Adaptive step length estimation algorithm using low-cost MEMS inertial sensors

In this paper we introduce a MENIS based pedestrian navigation system (PNS) which consists of the low cost MENIS inertial sensor. An adaptive step length estimation algorithm using the awareness of the walk or run status is presented. Future u-Health monitoring systems will toe essential equipment for mobile users under the ubiquitous computing environment. It is well known that the energy expenditure in human walk or run changes with the speed of movement. Also the accurate walking distance is an important factor in calculating energy expenditure in human daily life. In order to compute the walking distance precisely, the number of occurred steps has to be counted exactly and the step length should be exactly estimated as well. However the step length varies considerably with the movement's speed and status. Therefore, we recognize the movement status such as walk or run of a pedestrian using the small-sized MENIS inertial sensors. Based on the result, a step length is estimated adaptively. The developed method can be applied to PNS and health monitoring mobile system.