Walking Recognition in Mobile Devices.

Presently, smartphones are used more and more for purposes that have nothing to do withphone calls or simple data transfers. One example is the recognition of human activity, which isrelevant information for many applications in the domains of medical diagnosis, elderly assistance,indoor localization, and navigation. The information captured by the inertial sensors of the phone(accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performedby the person who is carrying the device, in particular in the activity of walking. Nevertheless,the development of a standalone application able to detect the walking activity starting only fromthe data provided by these inertial sensors is a complex task. This complexity lies in the hardwaredisparity, noise on data, and mostly the many movements that the smartphone can experience andwhich have nothing to do with the physical displacement of the owner. In this work, we exploreand compare several approaches for identifying the walking activity. We categorize them into twomain groups: the first one uses features extracted from the inertial data, whereas the second oneanalyzes the characteristic shape of the time series made up of the sensors readings. Due to the lackof public datasets of inertial data from smartphones for the recognition of human activity underno constraints, we collected data from 77 different people who were not connected to this research.Using this dataset, which we published online, we performed an extensive experimental validationand comparison of our proposals.

Robust Step Counting for Inertial Navigation with Mobile Phones.

Mobile phones are increasingly used for purposes that have nothing to do with phone calls or simple data transfers, and one such use is indoor inertial navigation. Nevertheless, the development of a standalone application able to detect the displacement of the user starting only from the data provided by the most common inertial sensors in the mobile phones (accelerometer, gyroscope and magnetometer), is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the mobile phone can experience and which have nothing to do with the physical displacement of the owner. In our case, we describe a proposal, which, after using quaternions and a Kalman filter to project the sensors readings into an Earth Centered inertial reference system, combines a classic Peak-valley detector with an ensemble of SVMs (Support Vector Machines) and a standard deviation based classifier. Our proposal is able to identify and filter out those segments of signal that do not correspond to the behavior of "walking", and thus achieve a robust detection of the physical displacement and counting of steps. We have performed an extensive experimental validation of our proposal using a dataset with 140 records obtained from 75 different people who were not connected to this research.

Mobile Robot Positioning with 433-MHz Wireless Motes with Varying Transmission Powers and a Particle Filter.

In wireless positioning systems, the transmitter's power is usually fixed. In this paper, we explore the use of varying transmission powers to increase the performance of a wireless localization system. To this extent, we have designed a robot positioning system based on wireless motes. Our motes use an inexpensive, low-power sub-1-GHz system-on-chip (CC1110) working in the 433-MHz ISM band. Our localization algorithm is based on a particle filter and infers the robot position by: (1) comparing the power received with the expected one; and (2) integrating the robot displacement. We demonstrate that the use of transmitters that vary their transmission power over time improves the performance of the wireless positioning system significantly, with respect to a system that uses fixed power transmitters. This opens the door for applications where the robot can localize itself actively by requesting the transmitters to change their power in real time.

Self-organized multi-camera network for a fast and easy deployment of ubiquitous robots in unknown environments.

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.