Gait recognition via random forests based on wearable inertial measurement unit

Shi, Ling‐Feng; Qiu, Chao-Xi; Xin, Dong-Jin

doi:10.1007/s12652-020-01870-x

Cited by 24 publications

(15 citation statements)

References 19 publications

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“…We ensured that all the included papers validated their proposed techniques by comparison against the gold standard measurement such as motion capture and/or force plate systems or other widely accepted measurement methods, such as using an instrumented treadmill or the GAITRite® system. There were some exceptions to this criterion, such as studies that used force-based sensors [54], [94] on the foot to directly estimate the initial/terminal contact of the foot, analyzed only the statistical features from the raw data [55], employed a machine learning-based algorithm for classification [80], [111], or cited their previously validated framework [78], [88].…”

Section: Synthesis Of Resultsmentioning

confidence: 99%

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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The current gold standard for gait analysis involves performing the gait experiments in a laboratory environment with a constrained space. However, there is growing interest in using flexible, efficient, and inexpensive wearable sensors as tools to perform gait analysis. This review aimed to identify and summarize the current advances in wearable sensors for various aspects of gait analysis, such as the application of wearable gait analysis systems, sensor systems and their attachment locations, and the algorithms used for the analysis. The PRISMA guideline was adopted to find relevant studies from the period 2011 to 2020 from several scientific databases. A total of 76 articles were selected based on the inclusion and exclusion criteria. A wearable inertial measurement unit (IMU) attached to the lower limb region was found to be the most common approach for gait analysis. Temporal, spatial, and spatiotemporal features were the most common quantitative gait features extracted from the wearable sensors. The proposed frameworks showed varying performances, and an increased number of sensors did not necessarily improve the estimation performance metrics. A few studies have integrated various machine learning techniques for classification problems, correction algorithms, crosschecking functions, and scoring functions. Finally, this review paper discusses the challenges and future direction of the research on quantitative gait analysis.

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Section: Synthesis Of Resultsmentioning

confidence: 99%

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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“…In order to determine the effectiveness of the proposed CNN-based model, it is compared with five baseline methods namely Support Vector Machine (SVM) [34], Decision Tree (DT) [35], Random Forest (RF) [36], Logistic Regression (LR) [37], and K-Nearest Neighbours (KNN) [38]. The models are mainly evaluated based on four performance metrics namely accuracy, precision, recall, and F1 score.…”

Section: Baselines and Evaluation Metricsmentioning

confidence: 99%

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

Middya

Roy

Mandal

et al. 2021

Neural Comput & Applic

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In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users' identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

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“…A lot of research is being carried out on Gait at present. The analysis of human gait was started in the 1960 s [2]. At the start, the Human Gait Recognition (HGR) was used for the diagnosis of various diseases such as spinal stenosis, Parkinson's [3], and walking pattern distortion due to age factor.…”

Section: Introductionmentioning

confidence: 99%

Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

Mehmood¹,

Khan²,

Tariq³

et al. 2022

Computers, Materials &Amp; Continua

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Background-Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed-In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained CNN model VGG-16 for the computation of the features, removing redundant features extracted from the CNN model, and classification. In the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named PSbK. After that, the features of PSbK are fused in one materix. Finally, this fused vector is fed to the One against All Multi Support Vector Machine (OAMSVM) classifier for the final results. Results-The system is evaluated by utilizing the CASIA B database and six angles 00 • , 18 • , 36 • , 54 • , 72 • , and 90 • are used and attained the accuracy of 95.80%, 96.0%, 95.90%, 96.20%, 95.60%, and 95.50%, respectively. Conclusion-The comparison with recent methods show the proposed method work better.

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Gait recognition via random forests based on wearable inertial measurement unit

Cited by 24 publications

References 19 publications

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

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