Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

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

doi:10.3390/s19040840

Cited by 70 publications

(60 citation statements)

References 45 publications

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“…To clearly illustrate the error distribution of stride length estimation, we employed CDF (cumulative distribution function) and box plots to compare the statistics of single stride length estimation errors, as described in Figure 16 and Figure 17. From Figure 16, we can see that the relative error of the proposed algorithm was smaller than those achieved by the Tapeline [38], Kim [24], Weinberg [23], and Ladetto [22]. In the box plots, the vertical axis and horizontal axis correspond to…”

Section: Experimental Results Of Stride Length Estimationmentioning

confidence: 96%

“…However, these methods required the user to wear a special device in a specific position on the body. In our previous work [38], a stride length estimation method based on long short-term memory (LSTM) and denoising autoencoders (DAE), termed Tapeline, was proposed. Tapeline [38] first leveraged a LSTM network to excavate the temporal dependencies and extract significant features vectors from noisy inertial sensor measurements.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [38], a stride length estimation method based on long short-term memory (LSTM) and denoising autoencoders (DAE), termed Tapeline, was proposed. Tapeline [38] first leveraged a LSTM network to excavate the temporal dependencies and extract significant features vectors from noisy inertial sensor measurements. Afterwards, denoising autoencoders were adopted to sanitize the inherent noise and obtain denoised feature vectors automatically.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Wang

Luo

et al. 2019

Remote Sensing

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Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

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Section: Experimental Results Of Stride Length Estimationmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Wang

Luo

et al. 2019

Remote Sensing

Self Cite

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“…To improve their accuracy dozens of motion models have been examined: models of empirical relationships [15,16], biomechanical models [15,17,18], linear models [19], non-linear models [20][21][22], and models that are based on regression [19,[23][24][25]. However, they also suffer from orientation variance and provide less accuracy than data-driven state-of-the-art techniques [11,12,26].…”

Section: Introductionmentioning

confidence: 99%

RNN-Aided Human Velocity Estimation from a Single IMU

Feigl

Kram

Woller

et al. 2020

Sensors

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Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.

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“…This being a low-cost solution also takes away the need to mount an external sensor/device to capture motion related data. Use of inertial data obtained using smartphones has shown reasonable estimate of person's age [8], identity [22], gait [23,24], step count [25], stride length [26], walk distance [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

Gohar

Riaz

Shahzad

et al. 2020

Sensors

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Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual’s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one’s inertial sensor data.

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Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

Cited by 70 publications

References 45 publications

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

RNN-Aided Human Velocity Estimation from a Single IMU

Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns

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