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Health Prediction Using Wearable Technology

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Wearable Technology, wearable, fashionable technology, wearable gadgets, tech togs, or fashion electronics are smart electronic devices (electronic device with micro- controllers) that can be incorporated into clothes or worn on the body as implants or accessories. Wearable Technology is a sweeping term for gadgets that can be worn on the body, either as an accessory or as part of material used in clothing. There are numerous kinds of wearable innovation yet probably the most prominent gadgets are action trackers and Smart Watches. One of the real highlights of wearable innovation is its capacity to connect with the internet, allowing data to be transferred between a system and the gadget. This capacity to both send and receive information has pushed wearable technology to the front line of the Internet of Things (IoT). This paper discusses about the changing technological advancements happened in the past decade and how the technology of Wearable Smart Watches changed from being a bunch of Fashion accessories to an important Health Care Equipment. In this paper I have also discussed about how we can predict a disease using a prediction algorithm and merging it with the Wearable Technology such as Smart Watches.


Wearable technology (also called wearable gadgets) is a classification of technological gadgets that can be worn by a customer and frequently incorporate following data identified with well-being and wellness. Other wearable tech gadgets include devices that have small motion sensors to take photos and sync with your mobile devices. The Health Care industry has got a huge benefit and a wide variety of opportunities with the introduction of these wearable gadgets such as the Fit-bit, Xiaomi Mi Band etc. These Wearable Smart bands allows their users to track their Heart Rate, calculate steps, and also connects to their Smart Phones providing the Notification Alert features. Talking about Health Care, these gadgets can also keep a track of your sleep and sleeping pattern. Therefore, the concept to Health Prediction can be really beneficial for the upcoming generations. Looking at the Sleeping habits and eating preferences of today’s generation, if there is a device on their wrists which reminds them of their Sleep time and what to eat and what food to avoid, this may bring a huge change in the lifestyle of the people. Making use of a Health Prediction Algorithm and Implementing that concept in these Smart Watches, where the User’s Heart Rate, Sleep Pattern and Steps are Calculated and analyzed and based on that analysis, there is a predictiondone using the Algorithm which tells the user what all diseases he/she may catch in future, if they continue this type of a lifestyle.This type of technology will make the present as well as the future generations more health conscious and therefore the practice of unhealthy food consumptions and bad habits can be stopped.

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The Methodology used is based on an experiment made using some sensors and wearable devices. The Data set used to analyze, comprises of body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities (Table 1). Shimmer2 [BUR10] wearable sensors were used for the recordings. The sensors were respectively placed on the subject’s chest, right wrist and left ankle and attached by using elastic straps (as shown in the figure in attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn and the magnetic field orientation, thus better capturing the body dynamics. The sensor positioned on the chest also provides 2-lead ECG measurements which are not used for the development ofthe recognition model but rather collected for future work purposes. This information can be used, for example, for basic heart monitoring, checking for various arrhythmias or looking at the effects of exercise on the ECG. All sensing modalities are recorded at a sampling rate of 50 Hz, which is considered sufficient for capturing human activity. Each session was recorded using a video camera. This data set is found to generalize to common activities of the daily living, given the diversity of body parts involved in each one (e.g., frontal elevation of arms vs. knees bending), the intensity of the actions (e.g., cycling vs. sitting and relaxing) and their execution speed or dynamicity (e.g., running vs. standing still). The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.

Using the above Box Plot Analysis we can predict the whole data set, like the Maximum Value in the given Data Set is 2.41 and the Minimum Value is 1.29, and the Quartile Deviations are Q1: 1.71 , Q2: 1.89 , Q3: 2.09 respectively.Fig. 2. Line Diagram of Acceleration from the left-ankle sensor from X axis and Z axisAs we can see that the 2 sensors placed at the Left Ankle of the volunteer from 2 different axis have given the above results. Where the Blue line represents the X axis and Orange Line represents the Z axis. Looking at the Line Graph, we can clearly say that the sensor on the Left Ankle when observed from the X axis gave higher output than when observed from the Z axis.


In this Paper we have seen how that evolution of Wearable Gadgets like Smart Watches and Fitness Bands have changed the face of Health Care industry now. The Fitness Bands keeping a track of almost every activity we do in our everyday lifestyle. In this paper we have proposed that using the technology of Wearable Gadgets along with the Prediction Algorithms for various diseases, we can predict a person’s lifestyle and what kind of disease he/she is about to catch in their future. In the future there is a huge scope of this technology and even further research can be done in this area for further advancements. Thus, we can conclude that Predicting diseases with the help of Wearable technology will make people healthier and fit in the future.


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