AI and Machine learning are two important pillars for gathering data from wearable devices. Without Artificial Intelligence Engine, data gathering will be very tough or can say impossible from wearable devices as well as for users.
This is the reason why, wearable application developers are adding artificial intelligence into wearable health applications and wearable health solutions.
Moreover, AI assisted data mining is also essential to the success of an intelligent healthcare platform that ties many smartphones, website, IoT devices and wearables together to gather data and return intriguing health insights of an individual.
Building the platform-machine learning
The platform should contain data points from various medico-sources such as manuals, journals, and public health data to emulate a doctor’s knowledge.
Upon adding patient-specific data, effects of time and location to the platform’s enormous data set, the machine learning system can generate a clinical model of a patient.
Compatible medical wearables and IoT devices can interface with the platform’s API and can be made to exert interesting insights about the data received from the devices.
Wearable systems can be broadly defined as mobile electronic devices that can be unobtrusively embedded in the user’s outfit as part of the clothing or an accessory. In particular, unlike conventional mobile systems, they can be operational and accessed without or with very little hindrance to user activity. To this end they are able to model and recognize user activity, state, and the surrounding situation: a property, referred to as context sensitivity.
1. Wearables for preventive health
Google wants to inject nanobots in your arteries. Don’t be scared already. If they could find a way to take them out, Google X could be the next breakthrough in medtech.
Once injected via capsules, nanoparticles proactively detect and diagnose diseases, cancers, impending heart attacks or strokes based on changes to the person’s biochemistry, at the molecular and cellular level.
After successfully developed nano particles which google is planning to inject in human body than a patient can use wearable like wristwatch on the wrist to gather data from nano particles.
The wearable then feeds the data to the AI engine of the platform and utilizes its machine learning capabilities to detect abnormalities if any in the wearer’s body.
If detected, the wearable reports a potential condition like blocked arteries that could lead to heart stroke or cancerous tumor at a very early stage.
2. Wearables for medical consultation
On the strike of any medical abnormality, the patient can consult with the physician or an Artificial Intelligence doctor. An AI doctor is generally a standalone neural network with deep learning algorithm that can detect ailments faster than an actual doctor can.
Deep Learning algorithm protect the platform which makes lesser errors and maximum detection through self learning module
While it shares the same data as the platform, the machine learning algorithms are stronger in nature, delivering detailed reports.
3. Wearables for medication management
The AI doctor based may prescribe you medication. Under the surface, the neural network that powers the AI doctors upon detection connects to the platform to gather required medical data and prescribe medications to the patient.
The prescription is then sent to the patient’s wearable which he can refer to or even order the medication over using the integrated contact-less payment system with the NFC chip embedded in the wearable.
A wearable health app can even remind you when it is time to take a medicine.
Ethical grounds, protocols, and acceptance
In some cases, machine learning systems need to be amalgamate with software codes to produce improved results.
Depending on the subfield, some structures can’t attain a high degree of accurateness without human intervention, such as in the instance of identifying images. A wild cat and house cat may appear similar to a computer.
In those cases, a crowdsourcing tactic like reCAPCHA aids improve the model further through human efforts.
A challenge is data integration, gathering across dissimilar data sets. The connection between the various schemas must be unstated before the data in all those tables can be joined.
Moreover, AI mobile app developers increasingly using both SQL and NoSQL, structured or unstructured relational database, formats for data storage in accordance to the AI-friendly wearable application development protocols.