scholarly journals Daily Living Movement Recognition for Pedestrian Dead Reckoning Applications

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alessio Martinelli ◽  
Simone Morosi ◽  
Enrico Del Re
Keyword(s):  
Supervised Learning ◽  
Activity Recognition ◽  
Sport Activity ◽  
Dead Reckoning ◽  
Support Vector ◽  
Navigation Systems ◽  
Cyclic Movements ◽  
Movement Recognition ◽  
Definition Of ◽  
Pedestrian Dead Reckoning

Nowadays, activity recognition is a central topic in numerous applications such as patient and sport activity monitoring, surveillance, and navigation. By focusing on the latter, in particular Pedestrian Dead Reckoning navigation systems, activity recognition is generally exploited to get landmarks on the map of the buildings in order to permit the calibration of the navigation routines. The present work aims to provide a contribution to the definition of a more effective movement recognition for Pedestrian Dead Reckoning applications. The signal acquired by a belt-mounted triaxial accelerometer is considered as the input to the movement segmentation procedure which exploits Continuous Wavelet Transform to detect and segment cyclic movements such as walking. Furthermore, the segmented movements are provided to a supervised learning classifier in order to distinguish between activities such as walking and walking downstairs and upstairs. In particular, four supervised learning classification families are tested: decision tree, Support Vector Machine,k-nearest neighbour, and Ensemble Learner. Finally, the accuracy of the considered classification models is evaluated and the relative confusion matrices are presented.

scholarly journals S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 148-176
Author(s):  
Maan Khedr ◽  
Naser El-Sheimy
Keyword(s):  
Smart Cities ◽  
Dead Reckoning ◽  
Human Motion ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Short Term ◽  
Navigation Solution ◽  
Correction Technique ◽  
Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

scholarly journals Audio Guide for Visually Impaired People Based on Combination of Stereo Vision and Musical Tones

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 151 ◽  
Author(s):  
Walter C. S. S. Simões ◽  
Yuri M. L. R. Silva ◽  
José Luiz de S. Pio ◽  
Nasser Jazdi ◽  
Vicente F. de Lucena
Keyword(s):  
Stereo Vision ◽  
Dead Reckoning ◽  
Ground Level ◽  
Indoor Navigation ◽  
Linear Filter ◽  
Navigation Systems ◽  
Delivery Time ◽  
Visually Impaired People ◽  
Margin Of Error ◽  
Pedestrian Dead Reckoning

Indoor navigation systems offer many application possibilities for people who need information about the scenery and the possible fixed and mobile obstacles placed along the paths. In these systems, the main factors considered for their construction and evaluation are the level of accuracy and the delivery time of the information. However, it is necessary to notice obstacles placed above the user’s waistline to avoid accidents and collisions. In this paper, different methodologies are associated to define a hybrid navigation model called iterative pedestrian dead reckoning (i-PDR). i-PDR combines the PDR algorithm with a Kalman linear filter to correct the location, reducing the system’s margin of error iteratively. Obstacle perception was addressed through the use of stereo vision combined with a musical sounding scheme and spoken instructions that covered an angle of 120 degrees in front of the user. The results obtained in the margin of error and the maximum processing time are 0.70 m and 0.09 s, respectively, with obstacles at ground level and suspended with an accuracy equivalent to 90%.

scholarly journals PEDESTRIAN DEAD RECKONING USING SMARTPHONES SENSORS: AN EFFICIENT INDOOR POSITIONING SYSTEM IN COMPLEX BUILDINGS OF SMART CITIES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 905-912
Author(s):  
E. Saadatzadeh ◽  
A. Chehreghan ◽  
R. Ali Abbaspour
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Dead Reckoning ◽  
Principal Component ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Abstract. This paper proposes an indoor positioning method using Pedestrian Dead Reckoning (PDR) based on the detection of the mode of the user’s smartphone. In the first step, to determine the mode of carrying the smartphone (Holding, Calling, Swinging) by suitably formed feature vectors based on sensor data, three classification algorithms (Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)) are evaluated. From the classification algorithm perspective, the decision tree algorithm had the best performance in terms of processing time and classification. Secondly, to determine the user position, the step detection is performed by defining the upper threshold and time threshold for Acceleration norm values. The orientation component is obtained by combining accelerometer, magnetometer, and gyroscope data using Complementary Filtering and Principal Component Analysis based on Global Acceleration (PCA-GA) methods. The mean standard deviation along the direct path for the three modes of carrying (Holding, Calling, and Swinging) were obtained 6.22, 6.82, and 14.68 degrees, respectively. Localization experiments were performed on 3 modes of carrying a smartphone in a rectangular geometry path. The mean final error of positioning from ordinary walking for the three modes of holding (Calling, Holding, Swinging) were obtained 2.11, 2.34, and 4.5 m, respectively.

