In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.

Laser sensors can be used to measure distances to objects and their related parameters (displacements, position, surface profiles and velocities). Laser sensors are based on many different optical techniques, such as triangulation, time-of-flight, confocal and interferometric sensors. As laser sensor technology has improved, the size and cost of sensors have decreased, which has led to the widespread use of laser sensors in many areas. In addition to traditional manufacturing industry applications, laser sensors are increasingly used in robotics, surveillance, autonomous driving and biomedical areas. This paper outlines some of the recent efforts made towards laser sensors for displacement, distance and position.

Two feet motion is estimated for gait analysis. An inertial sensor is attached on each shoe and an inertial navigation algorithm is used to estimate the movement of both feet. To correct inter-shoe position error, a camera is installed on the right shoe and infrared LEDs are installed on the left shoe. The proposed system gives key gait analysis parameters such as step length, stride length, foot angle and walking speed. Also it gives three dimensional trajectories of two feet for gait analysis.

This paper is concerned with improving performance of a state estimationproblem over a network in which a send-on-delta (SOD) transmission method is used. TheSOD method requires that a sensor node transmit data to the estimator node only if itsmeasurement value changes more than a given specified δ value. This method has beenexplored and applied by researchers because of its efficiency in the network bandwidthimprovement. However, when this method is used, it is not ensured that the estimator nodereceives data from the sensor nodes regularly at every estimation period. Therefore, wepropose a method to reduce estimation error in case of no sensor data reception. When theestimator node does not receive data from the sensor node, the sensor value is known to bein a (−δ_i , δ_i ) interval from the last transmitted sensor value. This implicit information hasbeen used to improve estimation performance in previous studies. The main contribution ofthis paper is to propose an algorithm, where the sensor value interval is reduced to(−δ_i / 2, δ_i / 2) in certain situations. Thus, the proposed algorithm improves the overallestimation performance without any changes in the send-on-delta algorithms of the sensornodes. Through numerical simulations, we demonstrate the feasibility and the usefulness ofthe proposed method.