A bio-digital twin is a virtual model of a person's biological systems developed by Hume. Using data collected by biosensors from wearable devices, body composition scales, and lifestyle information, Hume constructs a digital avatar that visualizes your health. One of the key features of Hume's Bio-Digital Twin is its ability to explore and analyze your health and well-being and predict possible future outcomes. Your Digital Twin is useful in many applications, such as creating a weight loss program, preventing metabolic diseases, or managing chronic diseases such as diabetes. So your Digital Twin helps you better understand and manage your health by combining health data and lifestyle data.
How to use the Hume Bio-Digital Twin (BDT)?
You'll need access to the Hume Health app to use the Hume Digital Twin. Once you have the app, connect your wearable device and your body composition scale. Our machine learning AI will then create a virtual model of your health based on the massive amount of data our scientists have used to train it.
Steps to create your digital twin:
- 1. Download Hume Health
- 2. Register an account and log in (if you have an account, log in directly with your MyHealth/Hume Health credentials)
- 3. Synchronize your bio-sensors
- 4. Choose your goals
- 5. Get weekly insights into your progress toward your goals, learn what's limiting you, and follow personalized recommendations to continue improving your health. .
Examples of digital twins:
Let us first take a look at the actual use of the Digital Twin.
The Application of Hume's BDT
BDT can support you in a wide range of goals. Some of the possible applications of the Digital Twin are
- Weight loss: The data collected by your digital twin can give you tailored advice on how to optimize diet and exercise to achieve maximum weight loss. Additionally, digital twins can provide real-time feedback on your progress, so you know immediately if your actions are working .
- Metabolic disease prevention: Your Digital Twin can help you prevent chronic diseases by giving you personalized advice based on your health data. For example, let's say your Digital Twin suggests that certain lifestyle changes would help reduce the risk of developing a certain chronic disease. In this case, you are more likely to take those actions and try to prevent the disease.
- Chronic disease management: By using machine learning algorithms, your digital twin can track changes in your health over time and provide insights into potential health risks associated with chronic disease. Digital twins can also identify trends in a person's health data, allowing doctors to better understand how to prevent or treat chronic diseases .
- Age management: Your digital twin can help you age healthily by helping you identify health risks and proactively make lifestyle changes. In addition, your digital twin can provide healthcare professionals with a complete picture of your health so they can make more informed decisions about treatments and interventions. .
The Limits of Hume Bio-Digital Twin 1.0
Like any other machine learning model, Hume's Bio-Digital Twin also has some limitations in its first version. Some of the possible limitations of Hume's Bio-Digital Twin are the following :
- Dependence on data: The Bio-Digital Twin 1.0 technology is a machine learning model trained by our data scientists on a large corpus of healthcare data, although this is just the beginning. So the quality and accuracy of the model's answers depend on the quality and variety of data it receives. If the model does not have a comprehensive dataset available, it may give answers that are not as relevant or accurate .
- Limited understanding: While BDT 1.0 technology learns from hundreds of thousands of data sets, it still needs to develop an understanding of more health topics. However, as the tool gets smarter with BDT 2.0, BDT 3.0 and beyond, we expect to see significant improvement over time as it leverages the power of deep learning .
- BDT 1.0 Errors: Artificial intelligence can make errors due to resource or technical limitations. BDT 1.0 is continuously learning, and the potential for computational errors will decrease over time .
Hume's Bio-Digital Twin technology is a powerful and versatile health management tool that can only get smarter with more and more data sets recorded.