Overview

Wearable and portable devices have been around for centuries. Glasses, watches and abacus have made life easier by enabling users to see, tell time or perform more complex calculus wherever they were. From the 1950s and throughout the decades, the creation and development of computers have expanded possibilities in technology, and wearable devices have evolved with the use of electronic components. By the 2000s, devices became smaller and more affordable to the everyday consumer. 

The popularisation of internet access and, more recently, artificial intelligence (AI), has significantly impacted these gadgets. From smartwatches for fitness tracking to sensors that check the biochemistry of our blood (like our glucose or cortisol levels) and hearing aids, such technology can quickly move from a want to a necessity. Monitoring health status via accessories that you can wear makes it possible for people to be aware of their body’s functions and links patients to doctors with quick and robust data.

The gathered information can be processed with AI to correct minor measurement errors, improve accuracy, detect patterns in data, and even predict outcomes. Combining AI and wearable devices can support a personalised approach to diagnosis and treatment.¹ 

Types of wearable health devices

• Fitness trackers: these mainly focus on heart rate monitoring, activity tracking and sports performance;
• Smartwatches: an extension to the smartphone, some versions are able to perform a simple form of ECG, estimate blood pressure, and analyse sleep patterns;
• Medical wearables as biosensors: devices that continuously check for physiological parameters and enable remote patient monitoring systems;
• Specialised health wearables: examples include stress and mood trackers, and posture and movement sensors.

How AI enhances wearable technology

The body of information obtained by wearables, either for medical purposes or personal use, is rich in insights into one’s health. Analysing all sorts of data originating from these gadgets can reveal patterns and target points for diagnosis and treatment, as well as support physical enhancement and health promotion. Such exploration requires thorough analysis through the efficient process of the collected raw data. The outcomes of this analysis provide the system with personalised feedback, which is why artificial intelligence and machine learning can be essential as innovation in healthcare continues to increase.

Data collection and analysis 

Wearables can obtain rich data about their users' health. Such information undergoes several stages of processing in order to transform the data into viable knowledge. Data analysis has a direct influence on the quality and significance of the output of these devices. 

For Health monitoring systems (HMSs), these steps are:²

• Sensing: collecting physiological measurements to convert to digital signals;
• Perceiving: extracting the relevant information;
• Reasoning: scrutinising data and performing analysis via pre-established algorithms;
• Acting: sending warnings or alarms if there is a medical need. 

Predictive analytics 

In the “reasoning” step, various types of data analysis can be performed. One is predictive analysis, which uses statistical models to anticipate the outcomes of a monitored condition. This can be associated with the subsequent choice of treatments or even act as preventive healthcare. 

“Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behaviour. Organizations can use historic and current data to forecast trends and behaviours seconds, days, or years into the future with a great deal of precision.”³

By feeding off real-time monitoring data, predictive models tend to constantly improve their outcome as new information is continuously added to the analysis. These updates aim to produce more precise predictions.

Machine learning algorithms 

The human body is composed of various systems (such as the digestive, respiratory, and nervous systems), and each one can be a source of signals for machine learning to transform into relevant information. Heartbeat, body temperature, oxygen concentration during inhalation, electric pulses, and blood pressure can all become digital information. Algorithms designed to follow specific systems can learn to identify patterns through repetitive observation. This is the starting point for further analysis; scrutinising patterns and recognising unexpected signals generates new data. That is the trend in tech devices, especially wearables.^2,4

Applications in real-time health monitoring

Chronic disease management 

Real-time health monitors can reduce the number of medical visits for the growing population of patients with chronic diseases, thereby reducing hospital costs and supporting people to keep track of their conditions.⁵

Carrying a device designed to constantly monitor specific health markers has expanded possibilities for patient diagnosis and treatments. Some medical wearables, like continuous glucose monitoring systems (CGMs), are already well established. As patients living with diabetes are required to monitor their blood sugar, relying on traditional methods to do so manually can be more troublesome than wearing a sensor that is able to complete the task with minimal to no input.⁵ Such technology can also enable healthcare professionals to remotely monitor the data from wearables, a growing practice since the COVID-19 pandemic. Some equipment can combine CGMs with an insulin infusion device, making it a useful accessory for patients to maintain their glucose levels within a target range.^6,7

Real-time monitoring devices can also be valuable tools for tracking your vital signs, making them a great fit for patients with cardiovascular disorders, as well as supporting relevant investigations. ECGs are an example of a common investigation in the assessment of cardiovascular conditions, and can now be performed by some wearables.⁸ 

Preventive healthcare 

By analysing the user’s health status, wearables can identify anomalies or measures that escape what is considered to be normal. Continued monitoring can alert the user in the case of any concerning information, sometimes even before it progresses to a serious health issue. Enabling the early detection of diseases can help avoid medical emergencies, facilitate treatment, and provide a better long-term prognosis. 

Wearables can also promote health and wellness. Fitness trackers and smartwatches can remind the user of personalised sleeping schedules, as well as to stay active, hydrated and more. Medical devices can also send specific warnings. For example, they can alert one to biochemical imbalances, concerning heart function, abnormal blood pressure changes, or even predict an oncoming seizure.²

Advances in wearables

Besides fitness trackers, smartwatches and the more traditional medical monitoring gadgets, devices vary from experimental to ready-to-use. Technologies that enable drug infusion and vital measurements can come in the form of accessories, clothing, or subcutaneous (under the skin) sensors. 

Electronic tattoos are an interesting concept at first glance. However, tattoo technology has integrated optical sensors to assess UV exposure. Similar technology is also able to measure alcohol levels in sweat, while skin patches can sense temperature. An electronic film, when applied to the surface of teeth, can detect H. pylori.⁹ The possibilities are endless.

Challenges and considerations

As an emerging technology, there are undoubtedly some concerns regarding wearables. These are continuously being actively solved and refined. For example, battery life and power consumption are continually addressed and gradually improved in each updated version of these gadgets. Many companies are committed to enhancing reliability and invest in studies regarding more accurate methods. The user’s experience and the potential learning curve faced rely on not only well-explored and planned strategies but also, public response and feedback. 

On the other hand, security and privacy are important matters in a heavily connected society. Stored personal and sensitive data can be prone to third-party access, leading to many ethical concerns. Granting access to other external apps, advertisers and services may put users in a vulnerable position, leaving them unsure as to how their information is being used.  The development of regulations to ensure user privacy remains relatively in its early stages, and despite constantly evolving, may still not be enough of a guarantee.¹⁰

Summary

From integration in our daily lives for health promotion and wellness to providing creative methods to elevate medical care, the evolution of wearables has reached new milestones. With much room to improve, diagnostic pathways and treatment algorithms still have a way to go when it comes to utilising, yet to rely even more on real-time monitoring approaches. 

Through implementing new materials for comfort, upgrading reliability, and exploring new targets, real-time monitoring wearables can increase their value as they evolve. 

However, as a growing technology, it faces its challenges. For example, when designing a device, companies must be concerned with factors like usage, battery, the user’s learning curve, and reliability. While these matters are the focus of continuous improvement solutions some of the biggest issues in tech remain tricky: security and privacy. This is probably one of the main obstacles to wider engagement, and protecting users will be the most significant promotion for wearables once achieved and ensured. 

Along the same lines is accessibility. Once safe and efficient devices become a reality, high-quality health management tools can become more common to meet medical needs. In the end, prolonged and expensive efforts to increase people’s quality of life may face its biggest challenge: becoming widely and fairly available to all.