Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).

No abstract

With the booming development of green lighting technology, visible light-based indoor localization has attracted a lot of attention. Visible light-based indoor positioning technology leverages a light propagation model to pinpoint target location. Compared with the radio localization technology, visible light-based indoor positioning not only can achieve higher location accuracy, but also no electromagnetic interference. In this article, we propose LIPOS, a three-dimensional indoor positioning system based on attitude identification and visible light propagation model. The LIPOS system takes advantage of the existing lighting infrastructures to localize mobile devices that have light-sensing capabilities (e.g. a smartphone) using light emitting diode lamps as anchors. The system can accurately identify the attitude of a smartphone using its integrated sensors, distinguish different light emitting diode beacons using the fast Fourier transform algorithm, construct a position cost-function based on a visible light radiative decay model, and apply a nonlinear optimizing method to acquire the optimal estimation of final location. We have implemented the LIPOS system and evaluated it with a small-scale hardware testbed, as well as moderate-sized simulations. Extensive experiments are performed in three representative indoor environments-open-plan office, cubicle, and corridor, which not only demonstrate that the LIPOS can effectively avoid the negative effects of dynamic change of a smartphone's attitude angle, but also show better locating accuracy and robustness, and obtain sub-meter level positioning accuracy.

This paper presents a bipedal locomotion model for passive dynamic walking with flat feet and compliant ankles. The two-dimensional seven-link model extends the simplest walking model with the addition of hip actuation, knee joints, flat feet and torsional springs based compliance on ankle joints, concerning heel-strike and toe-strike transitions, to achieve adaptive bipedal locomotion on level ground with controllable walking speed. We investigate the effects of foot geometric parameters and ankles stiffness on bipedal walking. The model achieves satisfactory walking results not only on even ground but also on uneven terrain with no active control and on different walking velocities. In addition, from the view of stability, there is an optimal footankle ratio of the passivity-based walker. The results can be used to explore further understanding of bipedal walking, and help the design of future intelligent ankle-foot prosthesis and passivity-based robot prototypes towards more practical uses.