Wearable devices, which are digital diagnostic tools, have the potential of detecting many diseases. They also play a role in providing personalized healthcare services. These wearables monitor various physiological, psychological, social and other variables. These wearables have their own problems. The most important challenges are energy consumption, safety, precision, computation, and computation.
For a long time, clinicians have suggested using wearables to diagnose various conditions. Wearables can monitor your activity level at a minute level, and they can also identify your emotional state. In addition, they can be used to detect real-time heart attacks. The problem with wearables, however, is their need for internet connectivity. It is difficult for rural users to use them. Additionally, many in developing countries are unable to afford wearables because of their high cost.
The fitness activity monitors were the first form of wearable tech. These devices can be worn around the wrist and allow for continuous monitoring of many parameters. These data can be used for early diagnosis and to reduce deaths.
More recent developments in wearables include smart tattoos that contain flexible electronic sensors. They can measure heart activity, 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).
An existing digital medical record can also include wearables. 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 allow for real-time monitoring of heart attacks and can detect dyskinesia among PD patients.
Wearables are increasingly relying on machine-learning algorithms. Wearables are able to provide highly customized information about the human physique by using machine learning (ML) methods. Machine-learning can be used to identify emotional and psychological conditions. Wearables that use ML can be used to help clinicians understand patients' behaviors and provide better 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. al. Ko et.al. studied the accuracy and reliability of ECG data and heart beat data from wearable devices. The latter proved to be more accurate. The clinical trial that involved over 60 patients showed that self monitoring using a watchable resulted is a more accurate diagnosis.
Nelson et. al. Nelson et.al. compared Apple Watch and Fitbit data accuracy with ECG data. Results showed that the accuracy of the Apple Watch was better than the Fitbit, which failed to 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 can now help diagnose a variety ailments, thanks to advancements in 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.