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

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.

Usual human motion capture systems are designed to work in controlled laboratory conditions. For occupational health, instruments that can measure during normal daily life are essential, as the evaluation of the workers' movements is a key factor to reduce employee injury- and illness-related costs. In this paper, we present a method for joint angle measurement, combining inertial sensors (accelerometers and gyroscopes) and magnetic sensors. This method estimates wrist flexion, wrist lateral deviation, elbow flexion, elbow pronation, shoulder flexion, shoulder abduction and shoulder internal rotation. The algorithms avoid numerical integration of the signals, which allows for long-time estimations without angle estimation drift. The system has been tested both under laboratory and field conditions. Controlled laboratory tests show mean estimation errors between 0.06° and of 1.05°, and standard deviation between 2.18° and 9.20°. Field tests seem to confirm these results when no ferromagnetic materials are close to the measurement system.

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.

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%.