Wearable devices can be used as diagnostic tools to detect various diseases. They are also useful in providing personalized healthcare services. These wearables can monitor many physiological, psychological, as well as social variables. They also have their challenges. The most important challenges are energy consumption, safety, precision, computation, and computation.
Since long, doctors have recommended wearing wearables to diagnose many conditions. Wearables can track your heart rate and measure your activity. They can also be used to detect heart attacks in real time. The downside to wearables is the need for internet connectivity. This makes it difficult to use them 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 for continuous monitoring of a wide range of parameters. These data can be used for early diagnosis and to reduce deaths.
Wearables are evolving with smart tattoos featuring flexible electronic sensors. They can measure heart rate, muscle function, and sleep. Some researchers are even testing microchip implants, which are worn on the fingertip. These devices use near-field communication and radio-frequency Identification (RFID).
A digital medical record can be integrated with wearables. A smart watch can monitor a person’s heartbeat, oxygen saturation, pulse rate, and valence. Wearable data can help detect many diseases and conditions, such as Alzheimer’s disease, depression and Parkinson's. Wearables allow for real-time monitoring of heart attacks and can detect dyskinesia among PD patients.
The wearables industry is increasingly reliant on machine-learning (ML) algorithms. Using machine-learning (ML) methods, wearables can provide highly personalized information on the human body. Machine-learning techniques can identify psychological states and emotional conditions. Wearables, which can be used with machine learning (ML), can aid clinicians in understanding patient-reported behaviors to develop more effective treatment plans. Wearables are also useful in helping patients make treatment decisions.
Smart wearable devices were found to be effective in treating social anxiety and sleep disorders. Ko et. 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. Additionally, over 60 participants in a clinical trial found that self-monitoring using the wearable device resulted into a faster diagnosis.
Nelson et.al. also found similar results. Nelson et al. compared the accuracy of Fitbit and Apple Watch data with ECG data. The results showed that the Apple Watch had a higher accuracy than the Fitbit. However, the Fitbit failed to meet the accuracy guidelines. Despite this, these results suggest that the ML algorithms may be effective in improving 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.