Adaptive 3D Position Estimation of Pedestrians by Wearing One Ankle Sensor

Zhao, Yuliang; Liang, Jiaqi; Cui, Yifeng; Sha, Xiaopeng; Li, Wen J.

doi:10.1109/jsen.2020.2995612

Cited by 18 publications

(7 citation statements)

References 42 publications

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“…And they added vertical variables to the adaptive multimodal stride in combination with it. Experimental outcomes demonstrated that it raised the applicability and precision of pedestrian horizontal position estimation [10]. Neethu P S's team applied a convolutional neural network classification method to human hand gesture detection and recognition, which extracted fingertip features in hand images by a connected component analysis algorithm.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

Jiang¹,

Zhang²

2023

IJACSA

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With the continuous improvement of technology, modern sports training is gradually developing towards precision and efficiency, which requires more accurate identification of athletes' sports stances. The study first establishes a classification structure of basketball stance, then designs a hardware module to collect different stance data by using inertial sensors, thus extracting multidimensional motion stance features. Then the traditional convolutional neural network (CNN) is improved by principal component analysis (PCA) to form the PCA+CNN algorithm. Finally, the algorithm is simulated and tested. The outcomes demonstrated that the average discrimination error rate of the improved PCA+CNN algorithm in the Human 3.6M dataset was 3.15%, which was a low error rate. In recognition of basketball sports pose, the wearable based on the improved algorithm had the highest accuracy of 99.4% and took the quietest time of 18s, which was better than the other three methods. It demonstrated that the method had high discrimination precision and recognition efficiency, which could provide a reliable technical means to improve the science of basketball sports training plan and training effect.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

Jiang¹,

Zhang²

2023

IJACSA

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“…In real life, people's activity trajectory includes not only two-dimensional horizontal walking, but also three-dimensional vertical motion. Pedestrians' gait when walking up and down stairs is more complex than that of horizontal walking [29,30], the inertial sensor will have more serious noise. In this paper, a barometer is added on the basis of the inertial sensor.…”

Section: Height Calculationmentioning

confidence: 99%

The Improved Constraint Methods for Foot-Mounted Pedestrian Three-Dimensional Inertial Navigation

Zhao

Guo

et al. 2021

Mathematical Problems in Engineering

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The foot-mounted pedestrian navigation system (PNS) that uses microelectromechanical systems (MEMS) inertial measurement units (IMUs) to track the person’s position. However errors accumulate over time during inertial navigation solutions, which affects the positioning precision. In this paper, a multicondition zero velocity detector is used to detect the stance phase of gait. Then the errors are corrected in the stance phase and the swing phase, respectively, through the Kalman filter. When pedestrians are going up and down the stairs, the divergence of height will reduce the accuracy of three-dimensional positioning. In this paper, an accelerometer and a barometer are used to obtain altitude variation, and after that the stair condition detection (SCD) algorithm is proposed to correct the height of Kalman filter output and detect the walking state of pedestrians. Through theoretical research and field experiments, these algorithms are studied carefully. The results of the experiment show that the algorithm proposed in this paper can effectively eliminate errors and achieve more accurate positioning.

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“…Stride-length estimation is an important part of the PDR system, and its error is the main source of the error in this system. Many previous studies used the acceleration-based nonlinear model [ 13 ] or the frequency-based linear model [ 14 , 15 ] to estimate pedestrian-stride length. The Weinberg model [ 16 , 17 ] is widely used, which constructs a nonlinear function based on the maximum and minimum accelerations to estimate stride length.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the accuracy of the model, the parameters were adjusted according to different walking patterns. Zhao et al [ 13 ] adopted different stride-length-estimation models for three scenarios: walking on a plane, moving up and down stairs, and the transition step. Their model’s parameters need to be tuned according to the physical characteristics of pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor

Zeng

Wang

et al. 2023

Micromachines

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Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult to adapt to changes in pedestrian walking speed, which leads to a rapid increase in the error of the PDR. In this paper, a new deep-learning model based on long short-term memory (LSTM) and Transformer, LT-StrideNet, is proposed to estimate pedestrian-stride length. Next, a shank-mounted PDR framework is built based on the proposed stride-length-estimation method. In the PDR framework, the detection of pedestrian stride is achieved by peak detection with a dynamic threshold. An extended Kalman filter (EKF) model is adopted to fuse the gyroscope, accelerometer, and magnetometer. The experimental results show that the proposed stride-length-estimation method can effectively adapt to changes in pedestrian walking speed, and our PDR framework has excellent positioning performance.

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Adaptive 3D Position Estimation of Pedestrians by Wearing One Ankle Sensor

Cited by 18 publications

References 42 publications

Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

The Improved Constraint Methods for Foot-Mounted Pedestrian Three-Dimensional Inertial Navigation

Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor

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