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 and allows to collect data to analyze the activity of a person:
Activity recognition aims to detect the actions of one or more agents from a series of observations on the agents' actions and the environmental conditions.
Counting the time that a person stands, walks or runs allows to make appropriate plans to improve their physical activity.
However, due to the complexity of human activities and the existing differences between two individuals, this task still remains as a big challenge.
Neural networks, due to their ability to generalize the knowledge that an individual provides and learning about particularities, are a the perfect tool to determine the physical activity of a person.
The next image shows a summary of the process that would be followed to obtain a predictive model for this case.
The process of activity recognition has different phases.
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.