified insights, participation in scientific research, and opportunities for reward.

It uses the basics of blockchain - public key cryptography - combined with Compute-to-Data analysis – to ensure data security and privacy (see infographic).

LYNX aims to amalgamate data from multiple wearables to create a biometric "digital twin". Users will retain ownership over their raw data, while Compute-to-Data technology ensures safe interactions with third parties. These will include scientific studies and independent algorithm providers (e.g. specialist AI startups, freelance developers, scientists) to revolutionise wearable UX (user experience). Users all over the world will be able to browse a personalised marketplace of studies and experiences, tailored to their particular health needs and goals. This will reduce bias in medical research by increasing participation globally, and forms a key component of the decentralised science (DeSci) movement.

We have three initial target users our solution will focus on:

1. Users with chronic health conditions (e.g. chronic pain, depression, long covid)

2. Scientific researchers in academia, public health, and industry; including those researching digital interventions (e.g. apps)

3. Health and performance conscious individuals (e.g. for sports training)

The solution will focus on addressing the needs of people with chronic health conditions in particular, through both empowering individual patients and enhancing scientific research. The solution will be particularly effective for rare conditions, where incentives are currently low for the industry to develop solutions, and for which scientists struggle to recruit effectively powered cohorts. The solution will also catalyse research for conditions that predominantly affect people in developing countries, who have been traditionally excluded from research due to a lack of infrastructure.

Sarah (Co-Founder) is both a research scientist and a patient managing two chronic health conditions. She has experience working as a research scientist with people with visual impairment, intellectual impairment and dementia, and has acted as a Trustee for the UK’s Fibromyalgia charity. She also has experience working as a research and development specialist as a consultant and in-house at a large tech organisation. 

Alexandra (Co-Founder) is both a software and data engineer with experience working in large organisations and start-ups. She has a personal interest in wearable devices and continuous monitoring as a way to provide proactive medicine rather than waiting until we are very sick until visiting healthcare professionals.

Our team has carried out user research with people who track their health with wearable devices to understand their needs and pain points. We have also created community Working Groups (including Data Ethics) and encouraged the participation of people with chronic health conditions and research scientists to participate in these.

Further, we have engaged directly with the decentralised AI community by running a hackathon with these researchers, and have been instrumental in catalysing the decentralised science (DeSci) movement through publications (e.g. Nature; a16z), podcasts, and running an educational DeSci guild within Kernel.

As we develop our solution further, we will partner with specialised stakeholders, including research organisations targeting specific conditions and digital therapeutics (e.g. VR solutions for mental health) to catalyse research in these emerging fields. We will also create patient focus groups around specific conditions (e.g. long covid) to iterate our solution in collaboration with these early use cases.

We see three key barriers we will face, which we hope Solve can help us overcome.

Firstly, we need to establish further partnerships with existing wearable technology companies and biometric AI specialists (including university labs, medical companies and tech startups). In order to achieve this, we would like to leverage Solve’s powerful network.

Secondly, we will need to navigate complex biometric data laws across the world, including both storage and brokerage. In order to help us achieve this, we would like to leverage Solve’s legal services from Member and Partner organisations.

Thirdly, our solution will require us to recruit top developer talent. Salaries for blockchain developers are high and talent is in relatively short supply. We would therefore leverage Solve’s access to grants and investments to secure this talent.

Our solution offers a privacy-preserving infrastructure for wearable data. Without leveraging decentralised file storage, as we aim to do with LYNX, there will always be centralised storage of this data (e.g. within a cloud computing provider). Ethically, this means that a very small handful of companies will have control over this. When we consider private neurotech companies (e.g. Neuralink owned by Elon Musk), without the solution we are building, humanity will likely sleepwalk into a situation where mass amounts of brain data are controlled by essentially one wealthy individual.

Further, shifting data ownership and permissions into the hands of individual wearable users enables people to choose who they share their data with, and reward them for doing so (including fractionalised ownership of any resulting IP). Importantly, compute-to-data functionality enables people to achieve this whilst still preserving their privacy. LYNX forms an integral component of the decentralised science (DeSci) movement and will catalyse new ways of doing science at scale.

