DeepWalking: Enabling Smartphone-Based Walking Speed Estimation Using Deep Learning

Shrestha, Aawesh; Won, Myounggyu

doi:10.1109/glocom.2018.8647857

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…Numerous recent ML-based works [10][11][12][13][14] predicted the PWS with a pre-trained black-box model; however, automatic feature extraction, generalization, and unbiased dataset remained a challenge on this task. Therefore, we tackled the problems of multi-pose context pedestrian walking speed estimation in a model-based way [20][21][22][23][24][25][26][27][28][29].…”

Section: Discussionmentioning

confidence: 99%

“…The sensor-based algorithms use smartphone inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to estimate the pedestrian walking speed and can be further divided into two subcategories, i.e., machine learning (ML)-based methods [10][11][12][13][14] and speed model-based methods [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The ML-based methods have the potential to exploit associative information within the data beyond an explicit model chosen by the system designer [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, the automatic selection and extraction of notable features remain a challenge. Consequently, a deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of the smartphone and to train the network model for speed estimation [14]. Similarly, the deep learning method generally requires a large amount of labeled training data.…”

Section: Introductionmentioning

confidence: 99%

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A Pose Awareness Solution for Estimating Pedestrian Walking Speed

Guo

Chen

et al. 2018

Remote Sensing

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Pedestrian walking speeds (PWS) can be used as a “body speedometer” to reveal health status information of pedestrians and positioning indoors with other locating methods. This paper proposes a pose awareness solution for estimating pedestrian walking speeds using the sensors built in smartphones. The smartphone usage pose is identified by using a machine learning approach based on data from multiple sensors. The data are then coupled tightly with an adaptive step detection solution to estimate the pedestrian walking speed. Field tests were carried out to verify the advantages of the proposed algorithms compared to existing solutions. The test results demonstrated that the features extracted from the data of the smartphone built-in sensors clearly reveal the characteristics of the pose pattern, with overall accuracy of 98.85% and a kappa statistic of 98.46%. The proposed walking speed estimation solution, running in real-time on a commercial smartphone, performed well, with a mean absolute error of 0.061 m/s, under a challenging walking process combining various usage poses including texting, calling, swinging, and in-pocket modes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Pose Awareness Solution for Estimating Pedestrian Walking Speed

Guo

Chen

et al. 2018

Remote Sensing

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“…Inertial navigation based on data: Many recent studies have used data‐driven technology, yet their focus is on identifying gait patterns rather than estimating walking speed [25]. At the beginning of this research phase, DeepWalking uses data‐driven technology to accomplish speed regression [26].…”

Section: Related Workmentioning

confidence: 99%

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

Jamil

Kim

2023

CAAI Trans on Intel Tech

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In the event of a fire breaking out or in other complicated situations, a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures. Thus, it is crucial to increase the positioning technology's accuracy. The sequential Monte Carlo (SMC) approach is used in various applications such as target tracking and intelligent surveillance, which rely on smartphone‐based inertial data sequences. However, the SMC method has intrinsic flaws, such as sample impoverishment and particle degeneracy. A novel SMC approach is presented, which is built on the weighted differential evolution (WDE) algorithm. Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction, like in a high‐dimensional space, such as a multistory structure. Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set, prevent the usage of an inadequate number of valid samples, and preserve smartphone user position accuracy. The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC, which aids in fast approximating the posterior distributions, to speed up the particle congregation process in the proposed SMC‐based WDE approach. Lastly, the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users. According to simulation findings, the suggested approach provides improved location estimation with reduced localization error and quick convergence. The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92% better in terms of MAPE, 15.24% for the case of MAE, and 0.031% when evaluating based on the R2 Score.

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“…Some machine learning methods have been developed to estimate free-living walking speed from specific accelerometer-based sensors (Schimpl et al, 2011 ), or smartphones (Silsupadol et al, 2017 ; Shrestha and Won, 2018 ). However, the different models in these studies (Schimpl et al, 2011 ; Silsupadol et al, 2017 ; Shrestha and Won, 2018 ) were mostly tested and validated in a supervised environment, using short distance walks in hallways, treadmills, or a measuring wheel rather than in the real-world during unconstrained and unsupervised walking (Warmerdam et al, 2020 ). New validated models that derive speed using data from acceleration-only devices used in free-living environments remain an open research problem.…”

Section: Introductionmentioning

confidence: 99%

Estimating Walking Speed in the Wild

Baroudi

Newman

Jackson

et al. 2020

Front. Sports Act. Living

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An individual's physical activity substantially impacts the potential for prevention and recovery from diverse health issues, including cardiovascular diseases. Precise quantification of a patient's level of day-to-day physical activity, which can be characterized by the type, intensity, and duration of movement, is crucial for clinicians. Walking is a primary and fundamental physical activity for most individuals. Walking speed has been shown to correlate with various heart pathologies and overall function. As such, it is often used as a metric to assess health performance. A range of clinical walking tests exist to evaluate gait and inform clinical decision-making. However, these assessments are often short, provide qualitative movement assessments, and are performed in a clinical setting that is not representative of the real-world. Technological advancements in wearable sensing and associated algorithms enable new opportunities to complement in-clinic evaluations of movement during free-living. However, the use of wearable devices to inform clinical decisions presents several challenges, including lack of subject compliance and limited sensor battery life. To bridge the gap between free-living and clinical environments, we propose an approach in which we utilize different wearable sensors at different temporal scales and resolutions. Here, we present a method to accurately estimate gait speed in the free-living environment from a low-power, lightweight accelerometer-based bio-logging tag secured on the thigh. We use high-resolution measurements of gait kinematics to build subject-specific data-driven models to accurately map stride frequencies extracted from the bio-logging system to stride speeds. The model-based estimates of stride speed were evaluated using a long outdoor walk and compared to stride parameters calculated from a foot-worn inertial measurement unit using the zero-velocity update algorithm. The proposed method presents an average concordance correlation coefficient of 0.80 for all subjects, and 97% of the error is within ±0.2m· s−1. The approach presented here provides promising results that can enable clinicians to complement their existing assessments of activity level and fitness with measurements of movement duration and intensity (walking speed) extracted at a week time scale and in the patients' free-living environment.

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DeepWalking: Enabling Smartphone-Based Walking Speed Estimation Using Deep Learning

Cited by 13 publications

References 24 publications

A Pose Awareness Solution for Estimating Pedestrian Walking Speed

A Pose Awareness Solution for Estimating Pedestrian Walking Speed

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

Estimating Walking Speed in the Wild

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