Personalized Stride-Length Estimation Based on Active Online Learning

Wang, Qu; Luo, Haiyong; Ye, Langlang; Men, Aidong; Zhao, Fang; Huang, Yan; Ou, Changhai

doi:10.1109/jiot.2020.2971318

Cited by 32 publications

(7 citation statements)

References 34 publications

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“…The researchers in [6] used cameras as additional sensors in pedestrian dead reckoning (PDR) to analyze step length and step frequency. Currently, PDR is a popular indoor localization method [19,20] due to the wide availability of smart devices. Cameras were also employed in [7] to track the motions of the person under test.…”

Section: Related Workmentioning

confidence: 99%

“…The smart device can be held in hand [22] or attached to the body, such as the pelvis [23], which provides useful information and helps position the point of interest. References [19,20] estimated the human stride length based on the data collected from inertial sensor measurements from a smartphone. The experimental results demonstrated that the step length can be estimated with an error rate of 4.63% for indoor scenarios.…”

Section: Related Workmentioning

confidence: 99%

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Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

Yang

Tran

Safaei

2022

Sensors

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In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

Yang

Tran

Safaei

2022

Sensors

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“…Therefore, a walking mode classifier is designed (see Fig. 25) based on the stacked denoising autoencoder [52] and temporal Convolutional neural network (CNN) with attention to recognize eight pedestrian motion modes [53], [54].…”

Section: A) Thementioning

confidence: 99%

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Potortì

Torres-Sospedra²,

Quezada-Gaibor

et al. 2022

IEEE Sensors J.

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Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Footmounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

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“…This means that tracking is unaffected by any possible repositioning of the phone (e.g., if the user moves the phone to a different pocket). A number of other learning-based algorithms for computing the walker's velocity, or for detecting steps and measuring stride lengths, have been recently proposed [33,34,[54][55][56][57][58][59][60][61].…”

Section: Learning-based Odometrymentioning

confidence: 99%

Smartphone-Based Inertial Odometry for Blind Walkers

Ren

Elyasi

Manduchi

2021

Sensors

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Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

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Personalized Stride-Length Estimation Based on Active Online Learning

Cited by 32 publications

References 34 publications

Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Smartphone-Based Inertial Odometry for Blind Walkers

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