Wearable Devices Analytics (Essay Sample)

Wearable Devices Analytics Student Name: Course: Institution: Instructor: Date: Wearable Devices Analytics Abstract Due to their personal nature, wearable gadgets and the data they collect offer a unique opportunity to study user behaviour and anticipate future needs. This data will increase dramatically over the next few years as wearable devices become very popular. With the explosion of personal data, analysis becomes as difficult as maintaining an up-to-date knowledge base. This requires the development of big data analytics methods for wearable devices. Proposing big data analysis approaches that update knowledge bases and provide users with customized suggestions based on data analysis. We have developed a tailor-made adaptive analysis method for data management and transformation. This approach also responds to information consumption APIs in real time. We will use mapreduce as a big data technique so that the data can be used for long-term research for various applications in the future. Wearable sensors are increasingly being used to monitor patient health, aid in illness diagnosis, and anticipate and, in some cases, enhance patient outcomes. Clinicians describe patient symptoms and assess functional abilities using a variety of self-report questionnaires and well-known tests. These evaluations are time-consuming and expensive, and they rely on subjective patient memory. Furthermore, measurements may not fully reflect the patient's functional ability at home. Wearable sensors can detect and quantify precise movements in a variety of applications. Key Terms: wearable technology, digital healthcare, quantified self (QS), deep learning (DL), neural network (NN), Big Data. Introduction In today's environment, wearable devices generate large amounts of data that are not being used and analysed efficiently. Due to the individual nature of mobile gadgets, this data is particularly different. Wearable devices are becoming ubiquitous and generating large amounts of data that cannot be analysed using traditional methods. Target data includes all the gadgets that revolve around the user, which can include digital wristbands, watches, glasses, and wearables. The data collected from wearables has yet to be thoroughly investigated or evaluated, allowing users to make informed decisions. This year, about 15 million wearable smart devices will be sold, totalling $800 million [1]. Since the wearable or sensor's ability to periodically analyse data is still early, all important sensorial data processing is performed on remote systems. Data storage, processing limits, and multitasking require consuming data on remote servers and then maintaining it at scale. The same data is then used in conjunction with the new data to extract hidden trends and states from the historical data and their associations. There are few strategies and approaches to using sensory data obtained from wearable devices to make decisions and recommendations. Literature Review and Background Wearable sensors generate sensory data that can be automatically detected, collected, classified, and evaluated. To learn from the environment, sensory inputs can be connected to new information. Sensory data from sensors and wearable smartphones is used in healthcare to make recommendations, track physical health activities, generate real-time alerts, and motivate users Prepare quickly and on time. Sensors are also widely used in buildings, institutions and bridges to monitor humidity, temperature, heat dissipation and maintenance issues. Sensor data is collected and alerts are generated based on historical analysis and learning algorithms to take precautions before any damage or failure occurs. This work develops a mechanism to obtain data collected by mobile devices and operate them according to customer needs and wants Adaptive analytics model is established in which users can describe their needs, and data is retrieved and presented in a way Allows users to make decisions. The data will be stored in a Big Data warehouse designed to handle the data stream generated by mobile devices. This will ensure that a large data set will be analyzed using this method. Personalization with the advent of wearable computing, Big Data is becoming a reality. Examples of wearable devices are medical gadgets, sensor bracelets, headsets, glasses, smartphones, and clothing. All of these gadgets acquire large amounts of data through human interaction and sensors. Wearable computers will be the next frontier in data creation and use. They generate data in real time, bringing in big data because it can handle these things. Big data refers to data that is increasing in speed, volume, variety, and authenticity, implying that traditional procedures and database management technologies are inefficient or ineffective. The problem stems from volume, generation rate, and redundant/noise data. Big data engineering includes new technologies that handle the flood of data through parallelism, redundancy, machine learning, and pattern recognition techniques, as well as processing huge data using the infrastructure of big data. Google Glass [2], Pebble [3], Fitbit One [4], heart rate monitors, smartphones, smart clothes and other recently created gadgets involving human interaction evidence demonstrate the relevance of search and application to the development of IT and communication. Different companies are entering the market from different aspects, such as wearable smartphones, Bluetooth pairing and apps. Data Sources and Types Wearable technology is next (data) Wearable technology refers to any device that can connect wirelessly to the Internet, satellite, other mobile device, or other connection point small enough to worn on the body, like a watch. Google Glass and Apple Watch are two popular examples of wearable technology. Google Glass, Pebble, Fitbit One, heart rate monitors, smartphones, smart clothing and other gadgets recently developed in relation to human contact demonstrate the relevance of the study. and computer applications. Medical wearables play an important role in monitoring and improving health. Bracelets and headsets/glasses are the next most popular wearables. It would be a huge waste of money if the data provided by the wearable was not used, so the need to mine the data for user personalization is huge. To properly leverage the data provided by wearables, a more data-driven strategy is needed. To capture continuous real-time activity in multi-sensory contexts, a knowledge-based solution is also required. Big data technology can enable efficient data-driven decision making. With devices under constant monitoring, a Big Data solution capable of evaluating large amounts of data is required. Personalization With the advent of wearable computing, Big Data is becoming a reality. Examples of wearable devices are medical gadgets, sensor bracelets, headsets, glasses, smartphones, and clothing. All of these gadgets acquire large amounts of data through human interaction and sensors. Wearable computers will be the next frontier in data creation and use. They generate data in real time, bringing in big data because it can handle these things. Big data analysis needed Big data refers to data that is growing in speed, volume, variety, and authenticity, implying that traditional procedures and database management technology ineffective or ineffective. The problem stems from volume, generation rate, and redundant/noise data. Big data can provide them with tailored recommendations and recommendations. And the combination of Big Data and wearable technology can be very effective for marketers. People will demand more wearable technology as it becomes more common. They will expect more personalized results according to their preferences and needs. And they will expect them to arrive faster and more consistently than ever before. Big data will be present on both sides of this issue. Demographic data, usage and consumer expectations will be poured from these gadgets; In turn, this data will have to be processed to give customers what they want. As wearables are worn more often, it provides more information, but expectations also increase. Data on wearable technology has yet to be studied and analyzed in depth. However, we are that would, of course, be Big Data - a term that refers to the large amount of information being collected, its speed and volume, and overall reliability. This constant flow of data will require the ability to process and store big data. This includes difficulty, along with computer limitations and multitasking requirements. The problem here is speed and volume, not speed. Storing an ever-increasing amount of data is a challenge; evaluate it and have a much more complex dynamic recommendation system. This is where big data technologies and techniques come into play. Cloud computing and parallelism are needed to manage this flood of data, while pattern recognition and machine learning-based prediction are used to gather information and make recommendations. Framework The main purpose is to maintain a personalized knowledge base for the user and make data-driven recommendations. A) Data collection and management Data collection: Data will be collected through the mobile application and then sent to the cloud. The data collection takes into account the physical sensors attached to various wearables such as accelerometer, GPS, illuminator, etc. Data is stored and transferred from the Android application to the data collection component. Data Transformation: Data Transformation layer receives raw data and organizes part into csv or text files based on sensor classification. Streaming data is captured for fast communication between cloud and mobile devices, app for devices Data validation: Input is partially structured data from the transfer component change. As a result, cross-check the sensor data and eliminate duplicates. It is then sav...