Wearable devices are digital diagnostic tools that have the potential to detect several diseases. They are also useful in providing personalized healthcare services. These wearables monitor various physiological, psychological, social and other variables. They also have their challenges. They face major challenges in computation, energy consumption and precision as well as safety.
Many clinicians have advocated the use of wearables for diagnosing various conditions for years. Wearables can measure physical activity at minute level and can identify emotional states. They can also detect real-time heart attacks. The problem with wearables, however, is their need for internet connectivity. They are therefore difficult to use in rural areas. In developing countries, they are also expensive and difficult to access.
The first wave in wearable technology was fitness activity trackers. They can be worn on the wrist, which allows for continuous monitoring of a wide range of parameters. This data can help to diagnose early and decrease fatalities.
Wearables are evolving with smart tattoos featuring flexible electronic sensors. They can measure heart rate, muscle function, and sleep. Some researchers even test microchip implants that are placed on the fingers. These devices are based on radio-frequency identification (RFID) and near-field communication.
A digital medical record can be integrated with wearables. One example is a smart watch that can track a person's heart beat, oxygen saturation, and valence. Wearable data can be used for diagnosing a range of disorders and illnesses, such as Alzheimer's, depression, and Parkinson’s disease. Wearables can detect dyskinesia, a condition that can lead to heart attacks, and can also be used in real time to monitor them.
Wearables are increasingly relying on machine-learning algorithms. Wearables can offer highly personalized information about the human body by using machine-learning (ML). Machine-learning algorithms can identify psychological states as well as emotional conditions. Wearables that use ML can be used to help clinicians understand patients' behaviors and provide better treatments. Wearables can also be used to assist patients in making treatment decisions.
Wearable smart devices can be used to treat social anxiety and sleep disorders. Ko et. Ko et al. compared ECG data to heart rate data obtained from wearable devices and found the former was more accurate. In a clinical study with over 60 people, self-monitoring with a wearable resulted in a quicker diagnosis.
Similarly, a study by Nelson et al. Nelson et.al. also examined the accuracy and precision of Fitbit and Apple Watch data in comparison to ECG. The accuracy of the Apple Watch was higher than that of the Fitbit which did not meet the accuracy guidelines. Despite this, this study suggests that ML algorithms could be used to improve the accuracy of wearable data.
With the advancement in ML algorithms, wearables can now be used to help diagnose a variety of ailments. They can be used to identify symptoms that are associated with particular diseases and can help to develop more effective treatments.