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning

2011 ◽  
Vol 64 (2) ◽  
pp. 265-280 ◽  
Author(s):  
Wei Chen ◽  
Ruizhi Chen ◽  
Xiang Chen ◽  
Xu Zhang ◽  
Yuwei Chen ◽  
...  
Keyword(s):  
Low Cost ◽  
Estimation Method ◽  
Dead Reckoning ◽  
Field Tests ◽  
Human Locomotion ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Navigation Systems ◽  
Emg Signal ◽  
Pedestrian Dead Reckoning

In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.

scholarly journals An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

2021 ◽  
Vol 13 (11) ◽  
pp. 2137
Author(s):  
Bang Wu ◽  
Chengqi Ma ◽  
Stefan Poslad ◽  
David R. Selviah
Keyword(s):  
Human Activity ◽  
Trajectory Optimization ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Support Vector ◽  
Positioning System ◽  
Positioning Error ◽  
Door Opening ◽  
Error Accumulation ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.

scholarly journals Indoor positioning based on foot-mounted IMU

2015 ◽  
Vol 63 (3) ◽  
pp. 629-634 ◽  
Author(s):  
H. Guo ◽  
M. Uradzinski ◽  
H. Yin ◽  
M. Yu
Keyword(s):  
Sensor Fusion ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Navigation Systems ◽  
Initial Alignment ◽  
Step Detection ◽  
Accumulated Error ◽  
Pedestrian Dead Reckoning ◽  
Average Position

Abstract The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning by using a pedestrian dead reckoning (PDR) method. This project is focused on estimating the position using step detection technique based on foot-mounted IMU. The approach is sensor-fusion by using accelerometers, gyroscopes and magnetometers after initial alignment is completed. By estimating and compensating the drift errors in each step, the proposed method can reduce errors during the footsteps. There is an advantage of the step detection combined with ZUPT and ZARU for calculating the actual position, distance travelled and estimating the IMU sensors’ inherent accumulated error by EKF. Based on the above discussion, all algorithms are derived in detail in the paper. Several tests with an Xsens IMU device have been performed in order to evaluate the performance of the proposed method. The final results show that the dead reckoning positioning average position error did not exceed 0.88 m (0.2% to 1.73% of the total traveled distance – normally ranges from 0.3% to 10%), what is very promising for future handheld indoor navigation systems that can be used in large office buildings, malls, museums, hospitals, etc.

scholarly journals A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4447 ◽  
Author(s):  
Zhuangsheng Zhu ◽  
Shibo Wang
Keyword(s):  
Inertial Navigation ◽  
Dead Reckoning ◽  
Step Length ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
Gait Characteristics ◽  
Motion State ◽  
Pedestrian Dead Reckoning ◽  
Indoor And Outdoor

Pedestrian Dead Reckoning (PDR)-based pedestrian navigation technology is an important part of indoor and outdoor seamless positioning services. To improve the performance of PDR, we have conducted research on a step length estimator. Firstly, based on the basic theory of inertial navigation, we analyze in detail the errors in traditional Strapdown Inertial Navigation Systems (SINSs) caused by the unique motion state of pedestrians. Then, according to the fact that the inertial data from the foot can directly reflect the gait characteristics, we conduct a step length estimator that does not rely on SINS. The experimental results show that accuracy of the proposed method is between 0.6% and 1.4% with a standard deviation of 0.25%.

scholarly journals A Method for the Definition of Immunological Non-response to Antiretroviral Therapy Based on Review Analysis and Supervised Classification Model

2021 ◽  
Author(s):  
Yong Shuai
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
Absolute Number ◽  
Learning Model ◽  
Classification Model ◽  
Support Vector ◽  
Infected People ◽  
Machine Learning Classification ◽  
Visualization Technology ◽  
Definition Of

Abstract BackgroundImmunological non-response (INR) accelerated the progression of AIDS disease and brought serious difficulties to the treatment of HIV-1 infected people. The current definition of INR lacked a credible consensus, which affected the diagnosis, treatment and scientific research of INR. MethodWe systematically analyzed the open source INR related references, used visualization techniques and machine learning classification models to propose the features, models and criteria that define INR. ResultWe summarized some consensus on the definition of INR. Among the features that defined INR, CD4+ T-cell absolute number and ART time were the best feature to define INR . The supervised learning classification model had high accuracy in defining INR, and the support vector machine (SVM) had the highest accuracy in the commonly used supervised classification learning model. Based on supervised learning model and visualization technology, we proposed some criteria that could help to reach a consensus on INR definition. ConclusionsThis study provided consensus, features, model and criteria for defining INR.

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