Walk detection and step counting on unconstrained smartphones

Brajdic, Agata; Harle, Robert

doi:10.1145/2493432.2493449

Cited by 330 publications

(252 citation statements)

References 47 publications

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“…User heading is estimated step by step. The walking steps are detected by a classic peak detection algorithm [27][28][29], which recognizes the peak acceleration caused by the unique heel strike during each walking step. We firstly project all acceleration within a walking step measured at DCS into GCS.…”

Section: Heading Estimation Strategy Selectionmentioning

confidence: 99%

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

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Section: Heading Estimation Strategy Selectionmentioning

confidence: 99%

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…Essay [3,4,5] put forward a kind of step counting algorithm based on autocorrelation analysis, reducing influences of positions of phones and movement postures.The weak point is that the algorithm is relatively complex and sampling frequency is too low. Essay [6,7,8,9] put forward a kind of step counting algorithm based on calibration factors and delayed feedback, reducing influences of multi-peak values, and its robustness is relatively ideal.The weak point is that accuracy reduces when walking slowly. Essay [10] puts forward a kind of step counting algorithm based on amplitudes of SVM, achieving low power consumption and high accuracy.The weak point is that we need to adjust amplitude threshold due to users' practical movement characteristics, therefore inconvenient in actual use.…”

Section: Analysis Of Current Technologymentioning

confidence: 99%

Walking Security Alarm System for Mobile Phone Addicts

Gü¹,

Chen²,

Xiao-yong³

2018

Proceedings of the 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017)

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Abstract. To handle walking safety problems for mobile phone addicts, a walking security alarm system based on android mobile platform is designed. According to movement features when someone is walking and when he falls down, this paper proposes a step counting algorithm and a fall-down detection algorithm based on SVM judging respectively. In addition, a graded alarm technology is designed, so that a complete security alarm and calling-for-help system is implemented. Test results indicate that the accuracy of core algorithms have reached over 96% in average, and functions of each module have reached the design objects.

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“…Conventional scheme takes identifying peaks as the core idea [10,11,12]. And use a few constraints to eliminate multi-peaks.…”

Section: Conventional Schemementioning

confidence: 99%

Design of Counting-Step Algorithm for Smartphone-Based Indoor Localization

Song¹,

Liu²,

Huang³

et al. 2017

dtcse

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Abstract. In recent years, the smartphones which are integrated with many high-accuracy sensors have become very popular. Utilizing the development of sensors, developers have done many useful researchers. Indoor positioning navigation is an interesting and hot field of them.Step detecting and counting are key technologies in indoor positioning. In this paper, we propose a novel method to count steps which is an improvement of the classical steps detection method, the peak detection method of the acceleration. Our proposed scheme consists of two parts: the scoring part and adaptive window length part. Two adaptive window length algorithms which can adapt to the varying velocity are also proposed in this paper. In our experiments, a smartphone -Nubia NX513J is handled, with screen facing upwards to record the accelerometer data. The scoring algorithm of acceleration in the process of step detection is tested in two kinds of path: a straight path and a U-shaped path in which it shows better results than conventional peak detection method.

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Walk detection and step counting on unconstrained smartphones

Cited by 330 publications

References 47 publications

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Walking Security Alarm System for Mobile Phone Addicts

Design of Counting-Step Algorithm for Smartphone-Based Indoor Localization

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