A Survey on Gait Recognition via Wearable Sensors

Marsico, Maria De; Mecca, Alessio

doi:10.1145/3340293

Cited by 84 publications

(53 citation statements)

References 77 publications

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“…Subtle gait alterations due to internal/external factors have never been considered or properly annotated in existing public data sets. A second category of data sets are focused on utilizing human gait performance as a biometrics characteristic for human identification 12 , 25 – 30 . Therefore, creators of those data sets usually only considered between subject differences and only collected short duration of gait trials from each participant which is not sufficient to train advanced machine learning models.…”

Section: Background and Summarymentioning

confidence: 99%

A database of human gait performance on irregular and uneven surfaces collected by wearable sensors

et al. 2020

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Gait analysis has traditionally relied on laborious and lab-based methods. Data from wearable sensors, such as Inertial Measurement Units (IMU), can be analyzed with machine learning to perform gait analysis in real-world environments. This database provides data from thirty participants (fifteen males and fifteen females, 23.5 ± 4.2 years, 169.3 ± 21.5 cm, 70.9 ± 13.9 kg) who wore six IMUs while walking on nine outdoor surfaces with self-selected speed (16.4 ± 4.2 seconds per trial). This is the first publicly available database focused on capturing gait patterns of typical real-world environments, such as grade (up-, down-, and cross-slopes), regularity (paved, uneven stone, grass), and stair negotiation (up and down). As such, the database contains data with only subtle differences between conditions, allowing for the development of robust analysis techniques capable of detecting small, but significant changes in gait mechanics. With analysis code provided, we anticipate that this database will provide a foundation for research that explores machine learning applications for mobile sensing and real-time recognition of subtle gait adaptations.

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Section: Background and Summarymentioning

confidence: 99%

A database of human gait performance on irregular and uneven surfaces collected by wearable sensors

et al. 2020

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“…Marsico et al [5] performed a detailed review of existing techniques for person identification based on gait parameters extracted from wearable sensors. The authors classify biometric traits for person identification as hard and soft.…”

Section: State-of-the-artmentioning

confidence: 99%

Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns

Peinado-Contreras

Muñoz-Organero

2020

Sensors

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This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone’s accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.

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“…Wearable motion sensors makes gait analysis [16,26] much easier. The research about general gait analysis using motion sensors are focused on model-based methodology [27,28], which needs to first model gait based on a comprehensive understanding of the gait mechanism, and then convert the sensor signal into some gait-related physiological parameters [29][30][31], such as gait rhythm, step length, symmetry, inner foot distance, ankle shape, detection of gait phases [32], or kinematic parameters (joint angle measurement) [33].…”

Section: General Footwear Related Gait Analysis Using Motion Sensorsmentioning

confidence: 99%

“…Meanwhile, based on some previous papers on exercise physiology, the height of the heels is also a important parameter related to the human gait. A recent survey [16] summarized a list of the five main open problems for gait recognition including different kinds of shoes. Walking requires ongoing, finely tuned interactions between muscular and tendinous tissues.…”

Section: Introductionmentioning

confidence: 99%

Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

2020

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The human gait pattern is an emerging biometric trait for user identification of smart devices. However, one of the challenges in this biometric domain is the gait pattern change caused by footwear, especially if the users are wearing high heels (HH). Wearing HH puts extra stress and pressure on various parts of the human body and it alters the wearer’s common gait pattern, which may cause difficulties in gait recognition. In this paper, we propose the Sensing-HH, a deep hybrid attention model for recognizing the subject’s shoes, flat or different types of HH, using smartphone’s motion sensors. In this model, two streams of convolutional and bidirectional long short-term memory (LSTM) networks are designed as the backbone, which extract the hierarchical spatial and temporal representations of accelerometer and gyroscope individually. We also introduce a spatio attention mechanism into the stacked convolutional layers to scan the crucial structure of the data. This mechanism enables the hybrid neural networks to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier for the footwear recognition task. To evaluate Sensing-HH, we built a dataset with 35 young females, each of whom walked for 4 min wearing shoes with varied heights of the heels. We conducted extensive experiments and the results demonstrated that the Sensing-HH outperformed the baseline models on leave-one-subject-out cross-validation (LOSO-CV). The Sensing-HH achieved the best Fm score, which was 0.827 when the smartphone was attached to the waist. This outperformed all the baseline methods at least by more than 14%. Meanwhile, the F1 Score of the Ultra HH was as high as 0.91. The results suggest the proposed model has made the footwear recognition more efficient and automated. We hope the findings from this study paves the way for a more sophisticated application using data from motion sensors, as well as lead to a path to a more robust biometric system based on gait pattern.

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A Survey on Gait Recognition via Wearable Sensors

Cited by 84 publications

References 77 publications

A database of human gait performance on irregular and uneven surfaces collected by wearable sensors

A database of human gait performance on irregular and uneven surfaces collected by wearable sensors

Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns

Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

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