Rafael C. González

Real-time gait event detection for normal subjects from lower trunk accelerations

In this paper we report on a novel algorithm for the real-time detection and timing of initial (IC) and final contact (FC) gait events. We process the vertical and antero-posterior accelerations registered at the lower trunk (L3 vertebra). The algorithm is based on a set of heuristic rules extracted from a set of 1719 steps. An independent experiment was conducted to compare the results of our algorithms with those obtained from a Digimax force platform. The results show small deviations from times of occurrence of events recorded from the platform (13+/-35 ms for IC and 9+/-54 ms for FC). Results for the FC timing are especially relevant in this field, as no previous work has addressed its temporal location through the processing of lower trunk accelerations. The delay in the real-time detection of the IC is 117+/-39 ms and 34+/-72 ms for the FC, improving previously reported results for real-time detection of events from lower trunk accelerations.

Comparison of Step Length Estimators from Weareable Accelerometer Devices

Wearable accelerometry provides easily portable systems that supply real-time data adequate for gait analysis. When they do not provide direct measurement of a spatio-temporal parameter of interest, such as step length, it has to be estimated with a mathematical model from indirect sensor measurements. In this work we are concerned with the accelerometry-based estimation of the step length in straight line human walking. We compare five step length estimators. Measurements were taken from a group of four adult men, adding up a total of 800 m per individual of walking data. Also modifications to these estimators are proposed, based on biomechanical considerations. Results show that this modifications lead to improvements of interest over previous methods

Multisensor Approach to Walking Distance Estimation with Foot Inertial Sensing

Walking distance estimation is an important issue in areas such as gait analysis, sport training or pedestrian localization. A natural location for portable inertial sensors for gait monitoring is to attach them to the user shoes. Step length can be computed by means of a biaxial accelerometer and a gyroscope on the sagital plane. But estimations based on the direct signal integration are prone to error. This paper shows the results achieved by using a multisensor model approach to reduce uncertainty. Unbounded growth of error is reduced by means of sensor fusion techniques. The method has been tested, and early experimental results show that it provides an estimation of the walking distance with a standard deviation smaller than with single IMU similar systems.

Pedestrian navigation based on a waist-worn inertial sensor.

We present a waist-worn personal navigation system based on inertial measurement units. The device makes use of the human bipedal pattern to reduce position errors. We describe improved algorithms, based on detailed description of the heel strike biomechanics and its translation to accelerations of the body waist to estimate the periods of zero velocity, the step length, and the heading estimation. The experimental results show that we are able to support pedestrian navigation with the high-resolution positioning required for most applications.

Modified Pendulum Model for Mean Step Length Estimation

Step length estimation is an important issue in areas such as gait analysis, sport training or pedestrian localization. It has been shown that the mean step length can be computed by means of a triaxial accelerometer placed near the center of gravity of the human body. Estimations based on the inverted pendulum model are prone to underestimate the step length, and must be corrected by calibration. In this paper we present a modified pendulum model in which all the parameters correspond to anthropometric data of the individual. The method has been tested with a set of volunteers, both males and females. Experimental results show that this method provides an unbiased estimation of the actual displacement with a standard deviation lower than 2.1%.

Vision Based Measurement System to Quantify Straightness Defect in Steel Sheets

A non-uniform distribution of rolling pressure during steel lamination may produce flatness asymmetries in the steel sheets, causing a certain curvature on its edges. This deformation may cause stoppages in the rolling process, and damages in the machinery. A computer vision system for measuring this straightness defect is presented. This system shows the adaptation of well-known computer vision techniques to fit precision and real-time constraints. Some problems that arise during the implementation phase are also described, and the correspondent solutions outlined.

Sensor management for local obstacle detection in mobile robots

The majority of analog-to-digital converters (ADCs) are designed without taking into consideration the distribution of input signal. In this correspondence, we present a novel ADC architecture that is optimized for a given input signals statistics. The new robust data-optimized stochastic flash (RDSF) ADC achieves robustness and high accuracy by employing a) a large number of 1-bit quantizers operating in parallel with an additive noise and b) a novel probability density transform (PDT). We demonstrate the performance gain of the RDSF over the conventional flash ADC using simulations and theoretical analysisIn experimental robotics it is common to complement the motion planning algorithm with a local obstacle avoidance module. We are concerned here with the specifications of a range sensor specifically design for the task. Such sensor has special requirements: it has to guarantee detection and response, and it has to guarantee throughput, providing information ail a rate in proportion to robot velocity. An analysis is presented which allows the selection of the sensor requirements for collision avoidance tasks in mobile robots. The design takes into account robot dynamics, it is compatible with fast motion-only the indispensable environment zones are explored-and avoids unnecessary velocity reductions. Experiments show the possibilities and limitations of the approach.

Ambulatory estimation of mean step length during unconstrained walking by means of COG accelerometry.

It has been reported that spatio-temporal gait parameters can be estimated using an accelerometer to calculate the vertical displacement of the bodys centre of gravity. This method has the potential to produce realistic ambulatory estimations of those parameters during unconstrained walking. In this work, we want to evaluate the crude estimations of mean step length so obtained, for their possible application in the construction of an ambulatory walking distance measurement device. Two methods have been tested with a set of volunteers in 20 m excursions. Experimental results show that estimations of walking distance can be obtained with sufficient accuracy and precision for most practical applications (errors of 3.66 ± 6.24 and 0.96 ± 5.55%), the main difficulty being inter-individual variability (biggest deviations of 19.70 and 15.09% for each estimator). Also, the results indicate that an inverted pendulum model for the displacement during the single stance phase, and a constant displacement per step during double stance, constitute a valid model for the travelled distance with no need of further adjustments. It allows us to explain the main part of the erroneous distance estimations in different subjects as caused by fundamental limitations of the simple inverted pendulum approach.

Fast stereo vision algorithm for robotic applications

Autonomous navigation applications demand sensors with a low sample time to be able to increase speed. We have developed a stereo vision algorithm, capable to deliver dense disparity maps for single, high-resolution scanlines at high speed (40 ms/line), even for wide disparity ranges. We have tested the algorithm with synthetic and real images. Our algorithm is based on a dynamic programming schema with a cost function based on a weighted sum of squared intensify errors. Weight factors are based on gradient values. The algorithm includes explicitly detection of occlusion. Occlusion cost changes dynamically depending on gradient values of matched points.

Pedestrian dead reckoning with waist-worn inertial sensors

We present a waist-worn personal navigation system based on inertial measurement units. The device makes use of the human bipedal pattern to reduce position error. We describe improved algorithms, based on detailed description of the heel strike biomechanics and its translation to accelerations of the body waits, to estimate the periods of zero velocity, the step length, and the heading estimation. The experimental results show that we are able to support pedestrian navigation with the high-resolution positioning required for most applications.