Online pose classification and walking speed estimation using handheld devices

Park, Jungeun; Patel, Ami; Curtis, Dorothy; Teller, Seth; Ledlie, Jonathan

doi:10.1145/2370216.2370235

Cited by 95 publications

(71 citation statements)

References 17 publications

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“…Spectral analysis (STFT and CWT): This promising technique has attracted a large amount of attention recently due to its robustness [3], [15], [17], [18]. Algorithms of this category first convert the acceleration signal to frequency domain using different algorithms like Fourier transform [15], [17], short time Fourier transform (STFT) [3], or continuous wavelet transform (CWT) [18], and then the dominant peak of the signal in frequency domain is identified as the step frequency. Windowed acceleration signal (magnitude) is common choice for people working with spectral analysis [15].…”

Section: Discussionmentioning

confidence: 99%

“…Algorithms of this category first convert the acceleration signal to frequency domain using different algorithms like Fourier transform [15], [17], short time Fourier transform (STFT) [3], or continuous wavelet transform (CWT) [18], and then the dominant peak of the signal in frequency domain is identified as the step frequency. Windowed acceleration signal (magnitude) is common choice for people working with spectral analysis [15]. As a result, online step detection makes this algorithm very computationally intensive.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement

Xiao

Wen

Markham

et al. 2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Pedestrian dead reckoning, especially on smartphones, is likely to play an increasingly important role in indoor tracking and navigation, due to its low cost and ability to work without any additional infrastructure. A challenge however, is that positioning, both in terms of step detection and heading estimation, must be accurate and reliable, even when the use of the device is so varied in terms of placement (e.g. handheld or in a pocket) or orientation (e.g holding the device in either portrait or landscape mode). Furthermore, the placement can vary over time as a user performs different tasks, such as making a call or carrying the device in a bag. A second challenge is to be able to distinguish between a true step and other periodic motion such as swinging an arm or tapping when the placement and orientation of the device is unknown. If this is not done correctly, then the PDR system typically overestimates the number of steps taken, leading to a significant long term error. We present a fresh approach, robust PDR (R-PDR), based on exploiting how bipedal motion impacts acquired sensor waveforms. Rather than attempting to recognize different placements through sensor data, we instead simply determine whether the motion of one or both legs impact the measurements. In addition, we formulate a set of techniques to accurately estimate the device orientation, which allows us to very accurately (typically over 99%) reject false positives. We demonstrate that regardless of device placement, we are able to detect the number of steps taken with >99.4% accuracy. R-PDR thus addresses the two main limitations facing existing PDR techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement

Xiao

Wen

Markham

et al. 2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

show abstract

“… Time passed between peaks. That function could be the time taken involving the peaks in the sinusoidal dunes [10].  Binned distribution.…”

Section: B Frequency-domain Featuresmentioning

confidence: 99%

“…Park et al [10] unveiled a musical instrument construct classification process on the cornerstone of the regularized kernel algorithm. It offers a way of how precisely to determine the sma.…”

Section: Huynh Et Al [4] Mixed Numerous Eigenspaces With Supportmentioning

confidence: 99%

Review on Various Data Mining Approaches on Smart Phone

Singh¹

2016

IJECS

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Abstract-Smartphones are common and becoming more and heightened, with ever-growing computing, marketing, and feeling powers. It has been adjusting the landscape of people's everyday life and has subjected the options for several interesting data mining purposes, which range from wellness and health checking, particular biometric trademark, downtown computing, assistive design, and elder-care, to interior localization and navigation, etc. Individual task popularity is just a core making end behind these applications.

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“…Displacement (step length) and direction of motion are then estimated for individual steps. To this end, recent research relies on machine learning techniques and activity recognition has been extended from distinguishing not only between the user moving and standing still, but to further include estimating the walking speed, climbing on stairs, taking an elevator, and more [24,27]. Gusenbauer et al [9], for instance, use detected activities to constrain the position estimate to known locations of stairs and elevators.…”

Section: Sensor Modalities For Indoor Positioningmentioning

confidence: 99%

Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

Hilsenbeck

Bobkov

Schroth

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

121

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We propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices. We employ our fusion technique to combine relative motion information based on step detection with WiFi signal strength measurements. The method is based on the well-known particle filter methodology. In contrast to previous work, we provide a probabilistic model for location estimation that is formulated directly on a fully discretized, graph-based representation of the indoor environment. We generate this graph by adaptive quantization of the indoor space, removing irrelevant degrees of freedom from the estimation problem. We evaluate the proposed method in two realistic indoor environments using real data collected from smartphones. In total, our dataset spans about 20 kilometers in distance walked and includes 13 users and four different mobile device types. Our results demonstrate that the filter requires an order of magnitude less particles than state-of-theart approaches while maintaining an accuracy of a few meters. The proposed low-complexity solution not only enables indoor positioning on less powerful mobile devices, but also saves much-needed resources for location-based end-user applications which run on top of a localization service.

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Online pose classification and walking speed estimation using handheld devices

Cited by 95 publications

References 17 publications

Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement

Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement

Review on Various Data Mining Approaches on Smart Phone

Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

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