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Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

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.

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.

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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. Keywords: indoor positioning, deep learning, pedestrian dead reckoning, walking distance, stride-length estimation 1. Introduction Accurate and pervasive indoor positioning significantly improves our daily life [ 1 ].

The demand for accurate and practical location-based services anywhere using portable devices, such as smartphones, is quickly increasing in various applications, including asset and personnel tracking, health monitoring, precision advertising, and location-specific push notifications. To meet this explosive demand, various indoor positioning approaches have recently been developed, including RFID [ 3 ], Wi-Fi [ 4 , 5 ], UWB [ 6 ], BLE [ 7 ], magnetic [ 1 , 8 , 9 , 10 ], visible light [ 11 , 12 ] and visual methods [ 13 ].

Indoor localization techniques are classified into propagation model-based, fingerprint, and dead reckoning methods. Positioning performance of propagation model-based methods depends on the deployment density of the reference points.

However, these methods are ineffective when the radio signal is weak or not available in many scenarios, such as underground parking lots.

The accuracy of fingerprint-based approaches is affected by device orientation, pedestrians, and layout changes of indoor environments e. It is important that fingerprint- or infrastructure-based positioning techniques are not available for emergency scenarios, such as anti-terrorism action, emergency rescues and exploration missions. Abstract We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone.

First, we apply a fast Fourier transform FFT -based smoother on the acceleration data collected by the smartphone to remove the interference signals.

Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length.

To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels.

The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

Introduction Pedestrian dead-reckoning PDR is extensively studied as an effective approach to obtain pedestrian locations by estimating the distance traveled via handheld inertial sensors [ 1 ]. This method can be developed at a low cost for use in conjunction with new services. With the rapid development of microelectromechanical systems MEMS in recent years, the demand for pedestrian positioning has been considerably increasing in fields such as navigation systems [ 2 ], augmented reality [ 3 ], gait analysis [ 4 ], and health monitoring [ 5 ].

Therefore, it is necessary to accurately detect human walking distances for the determination of pedestrian locations.

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Recently, two main types of algorithms for distance estimation have been described. The first type is based on the successive double-integral-based length-step measurement of acceleration.

The major drawback of this technique is the error accumulated over the duration of the experiments. This problem can be partially addressed using zero velocity updates ZUPT.

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In the second type, researchers applied a verifiable relationship between vertical acceleration and the step length to estimate the distance traveled by a moving subject. Bylemans et al. Another empirical method was based on the correlation of vertical acceleration at the foot of the subject [ 7 , 8 , 9 ] with the length of the step.On the counter lay some sliced cucumber, some pieces of dried black bread, and some fish, chopped up small, all smelling very bad.

Inertial Measurement Unit IMU, consisting of gyroscopes and accelerometers -based navigation algorithms overcome the limitations of radio-frequency signal fluctuations and blockage since they provide real-time locations of a pedestrian or object given an initial position, as well as not relying on any additional infrastructure or pre-collected database.

1. Introduction

We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. Download episode skype. They are included to give you an idea of the product offerings.

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