2018
DOI: 10.3390/s18061811
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Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Abstract: This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be hori… Show more

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Cited by 76 publications
(68 citation statements)
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“…Kang and Han [22] designed a smartphone-based algorithm, named smartPDR, but users had to reduce the sway of the smartphone in experiments. Wang et al [23] presented a PDR approach based on motion mode recognition using a smartphone. The motion mode recognition was achieved using a support vector machine (SVM) and a decision tree (DT), which thereby increased the complexity of the algorithm.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Kang and Han [22] designed a smartphone-based algorithm, named smartPDR, but users had to reduce the sway of the smartphone in experiments. Wang et al [23] presented a PDR approach based on motion mode recognition using a smartphone. The motion mode recognition was achieved using a support vector machine (SVM) and a decision tree (DT), which thereby increased the complexity of the algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…The usage mode could be determined based on the gravity-assisted (GA) method. However, in References [22][23][24], step detection methods were both based on peak detection, which would introduce detection errors due to the step detection errors. Moreover, the step length estimation method was based on a nonlinear model or constant which would cause position errors for different users.…”
Section: Introductionmentioning
confidence: 99%
“…However, PDR results estimated from inertial measurement unit (IMU) data have errors that are accumulated over time. Thus, many methods have been proposed for correcting PDR positioning errors, such as combining various sensors and wireless devices for error correction [2,3] and conducting algorithmic advancements for obtaining heading direction and step length estimations [9][10][11][12][13][14][15][16]. This study optimised the PDR algorithm by using the difference magnetic fingerprint between real-time measurement and magnetic fingerprint map data to calculate the weight then put in a particle filter method (in this study call modified particle filter) to get the position of user.…”
Section: Introductionmentioning
confidence: 99%
“…This is compounded by the risks of disclosing plaintext measurements, say, after the exploitation of a vulnerable service, such as insecure AWS S3 buckets [7]. A large corpus of work has already demonstrated identifying users based on their gait from accelerometer and other inertial measurement unit (IMU) data [18,19,22,29,44]; their position using dead reckoning [23,37,46]; and determining activities of daily living (ADLs), like whether the user is walking, sleeping, and sitting [21,41]. Existing work on privacy-enhancing fall detection has focussed on non-cryptographic techniques for video-based methods, including image blurring, silhouetting and foreground extraction.…”
Section: Introductionmentioning
confidence: 99%