Recognizing a Mobile Phone’s Storing Position as a Context of a Device and a User

Fujinami, Kaori; Kouchi, Satoshi

doi:10.1007/978-3-642-40238-8_7

Cited by 19 publications

(25 citation statements)

References 15 publications

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“…Furthermore, we plan to perform evaluation experiments under more realistic situations, for example, a smartphone in a pants pocket or in a handbag. To achieve realistic tracking with a smartphone, we should incorporate smartphone-based sensing methods such as those for detecting the position of a smartphone on the body and estimating the heading of a smartphone [Kang et al 2012;Fujinami and Kouchi 2013].…”

Section: Discussionmentioning

confidence: 99%

Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning

Taniuchi

Maekawa

2015

ACM Trans. Embed. Comput. Syst.

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In this article, we propose a new method for automatically updating a Wi-Fi indoor positioning model on a cloud server by employing uploaded sensor data obtained from the smartphone sensors of a specific user who spends a lot of time in a given environment (e.g., a worker in the environment). In this work, we attempt to track the user with pedestrian dead reckoning techniques, and at the same time we obtain Wi-Fi scan data from a mobile device possessed by the user. With the scan data and the estimated coordinates uploaded to a cloud server, we can automatically create a pair consisting of a scan and its corresponding indoor coordinates during the user's daily life and update an indoor positioning model on the server by using the information. With this approach, we try to cope with the instability of Wi-Fi-based positioning methods caused by changing environmental dynamics, that is, layout changes and moving or removal of Wi-Fi access points. Therefore, ordinary users (e.g., customers) who do not have rich sensors can benefit from the continually updating positioning model. ACM Reference Format:Daisuke Taniuchi and Takuya Maekawa. 2015. Automatic update of indoor location fingerprints with pedestrian dead reckoning.

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Section: Discussionmentioning

confidence: 99%

Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning

Taniuchi

Maekawa

2015

ACM Trans. Embed. Comput. Syst.

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“…It is not difficult to foresee that the best performance comes from using a personalized classifier, in which a classifier is trained with the dataset of a particular person and tested with the dataset of the same person (e.g., [26]). Therefore, to see the performance under this best condition, we conducted 10-fold cross-validation using personalized classifiers.…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, a classifier built from the dataset from "trousers back pocket" is shared with the data obtained from "trousers front pocket". As shown in [26], front and back trousers pockets are often misrecognized for each other. Therefore, sharing the classifier between two positions can become robust against the mistake of the underlying storing position recognizer.…”

Section: Methodsmentioning

confidence: 99%

Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection

Tsuyoshi

Fujinami

2016

IJGI

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Abstract:Road anomalies, such as cracks, pits and puddles, have generally been identified by citizen reports made by e-mail or telephone; however, it is difficult for administrative entities to locate the anomaly for repair. An advanced smartphone-based solution that sends text and/or image reports with location information is not a long-lasting solution, because it depends on people's active reporting. In this article, we show an opportunistic sensing-based system that uses a smartphone for road anomaly detection without any active user involvement. To detect road anomalies, we focus on pedestrians' avoidance behaviors, which are characterized by changing azimuth patterns. Three typical avoidance behaviors are defined, and random forest is chosen as the classifier. Twenty-nine features are defined, in which features calculated by splitting a segment into the first half and the second half and considering the monotonicity of change were proven to be effective in recognition. Experiments were carried out under an ideal and controlled environment. Ten-fold cross-validation shows an average classification performance with an F-measure of 0.89 for six activities. The proposed recognition method was proven to be robust against the size of obstacles, and the dependency on the storing position of a smartphone can be handled by an appropriate classifier per storing position. Furthermore, an analysis implies that the classification of data from an "unknown" person can be improved by taking into account the compatibility of a classifier.

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“…The size and sliding-width (0.1 Hz) of subwindow were heuristically determined. A feature calculated as the sum of squared values of frequency components is sumPower F (a.k.a "FFT energy" in [9]) [2]. The FFT entropy (entropy F ) is then calculated as the normalized information entropy of FFT component values of acceleration signals, which represents the distribution of frequency components in the frequency domain [2].…”

Section: Recognition Featuresmentioning

confidence: 99%

“…F-measure for class i is defined by (8), which is averaged over 17 classes. The recall and precision for class i are represented by (9) and (10), where N correcti , N testedi , and N judgedi represent the number of cases correctly classified into class i , the number of test cases in class i , and the number of cases classified into class i , respectively, while i corresponds to either one of 17 classes.…”

Section: A Conditionmentioning

confidence: 99%

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

Fujinami¹,

Saeki²,

Li³

et al. 2017

ijacsa

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Abstract-Due to the dependency of our daily lives on smartphones, the states of the device have impact on the quality of services offered through a smartphone. In this article, we focus on the carrying states of the device while the user is walking, in which 17 states, e.g., in the front-left trouser pocket, calling phone in the right hand, in a backpack are subjects to recognition based on supervised learning with accelerometer-derived features. A large-scale data collection from 70 persons with three walking speeds allows reliable evaluation regarding suitable features and classifiers model, the feature selection method, robustness of localization against unknown person, and effect of walking speed in training a classifier. Person-independent evaluation shows that average F-measures of 17 class classification and merged 9 class classification were 0.823 and 0.913, respectively.

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Recognizing a Mobile Phone’s Storing Position as a Context of a Device and a User

Cited by 19 publications

References 15 publications

Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning

Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning

Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

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