Smart watches have the potential to contribute to health in daily life: enabling self-monitoring of public activities; Obtaining feedback support activities with surveys to spot patterns of behavior; and supporting tri-directional communication with health care providers and members of the family. However, smart watches are in the early stages of developing wearable Health Technology and research with these devices.
A wearable device can be defined as a mobile electronic device worn as an accessory or unobtrusively embedded in the users clothing . Talking in general, wearable devices has the power to adopt the technologies of advanced biosensors and of course wireless data communication that allow the wearer to access and transmit information in all sectors of human endeavor.
Given the functionality of miniaturized biosensors capable of wireless communication, these devices are developed to be innovative, non-invasive monitoring technologies for continuous and autonomous transmission of physiological data . As these devices multiply in a very clinical domain, they also have the potential and power to provide caretakers with the information they require to improve the quality of the health care, change and facilitate clinical workflow, manage and treat patients remotely, collect greater health data, and deliver more meaningful healthcare to patients .
Wearable Health Technology
While the miniaturized health sensors enable consumers to observe health conditions by themselves and remember of their own healthcare, they also touch the enterprise market by delivering superior analytics for clinical and medical research. Once all the privacy and major security concerns are addressed and processed to standardization in health related communication protocols are put into place, the next-gen of wearable healthcare are going to be ushered in.
How This Wearable Health Technology Interconnected?
There’s no doubt that with the recognition of smartphones that can manage virtually every aspect of our lives, The technical trend is to urge more “connectivity” in smaller and smaller packages. Wearable Health technology provide convenient ways to monitor many physical features, providing a large number of medical solutions. Not only are these devices easy for the patron to use, but they provide real-time data for physicians to research additionally.
Before we start to know what goes inside wearable technology, it would be first important to know how they work as an entire. We may be confused the way to define wearable tech because were too exposed to them that we didnt even see a requirement to define them.There are multiple iterations of wearable technology around us. they’re going outside and beyond the casual headsets that were using. In fact, you will already own one and not realize it.
Eye-wears are getting down to have wearable variants. they have an inclination to possess sensors of their own that can detect, monitor, and track various things. Wristbands are getting the fitness savants constant companions during exercise. They’ll monitor various statistics around our body for health purposes. Smart watches are like wristbands, in this we wear them on our wrists. However, smart watches may be called the right smartphone companion because of its sheer capacity to answer and take calls.
Video cameras are mostly attached to head-mounted displays and glasses that allow us to trace and monitor certain movements of objects. Sensors can be attached around the wearable device, allowing them to see different activities in the area including track speed, brain activity, heart activity and muscle activity. We are able to mostly see them in health-oriented devices. Miniature computers are inside some wearable devices, like how smartphones have miniature processors within them. These are found in smart watches and other wearables that are purported to help us interact with other objects within the vicinity.
Latest Trends In Wearable Health Technology
Wearable ECG Monitors
Heart rate monitors are now part and parcel of smart watches and fitness trackers, but the Electrocardiogram (ECG) is the new flagship sensor in this era.This technique is designed to help people monitor their heart health and can detect atrial fibrillation (aphid), a serious medical condition and the leading cause of stroke. Almost every wearables that is in the market today have optical heart rate monitors. It is the device that uses a flashing LED to penetrate the skin and detect blood flow. When light is reflected through the bloodstream, it is captured by the sensor and The algorithm then works to generate heart rate data.
Weight Control and Monitoring
Monitoring regular physical activities using these wearable devices has now become a popular way to help the population increase activity intensity and their calories. There is a vast and steady growing interest among various health consumers to use these wearable devices, especially consumer related wearable devices, to learn weight control activities and their net results.
Although there are studies suggesting that wearable devices may use a stimulus mechanism to increase user activity, the use of a wearable device for weight loss has not been confirmed that’s why Evidence-based studies are lacking and as as result of this Technology Behavioral changes (such as physical activity, healthy eating) and anthropological changes (such as body weight, body mass index, waist circumference) are eating the body like a bug.
The results of the needed study sought to provide a great scientific evidence for the alertness and effectiveness of using these wearable device systems for the weight control.
Patients with Brain and Spinal Cord Injuries
Patients with brain and spinal cord injuries need exercise to improve motor recovery. Often, these patients are not qualified to monitor or assess their condition and need the guidance of a health care provider. Therefore, there is a need to transmit physical data from patients to physicians in their home environment.
Chronic Pulmonary Patients
As a chronic disease, obstructive pulmonary disease usually worsens with time, and therefore requires extensive, long-term lung rehabilitation practice and patient management. A team of great researchers has designed a remote well sophisticated rehabilitation system for a gross sensor-based application for the needed patients with chronic breathing and asthma difficulties.
The system includes a set of rehabilitation exercises specific to lung lung patients and provides exercise tracking progress, patient performance, exercise appointment and exercise. Patients in the study can obtain accurate pulmonary extensive Guidance from sensory data and Further evaluation studies are needed to ensure that the proposed remote system can provide a convenient and cost-effective alternative to remote healthcare rehabilitation.
Future Of Wearable Health Technology
Hemoglobin is a red protein responsible for transporting oxygen in the blood. Wearable technologies provide portable, non-point-of-care methods for measuring the concentration of hemoglobin. Wearable devices have the potential to enhance the quality of care.
Unfortunately, a study has shown on widely available noninvasive point-of-care of hemoglobin monitoring equipment is systematically biased and even reliable for transplant decisions that replicates wearable devices with better accuracy are needed for future development. .In the future there is a possibility of developing new wearable delivery and drug delivery systems inbuilt for blood pressure management.
Artificial Intelligence (AI)
The advancement of wearable technology and the possibilities of using AI in health care is a concept that has been researched by many studies. The availability of Smartphone and wearable sensor technology leads to faster access to human content data and machine learning is evolving into technology that maps that data into diagnostics.
Another study that automatically applies Parkinsonian shocks by proposing a machine learning algorithm to estimate the Unified Parkinson’s disease Rating Scale (UPDRS) (Zion et al., 2017). In this study, the tremor signals from varied 85 patients with “Parkinson’s” disease (PD) were measured using the wristwatch-type wearable portable device, which included the accelerometer and gyroscope for their respective uses.
Nineteen features were collected from each signal and a pair correlation strategy was used to reduce the number of feature measurements. With selected features, the Decision Tree (DT), Support Vector Machine (SVM), Differential Analysis (DA), Random Forest (RF) and K-Near-Neighborhood (KNN) algorithm are explored for automatic scoring of Parkinsonian vibrations. The performance of job classifiers was analyzed using accuracy, recall and accuracy and compared with those found in a similar study.
As machine-learning algorithms are constantly increasingly and are used to support the clinical decision-making, is quite reliably quantifying of their prediction accuracy and proves it to be vital. Invariable results can confuse doctors and data scientists that’s why Cross-recognition (CV) is a standard method of estimating the accuracy of an algorithm that does not observe data during training in the algorithm.