Abstract—The numbers of fit bands and other IoTdevices such as sleep trackers etc. have risen exponentially. With the amountof data now available through the means of these devices about people from allwalks of life has risen greatly too. All of the daily activities of a personamount to something so there needs to be a pattern to the amount of datacollected by the means of these various devices such as a sleep tracker and afitness band. Currently there are few applicationswhich assess the data for the user. A lot of it has to be done by the usermanually.
We are making an application which will monitor this daily activitydata through these devices and assess it and find patterns in it using k-meansclustering in unsupervised learning. The assessed data will be collected in adatabase and store it in cloud and use it as training data and tests will berun on it to find patterns. We further aim to predict user actions and healthproblems through the data collected. Such an application and assessed data canbe useful to various institutions, fitness companies etc.
Keywords—IoT, clustering, unsupervised learning, patterns. I. Introduction (Heading 1)The main aim of this project isto develop an application which uses the collected data and assesses it to findpatterns in it. Mental stress is one of the growing problems of the presentsociety. The number of people experiencing mental stress is increasing day byday.
Stress is a response of our body to prepare itself to face difficultsituations. When a person goes under stress, his nervous system responds byreleasing stress hormones. These hormones make our body ready for emergencyactions. In certain situation it become dangerous and can put a person inserious mental disorder. Long term effects of stress can be chronic. Chroniceffect of the stress causes health problems like hypertension, cardiovasculardiseases and memory problems.
The sense of loneliness and hopelessness may leadpeople to suicide. People may be less likely to notice whether they are underhigh stress or may be generally less sensitive to stress. Stress detectiontechnology could help people better understand and relieve stress by increasingtheir awareness of heightened level of stress that would otherwise goundetected.
For this objective we have designed a smart band device in order todetect different conductance levels of the skin and predict whether the personis under stress or not. But skin conductance alone cannot accurately predictthe stress level in everyday activities. Physiological responses caused bystress can also be provoked by physical activities like running, lacking ofsleep etc.
In order to accurately measure the stress level, classificationshould be made. The fit band will be capable of detecting stress by analyzingdifferent parameters in accordance with skin conductance like activitiestracking, sleep quality etc. The collected data is then transmitted to user’ssmart phone via Bluetooth and upload to web from where it is accessed to findpatterns to further ease the user experience.