Wearable diagnostic devices are digital tools that can detect many diseases. They can also help provide personalized healthcare services. These wearables are capable of monitoring various social, psychological and physiological variables. However, these wearables come with their own challenges. They face major challenges in computation, energy consumption and precision as well as safety.
Since long, doctors have recommended wearing wearables to diagnose many conditions. Wearables are able to measure your physical activity and identify your emotional state. These wearables can also detect and monitor heart attacks. The downside to wearables is the need for internet connectivity. This makes them difficult to use in rural areas. Additionally, many in developing countries are unable to afford wearables because of their high cost.
The first wave in wearable technology was fitness activity trackers. They can be worn on the wrist which allows continuous monitoring of a wide variety of parameters. Such data can be used to make early diagnosis and reduce fatalities.
Smart tattoos, which contain flexible electronic sensors, are one of the latest developments in wearables. They can measure heart rate, muscle function, and sleep. Some researchers even test microchip implants that are placed on the fingers. These devices use near-field communication and radio-frequency Identification (RFID).
You can integrate wearables into your existing digital medical records. For instance, a smart watch can provide real-time information on a person's heart rate, oxygen saturation, and valence. Data from wearables can be used in diagnosing many disorders, such as Alzheimer's and Parkinson's diseases. Wearables can be used to monitor heart attacks in real-time and can detect dyskinesia in PD patients.
Wearables are increasingly relying on machine-learning algorithms. Wearables can provide highly personalized information about the body through machine-learning methods (ML). Machine-learning techniques can identify psychological states and emotional conditions. Wearables can be used by clinicians to better understand patient behavior and help them develop more effective treatments. Wearables can also help patients make treatment choices.
It has been shown that smart wearable devices can improve treatment for social anxiety disorders and sleep disorders. Ko et. compared the accuracy of heart rate data from a wearable device to ECG data, and found that the latter was more accurate. Furthermore, a clinical trial with over 60 adults showed that self-monitoring using a wearable led to an earlier diagnosis.
Nelson et.al. also found similar results. Nelson et.al. compared Apple Watch and Fitbit data accuracy with ECG data. The accuracy of Apple Watch was greater than that found for the Fitbit, which didn't meet the accuracy guidelines. Despite these results, it is clear that the ML algorithms are effective in increasing the accuracy and reliability of wearable data.
Wearables are now able to diagnose many ailments thanks to the development of ML algorithms. They can also be used as diagnostic tools to help identify the symptoms of specific diseases. This could lead to more effective treatments.