Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Zou, Qin; Wang, Yanling; Wang, Qian; Zhao, Yi; Li, Qingquan

doi:10.1109/tifs.2020.2985628

Cited by 209 publications

(166 citation statements)

References 91 publications

(83 reference statements)

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“…Classifying gait events is a typical task which could be addressed by machine learning and deep learning techniques. Many examples were reported in literature [9][10][11][12]. However, only few reports addressed the issue of gait-phase classification from sEMG signal only [13][14][15].…”

Section: Aim Of the Studymentioning

confidence: 99%

“…Wang and Zieliska [12] designed an EMG-based method for detecting the variability in gait features depending on footwear, by applying vector quantization classifying networks and clustering competitive networks. Zou et al [10] performed gait recognition analyzing inertial sensor data by means of deep convolutional neural network (and deep recurrent neural network approaches…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of 94.9 for learned subjects and 93.4 for unlearned ones, while mean absolute difference ( ± S D ) between phase transitions timing predictions and footswitch data was 21.6 ms and 38.1 ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot–floor-contact signal from sEMG signals during level ground walking.

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Section: Aim Of the Studymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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“…There are multiple forms of deep learning models, e.g., multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) [35]. Different models fit for different types of problems, and among all of those models, MLP is a very common and representative form of deep learning architecture.…”

Section: B Deep Learningmentioning

confidence: 99%

Privacy-Preserving Collaborative Deep Learning With Unreliable Participants

Zhao

Wang

Zou

et al. 2020

IEEE Trans.Inform.Forensic Secur.

Self Cite

162

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With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy.In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve -differential privacy. In particular, for the first time, we consider the existence of unreliable participants, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

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“…They reported a good performance, but they only evaluated their proposal in controlled tests walking while texting and swinging. Zou et al [22] used deep learning techniques to learn and model the gait biometrics. In particular, features from time and frequency domains were successfully abstracted by combining a convolutional neural network with a recurrent neural network.…”

Section: Feature-based Approachmentioning

confidence: 99%

Walking Recognition in Mobile Devices

Casado

Rodríguez²,

Iglesias

et al. 2020

Sensors

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Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.

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Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Cited by 209 publications

References 91 publications

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

Privacy-Preserving Collaborative Deep Learning With Unreliable Participants

Walking Recognition in Mobile Devices

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