The mix of Artificial Intelligence (AI) and wearable health devices has changed how patients and healthcare providers monitor and address health. Initially used for fitness tracking, these devices are now essential tools for overall well-being and managing chronic conditions.
Wearables give users better insights into their health, helping them manage aspects like sleep, stress, and daily activities. For healthcare professionals, these devices offer valuable data that can help in diagnosing, treating, and tracking progress on ongoing conditions.
Understanding AI-Driven Wearables
Wearable health devices are electronic gadgets intended for body use that monitor, track, and periodically analyze different aspects of a person’s health. Supplied with sensors and software, these devices gather data on physical activity, vital signs, and other health metrics. They usually connect to smartphones or cloud platforms to store, process, and present the collected information to users or healthcare providers.
Wearable devices integrate a variety of sensors, including temperature sensors, accelerometers, optical sensors, and biometric sensors, to monitor human signals continuously. Although these sensors might not always match the accuracy of hospital-based equipment, they are generally considered suitable for certain uses. The data gathered from these devices, along with human interaction, offers valuable insights for Machine Learning (ML) algorithms, facilitating pattern detection in healthcare scenarios such as activity recognition, fall detection, stress monitoring, fitness tracking, vital signs assessment, and disease diagnosis. Even with substantial research and the rising popularity of wearable technologies like smartwatches, only a few ML applications have successfully made it to market (Sabry et al., 2022). One example is the irregular rhythm notification feature in the Apple Watch, which gained U.S. FDA approval in 2018, albeit with significant warnings and precautions attached.
Technological advancements, particularly in wearables and AI, are transforming healthcare by allowing real-time, continuous monitoring of patient’s health and improving disease detection and recovery tracking. Yet, challenges remain in managing the extensive data these technologies generate and combining it into electronic health records (EHRs). Despite this, AI’s potential to revolutionize healthcare, similar to past industrial and digital revolutions, is huge. With improved data processing, computing power, and machine learning models, AI can help healthcare professionals diagnose and treat patients more effectively. Also, advancements in telemedicine are making healthcare more accessible, particularly in rural areas. As the global population ages, the demand for telemedicine and remote monitoring will rise, highlighting the need for scalable healthcare solutions, especially for the elderly, to address the growing burden of chronic diseases (LaBoone et al., 2024).
Wearable health technology offers many benefits but also faces challenges. One major issue is the accuracy and reliability of the data these devices collect, especially as biosensors are still being developed to monitor biochemical signals. These devices need to improve their sensitivity, selectivity, and accuracy. There is also a risk of inaccurate readings, which could result in false alarms or missed early detection of health issues. To address this, wearable devices should be validated with standard methods like blood chemistry. Another challenge is privacy and security, as wearable devices collect sensitive health data that could be vulnerable to breaches. Securing data protection is crucial for healthcare providers and manufacturers. AI is helping to improve the accuracy and efficiency of wearable sensors by correcting errors in data, such as heart rate inaccuracies. AI can analyze large amounts of data from these devices, helping healthcare providers detect patterns, predict health outcomes, and make informed decisions. For example, AI can predict heart attacks or strokes by examining data like activity level, sleep, and heart rate.
It is also useful in handling multimodal sensing issues, where one signal is influenced by others. Machine learning models can identify patterns and isolate signals, improving measurement accuracy. AI techniques, such as deep neural networks and signal processing, can also filter out unwanted signals and improve biomarker detection.
Lastly, energy consumption is a key challenge in wearable sensor networks, particularly with non-rechargeable batteries. Efficient energy usage and optimization of the sensor network design are important. AI can help optimize the design and management of these networks, guaranteeing accurate data collection while conserving energy. AI-based techniques can also predict sensor failures and optimize resource allocation (Shajari et al., 2023).
Their Benefits
We saw that wearable technology tracks metrics such as sleep score, step count, heart rate, and more. These measurements show how active a user is and how close they are to reaching their health goals.
Let’s see what makes these systems so effective:
• Predictive Analytics: AI can make wearables smarter by predicting future health problems. For example, if something looks off in a user’s vitals, the AI can warn them and send the information to their doctor to help avoid a potential health issue.
• Personalization: AI-powered wearables give personalized advice to users. If someone has a condition like diabetes, the device can suggest exercises or foods that help control their glucose levels, making it easier for them to manage their health.
• Telehealth Integration: AI and wearable devices make remote healthcare easier. Doctors can, in fact, track changes in a patient’s health in real time. In this way, they don’t need to wait for lab results or tests to make decisions.
• Budget-friendly: By using AI with wearables, healthcare costs go down. Patients don’t need as many expensive hospital visits or tests, and doctors can focus on patients who need urgent care while managing others virtually.
• Contextual Insights: AI wearables are smarter than regular ones because they can consider things like the weather or noise around you. For example, the device might suggest indoor exercises if it’s too hot outside or warn you about loud sounds that could damage your hearing.
Takeaways
AI and wearable technology are changing healthcare for the better. Wearables help people track their health and AI makes them smarter by improving accuracy and providing personalized advice. While there are challenges like data accuracy, privacy, and energy use, AI-powered wearables can help doctors make better decisions and lower healthcare costs. As technology keeps improving, these devices will become even more important in making healthcare easier and more accessible for everyone.
References
- LaBoone, P. A., & Marques, O. (2024). Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights, 4(2), 100294.
- Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022). Machine learning for healthcare wearable devices: The big picture. Journal of Healthcare Engineering, 2022, 4653923.
- Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023). The emergence of AI-based wearable sensors for digital health technology: A review. Sensors (Basel), 23(23), 9498.
- U.S. Food and Drug Administration. (2018). De novo classification request for ECG app. (https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN180044.pdf)
