Wearable diagnostic devices are digital tools that can detect many diseases. They can also be used to provide personalized healthcare services. These wearables are capable of monitoring various social, psychological and physiological variables. However, they have their own 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 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. Wearables require internet connectivity. It is difficult for rural users to use them. In developing countries, they are also expensive and difficult to access.
The fitness activity monitors were the first form of wearable tech. They can be worn on your wrist, which allows you to continuously monitor a large range of parameters. This data can be used to detect early problems and prevent fatalities.
Smart tattoos, which contain flexible electronic sensors, are one of the latest developments in wearables. They can measure heart activity, muscle function, and sleep. Researchers are also testing microchip implant technology, which is worn on the fingertip. These devices use radio-frequency identification (RFID), and near-field communications.
It is possible to integrate wearables into an existing digital health record. A smart watch could provide real-time information about a person’s heart rate and oxygen saturation. Wearable data can be used to diagnose a wide range of diseases and illnesses such as Alzheimer’s, Parkinson's, and depression. Wearables can be used to monitor heart attacks in real-time and can detect dyskinesia in PD patients.
The wearables industry is increasingly reliant on machine-learning (ML) 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 with ML can help clinicians better understand patient-reported behavior and create more effective treatments. Wearables can also be used to assist patients in making treatment decisions.
Smart wearable devices were found to be effective in treating social anxiety and sleep disorders. A study by Ko et al. 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.
Nelson et.al. compared the accuracy and accuracy of Apple Watch and Fitbit data to ECG data. The accuracy of the Apple Watch was higher than that of the Fitbit which did not meet the accuracy guidelines. Despite this, the results of this study suggest that the ML algorithms can be effective in improving the accuracy of wearable data.
Wearables can be used to diagnose a wide range of conditions thanks to advances in ML algorithms. They can also help determine if symptoms are linked to specific diseases. This can lead directly to more effective treatment.