Diego Álvarez

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%.

Online motion planning using Laplace potential fields

Robot Navigation is an especially challenging problem when only online sensor information is available. The main problem is to guarantee global properties, such as algorithm convergence or trajectory optimality, based on local information. In this paper we present a new non-heuristic sensor-based planning algorithm, characterized by: 1) it is based in potential functions, allowing to introduce optimality criteria, 2) it is computed incrementally to introduce last sensor readings, and 3) it accounts for robot dynamics. The result is a method suitable for real-time navigation, it is intuitive and easy to understand, and produces smooth and safe trajectories.

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.

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.

Ambulatory human upper limb joint motion monitoring

In order to make an ergonomic analysis of laborer working conditions, we need to measure the different joint angles along the daily work. These angles will be used to define the requirements of each workstation. This information, together with the medical examination of each worker, is then used to determine whether a worker can develop a task, or if the task may have caused an occupational disease. Usual human motion capture systems are designed to work in laboratory controlled conditions. This paper presents a method of angular joint measurement, combining inertial sensors (accelerometers and gyroscopes) and magnetic sensors, which allows the ambulatory estimation of the 7 degrees of freedom of the upper limb, for a long time, without problems due to time integration of the signal.

Slope Estimation during Normal Walking Using a Shank-Mounted Inertial Sensor

In this paper we propose an approach for the estimation of the slope of the walking surface during normal walking using a body-worn sensor composed of a biaxial accelerometer and a uniaxial gyroscope attached to the shank. It builds upon a state of the art technique that was successfully used to estimate the walking velocity from walking stride data, but did not work when used to estimate the slope of the walking surface. As claimed by the authors, the reason was that it did not take into account the actual inclination of the shank of the stance leg at the beginning of the stride (mid stance). In this paper, inspired by the biomechanical characteristics of human walking, we propose to solve this issue by using the accelerometer as a tilt sensor, assuming that at mid stance it is only measuring the gravity acceleration. Results from a set of experiments involving several users walking at different inclinations on a treadmill confirm the feasibility of our approach. A statistical analysis of slope estimations shows in first instance that the technique is capable of distinguishing the different slopes of the walking surface for every subject. It reports a global RMS error (per-unit difference between actual and estimated inclination of the walking surface for each stride identified in the experiments) of 0.05 and this can be reduced to 0.03 with subject-specific calibration and post processing procedures by means of averaging techniques.