Step Detection Robust against the Dynamics of Smartphones

Lee, Hwan-hee; Choi, Suji; Lee, Myeong-jin

doi:10.3390/s151027230

Cited by 57 publications

(40 citation statements)

References 36 publications

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“…The threshold method may fail to be effective when used with mobile devices in case of hand-held applications. In such applications, the unconstrained motion of the mobile device cause the data range to change abruptly, affecting the utility of the thresholds [19,20]. However, in our study, the VR headset is worn in the head, hence ensures a good frame of reference for sensors on the smart phone.…”

Section: Threshold Settingmentioning

confidence: 96%

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

Sari

Küçükyılmaz

2019

Mobile Web and Intelligent Information Systems

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With recent advances in mobile and wearable technologies, virtual reality (VR) found many applications in daily use. Today, a mobile device can be converted into a low-cost immersive VR kit thanks to the availability of do-it-yourself viewers in the shape of simple cardboards and compatible software for 3D rendering. These applications involve interacting with stationary scenes or moving in between spaces within a VR environment. VR locomotion can be enabled through a variety of methods, such as head movement tracking, joystick-triggered motion and through mapping natural movements to translate to virtual locomotion. In this study, we implemented a walk-in-place (WIP) locomotion method for a VR-enabled exercise application. We investigate the utility of WIP for exercise purposes, and compare it with joystick-based locomotion in terms of step performance and subjective qualities of the activity, such as enjoyment, encouragement for exercise and ease of use. Our technique uses vertical accelerometer data to estimate steps taken during walking or running, and locomotes the user's avatar accordingly in virtual space. We evaluated our technique in a controlled experimental study with 12 people. Results indicate that the way users control the simulated locomotion affects how they interact with the VR simulation, and influence the subjective sense of immersion and the perceived quality of the interaction. In particular, WIP encourages users to move further, and creates a more enjoyable and interesting experience in comparison to joystick-based navigation.

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Section: Threshold Settingmentioning

confidence: 96%

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

Sari

Küçükyılmaz

2019

Mobile Web and Intelligent Information Systems

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“…Besides, we want to emphasize that we are interested on an individual labeling of each step in the signal. Most articles in the bibliography evaluate the performance of their algorithms taking only into account the total number of steps detected per experiment or the total distance walked, instead of a detailed prediction about when the person is really walking and when is not [61][62][63]. We want to evaluate classifiers that distinguish whether each segment of signal corresponds to an user walking or not.…”

Section: Ground Truthmentioning

confidence: 99%

Walking Recognition in Mobile Devices

Casado

Rodríguez²,

Iglesias

et al. 2020

Sensors

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Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.

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“…Time interval constraint [12]. The constraint of time interval can also improve the accuracy of the step detection.…”

Section: Conventional Schemementioning

confidence: 99%

“…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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Step Detection Robust against the Dynamics of Smartphones

Cited by 57 publications

References 36 publications

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

Walking Recognition in Mobile Devices

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

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