Sensor data, such as accelerometers in smartphones, allows us to identify what activity a person is doing in real time.
This is the basis for the development of many potential applications in health, wellness, sports, etc.
Nowadays, with the growth of Internet of Things, almost every person has some gadget that monitor their movements.
This information is collected along all the day and can be used to analyze the activity of a person. Counting the time that they stand, walk or run to make appropriate plans to improve their physical activity.
Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions.
Using predictive analytics to recognize human activity can provide us a better understanding about human behavior. However, due to the complexity of human activities and the existing differences between two individuals, this task still remains as a big challenge.
In addition, neural networks can manage large amounts of information and treat them correctly.
Machine learning, and particularly neural networks, are a perfect tool to find the patterns that determine the physical activity of a person.
One of the best features of neural networks is the ability that they have to generalize the knowledge that an individual provides and, at the same time, learning about particularities that they may have.
For that reason, neural networks are one of the best tools for activity recognition problems.
The next image shows a summary of the process that would be followed to obtain a predictive model for this case.
Firstly, the data about the activity of an individual are arranged in a database. This database is analyzed by Neural Designer to find patterns. As a result, we obtain a predictive model.
Neural Designer is a software that implements neural networks for the analysis of data sets. It is able to manage information arranged in a large number of variables, find patterns between them and make accurate predictions based on them.