Using Streaming Videos For Human Activity Prediction

In Paper 2, the main problem is that the current way of recording human activity is not accurate enough. Human Activity recognition is very important for health care monitoring which is one of the main reasons why accuracy is very crucial. The proposed solution for this problem is the Monkey Algorithm which is a classification algorithm. The goal of the solution is to use a combination between classification task and nature inspired computing techniques to classify human activity patterns based on smartphone data. The proposed solution allows for nature inspired computing to study human behavior based on wireless devices and the new age of modern technology. After data was taken from the Monkey

Geolocation application can identify the user and their location. The benefits are numerous while staying connected with friends and family. More advantages with geolocators are attaining data while in the proximity of a business. When traveling concurrent to restaurants some apps have the ability to populate the sales, coupons, and other amenities. Innovations for mobile applications is only limited to the

Taking this into consideration, we should aim to develop an effective Machine learning application using available Artificial Intelligence paradigms like voice recognition, image processing, natural language processing and knowledge representation. This application will be integrated with Google’s IoT devices like Google Home, Google Android Phones, smart watches and wearables, which are widely available and in use. These devices will continuously monitor and collect data from its surroundings like the android phone can collect voice calls, text messages, and image data available, Google Home can collect noises around it and location, while wearable devices can collect personal data like heartbeat rate, curiosity and anxiety levels. Since we will be dealing with sensitive data, we can incorporate a feature that these devices will collect data only when needed, or activated with a command like “Ok Google” or “Help Google”. The data from these devices will send to our application over the network, where we can analyze and detect. After which the least we could do is to call an emergency hotline and report the location, phone number and address for a possible domestic violence case

INTRODUCTION: place, we use our five natural senses which includes eye, ear, nose, tongue mind and body to get information about it; that information helps us make decisions and chose the right actions to take. But arguably the most useful information that can help us make the right decision is not naturally perceivable with our five senses, namely the data, information and knowledge that day by day we human beings are gathering information about everything and which is increasingly all available online. Although the miniaturization of computing devices allows us to carry computers in our pockets, keeping us continually connected to the digital world, there is no link between our digital devices and our interactions with the physical world.

In everyday lives the thing which is the most important amongst all is the wellness as it affects behaviors, communications with society, performances and much more. Since as there is a huge progress in the field of technology still there is some lag in the resources of wellness and health of the mind (Madison McCarthy and Petros Spachos). As per a research phone text and call logs were utilized for predicting the personality of a human and the results disclosed that there is a certain enhancement in the accuracy of the prediction through using data of the mobile. (St. Einarsen and M. B Nielsen) Level of stress was also measured with the help of phone’s data (R. LiKamWa et al.). Similar kind of other researches were carried out and the results showed that there is a relation in between the logs of the phone and data of the sensor (Madison McCarthy and Petros Spachos)

We have evolved over millions of years to sense the world around us. When we encounter something, someone or some place, we use our five natural senses which include eye, ear, nose, tongue mind and body to perceive information about it; that information helps us make decisions and chose the right actions to take. But arguably the most useful information that can help us make the right decision is not naturally perceivable with our five senses, namely the data, information and knowledge that mankind has accumulated about everything and which is increasingly all available online. Although the miniaturization of computing devices allows us to carry computers in our pockets, keeping us continually connected to the

Humans are continuously trying to develop a technology that will give artificial intelligence to machines (Gupta, 2013). People have been comparing the human brain to different technologies such as computers and smart devices. Recent years have seen an explosion in mobile web use (Entertainment close-up, 2013). Most devices and apps are capable of completing multiple tasks such as; entertainment, communication, navigation, education and health monitoring. In fact, people tend to rely their meetings, alarms, anniversaries, birthdays etc. all on their smart devices. As a result, people are becoming more occupied in their own digital worlds, anywhere and at

Crime makes security cameras important for almost anyone. People are certainly concerned about security. In the decade between 1990 and 2000, $42.8 billion was spent in the US on the installation and monitoring of home security systems.

Prediction Architecture with Event-Based Sensing The preliminary results demonstrated the feasibility of the Prediction Architecture image sensors. We propose to further explore the prediction architecture in audio and other sensors such like temperature, pressure, or radar sensors by combining the prediction circuits with event-based processing circuits.

With the recent development of new ranges of mobile devices with advanced features such as (context-aware systems, real-time

Nowadays, we are currently in the era of technology and people increasingly use technology in a day-to-day activity, for instance, smart watch, mobile application, GPS or in-vehicle infotainment. Therefore, there are a lot of interactions stemming from those applications towards users, which notify them when applications need to convey the message. In this research, we argue that mobile operating system should be responsible for managing user attention as a resource. As the current operating system uses permission-based models, which enables application to incessantly interrupt users and then allow users to make decision whether they would like to deny or not based on their circumstance. The operating system should use their valuable resource, in which OS incorporates with diverse kinds of applications and information on mobile’s users, to predict the right decision of each interaction based on the user’s current activity. However, there are many key challenges in implementing this notion as well, which will be expounded in this paper.

In this research we try to find another solution to optimize the battery usage of the whole system. We will propose a framework that sends the sensing task to the adequate number of candidates which fits the context of the task best. By using this approach, the system only ask a subset of users who are most likely to perform the sensing task correctly rather than send it to all of them. So without having a major reduction in the accuracy of the sensing task, we will save considerable amount of energy. To illustrate it better, consider we want to sense the light level of lecture room in the Caulfield campus of Monash university. In this scenario, our system first filter the users to find who are currently in Caulfield area. Furthermore, it tries to filter the result more and find the users who are Monash students and also have a device with ambient light level sensor, then ask them to do the task. So as you can see in this scenario, we give the sensing task to small portion of users that saves considerable amount of energy. Also it does not impact the accuracy of the rustle as other users cannot produce any useful data in this context.

Position location systems are becoming increasingly important as add-ons to today’s pervasive wireless technology. Location – aware services are based on some form of positioning techniques. Positioning systems enable context – aware computing with location awareness [1]. Information such as location, group, object that are directly related to an application’s operating environment are often referred to as context of that application which can detect the control and modify its behaviour based on dynamic content is called a contact-aware application [2]. Location in one such context can be of great significance for wireless applications.

Since ages, user interfaces needed the presence of user who should physically manipulate the inputs from the input devices like keyboards. However, it is desired to detect and monitor the physical movements of users within an environment. Automated systems can take the user motions and gestures from

The implementation is made simpler by using IR sensor to detect the person. The system include the IR sensor, microcontroller, LED and a  5V  power is supplied to run the system.