Fall risk assessments provide a useful tool to prevent morbidity and mortality provoked by falls. Nowadays, these assessments are usually performed manually by the medical staff. This approach has three main drawbacks: (i) it is time consuming, so it is only performed a few times per volunteer during their rehabilitation process; (ii) it requires supervision by medical staff, so assessment at home or preferred environments is not feasible; and (iii) fall risk is evaluated in a global way, so imminent fall risk is not available for decision making in assistive navigation. In this paper we propose an imminent fall risk estimator for rollator's users that can be automatically obtained on the fly. Its main advantages are: (i) it can be used in everyday conditions in any environment; (ii) it does not require assistance of medical staff; and (iii) it is suitable for a variety of users with minimal configuration changes. We have validated our estimator with a set of volunteers (n=10) presenting different physical and cognitive disabilities. Although the number of volunteer is limited, results show that our estimator is coherent to two traditional, well accepted assessments: the Tinetti Mobility Test and the walking speed.

The assistance to people during rehabilitation has to be adapted to their needs. Too little help can lead to frustration and stress in the user; an excess of help may lead to low participation and loss of residual skills. Robotic rollators may adapt assistance. The main challenge to cope with this issue is to estimate how much help is needed on the fly, because it depends not only on the person condition, but also on the specific situation that they are negotiating. Clinical scales provide a global condition based estimation, but no local estimator based on punctual needs. Condition also changes in time, so clinical scales need to be recalculated again and again. In this paper we propose a novel approach to estimate users' condition in a continuous way via a robotic rollator. Our work focuses on predicting the value of the well known Tinetti Mobility test from spatiotemporal gait parameters obtained from our platform while users walk. This prediction provides continuous insight on the condition of the user and could be used to modify the amount of help provided. The proposed method has been validated with 19 volunteers at a local hospital that use a rollator for rehabilitation. All volunteers presented some physical or mental disabilities. Our results successfully show a high correlation of spatiotemporal gait parameters with Tinetti Mobility test gait (R2 = 0.7) and Tinetti Mobility test balance (R2 = 0.6).

Gait analysis provides insightful information about people condition, progress of rehabilitation treatments and fall risk. This analysis is frequently performed in very specific conditions, because it often implies the use of treadmills, cameras and/or wearable sensors. Alternatively, gait can be analyzed using a smart rollator, equipped with a basic set of sensors. This work proposes a methodology to obtain relevant gait parameters using a simple, cheap source of information: wheel odometry and handlebar force sensors. The main advantages of this approach are that users are not bothered with extra equipment. Besides, monitoring can be performed anywhere, even during everyday conditions and for extended periods of time. We have tested our system with a set of rehabilitation patients with different disabilities. Preliminary results have successfully proven that extracted parameters are coherent with reported effects of their specific condition.

The fully flexible navigation of autonomous vehicles in industrial environments is still unsolved. It is hard to conciliate strict precision requirements with quick adaptivity to new settings without undergoing costly rearrangements. We are pursuing a research project trying to combine the precision of laser-based local positioning with the flexibility of vision-based robot motion estimation. An enhanced circle approach to dynamic triangulation combining laser and odometric signals has been used to improve positioning accuracy. As regards to vision, a novel technique relating the deformation of contours in an image sequence to the 3D motion underwent by the camera has been developed. Interestingly, contours are fitted to objects already present in the environment, without requiring any presetting. In this paper, we describe a practical experience conducted in the warehouse of a beer production factory in Barcelona. A database containing the laser readings, image sequences and robot odometry along several trajectories was compiled, and subsequently processed off-line in order to assess the accuracies of both techniques under a variety of circumstances. In all, vision-based estimation turned out to be about one order of magnitude less precise than laser-based positioning, which qualifies the vision-based technique as a promising alternative to accomplish robot transfers across long distances, such as those needed in a warehouse, while backing up on laser-based positioning when accurate docking for loading and unloading operations is needed.

This paper shows how to adapt the set inversion via interval analysis algorithm (SIVIA) to the problem of mobile robot start-up positioning with a goniometric sensor. This approach is an alternative way to Markov, Monte Carlo and neural networks techniques, among others, that deal with that problem. SIVIA brings a global solution and confines the desired result into a union of intervals.

A method for robot navigation is presented. It is based on the detection of the main local reflection symmetry axes in images where straight lines are prevailing such as structured environments where robots are moving. The objective is to maintain the right orientation during navigation and to detect and correct any change in this orientation comparing continuously the symmetry axes between images. The algorithm measures the positions and the lengths of these local symmetry axes and their relative importance by means of an accumulation map of each contributing segment. A definition of symmetry between two segments that allows us to calculate a local symmetry axis between them is also given.