Weight sensors based human walking step recognition system : implementation and statistical evaluation

Abstract EN

It is well known that with the growing of the humanity and all the development in technologies, there is an increasing in need for recognition systems.

These systems can recognize people from distinct characteristics in which these are unique for each one individually.

The researchers went to the finger print and eye recognition methods to be adopted as the dominated approaches, yet, these methods suffers from numerous health risks due to diseases transferring.

Therefore, the walking step recognition method has been adopted recently.

This is because each person has different walking style from others.

This paper proposed a human walking step recognition system that adopts group of weight sensors distributed amongst carpet.

The reading data from sensors has been transmitted to the information center for processing.

The data is transmitted through out a wired sensor network that includes sensor nodes and sink node.

The latest node is used to collect the reading data from the sensor nodes.

At the information center, the received data is processed using the proposed recognition algorithm.

This algorithm gives two decisions; either matching with full information about the intruder or no matching.

On the other hand, the proposed system has been designed and implemented using MATLAB simulator.

Throughout this simulator, a database matrix is generated randomly to cover all the probability of walking step patterns available for humans.

This matrix consists of three dimensions; one for users, second for sensor readings (walking patterns), and third for tries.

Each user records numerous walking patterns by passing over the designed carpet several times at different modes just to cover the slightly changes in walking style in terms of modes.

It is important to note, that the carpet include the sensors in between of two layers.

The simulation results show the successful performance of the proposed system with high efficiency and recognition accuracy.

In addition, statistical analysis has been obtained using sampling theorem by adopting sample of 100 employees at University of Technology.

This is done by distributing a questioner form over the employees to evaluate the acceptation of the proposed system by people in terms of health issues and ease of use.

The outcome results show high ratio of accepted people in comparison with rejected