18-month goals:

1. People will be able to gain mind-body insights through simultaneous analysis of data from body and brain wearables (e.g. sensor jewellery plus EEG headphones).

2. Users can choose from a personalised marketplace of third parties to interact with, including specialist AI for amplified insights, and research studies to contribute to science.

Five-year goals:

1. No third party will ever own or see raw biometric wearable data again.

2. There will be a plethora of decentralised scientific studies that citizens can take part in through their wearable devices.

3. Participants of such scientific studies will share in the rewards of any resulting IP or profits from the data they contribute.

1. Can our solution handle data from an EEG wearable plus anyone body-based wearable? Y/N

2. Does LYNX connect to a marketplace containing both AI offerings and scientific studies? Y/N

3. What percentage of wearables on the market are compatible with LYNX?

4. How many DeSci studies leveraging wearables are available on our marketplace?

5. How much are participants rewarded for contributing their wearable data to science? Including by breakdown of user demographics to understand any biases.

Please see the table below:

Our solution is powered by blockchain and cryptography. LYNX can be thought of as a privacy-preserving data layer that sits between the data provider (human providers; data unions) and the data consumer (AI companies, researchers, medical companies). This is also known as Compute-to-Data (C2D) which addresses and resolves the tradeoff between the benefits of using private data, and the risks of exposing it. Compute-to-Data was coined and invented by Ocean Protocol and LYNX fits in by taking this from its concept stage into a real-world application with biometric data.

C2D allows for the exchange of data while preserving privacy by allowing the data to stay behind a “firewall” only visible to the data provider, yet allows data consumers to run compute jobs on data.

Data publishers will approve where their data is shared (i.e. only data unions looking at x disease) and these data unions will work to approve the algorithms that are allowed to run on the data and then the C2D environment orchestrates remote computation and execution on data while preserving the privacy of the data.

The blockchain-based smart contracts ensure that every data publisher / AI practitioner can verify the proper execution of their algorithm.

Users can benefit from this technology in a number of ways.

• Data owners can monetise their data while maintaining privacy and control.
• Data consumers can access private data without the liability of directly seeing private data.
• AI practitioners & data scientists can access valuable, private data that was previously unavailable, which can lead to more accurate AI models to improve research and business outcomes.
• AI practitioners can also publish their own algorithms, allowing them to earn passive income every time their algorithm is downloaded or run as part of a Compute Job. They can even retain the privacy of the algorithms themselves.
• Businesses and entrepreneurs can quickly launch their own data marketplace for their vertical, and leverage Compute-to-Data to preserve their sellers’ privacy and support more compute & AI flows.

Our leadership team is 100% female, with two women leading this highly technical project in a male-dominated space.

We keep DEI at the heart of what we do. From ensuring the events we host have a diverse set of speakers to gathering DEI data on the people joining our community in order to get a good understanding of what this looks like and therefore we can make an effort to improve diversity in different sectors of our business. Please read our DEI policy for more information regarding this.

LYNX provides a firewall layer to protect precious human biometric data. We will not see or use the biometric data we protect, instead, LYNX acts as a “data broker”. The following diagram helps to visualise the data flow. In this system, data equates to value as with blockchain we can now effectively track data provenance with a public ledger.

The following table walks through the business models we are exploring, whilst making sure we weigh up the key considerations of each one. 

Data consumers:

Data providers:

At this stage, we rely on grants and bootstrapping through our consultancy work. We will look to raise investment when ready to scale up the project, as well as fund our company through software partnerships with hardware companies. What this looks like is providing the software for specialist hardware companies that currently do not have the capacity to build out their own software capabilities. We have had a couple of companies interested in this partnership.

We have received over USD$40,000 in grants so far from organisations such as Ocean Protocol and the Filecoin Foundation. We also recently won a competitive contract with the UK government to explore international development goals using web3. Both funding methods are helping us to bootstrap this project.