Personal‐specific gait recognition based on latent orthogonal feature space

Zhou, Quan; Shan, Jiayuan; Fang, Bin; Zhang, Shixin; Sun, Fuchun; Ding, Wenlong; Wang, Chengyin; Zhang, Qin

doi:10.1049/ccs2.12007

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The storage unit of LSTM contains three gate structures: forgetting gate, input gate, and output gate. The forgetting gate determines the information that needs to be retained and discarded from the previous storage unit, the input gate parses the information that needs to be updated to the storage unit, and the output gate determines the output information according to the input and storage unit [24,25]. It is precise because LSTM has a unique internal structure that it can process time series signals well.…”

Section: Data Preprocessingmentioning

confidence: 99%

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors

Liu

et al. 2023

Sensors

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Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is proposed. In the LSTM-CNN model, the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features A wireless sensor system including six inertial measurement units (IMU) fixed on the six positions of the lower limbs was developed. The difference in the gait recognition performance of the LSTM-CNN model was estimated using different groups of input data collected by seven different IMU grouping methods. Four phases in a complete gait were considered in this paper including the supporting phase with the right hill strike (SU-RHS), left leg swimming phase (SW-L), the supporting phase with the left hill strike (SU-LHS), and right leg swimming phase (SW-R). The results show that the best performance of the model in gait recognition appeared based on the group of data from all the six IMUs, with the recognition precision and macro-F1 unto 95.03% and 95.29%, respectively. At the same time, the best phase recognition accuracy for SU-RHS and SW-R appeared and up to 96.49% and 95.64%, respectively. The results also showed the best phase recognition accuracy (97.22%) for SW-L was acquired based on the group of data from four IMUs located at the left and right thighs and shanks. Comparably, the best phase recognition accuracy (97.86%) for SU-LHS was acquired based on the group of data from four IMUs located at left and right shanks and feet. Ulteriorly, a novel gait recognition method based on Data Pre-Filtering Long Short-Term Memory and Convolutional Neural Network (DPF-LSTM-CNN) model was proposed and its performance for gait phase recognition was evaluated. The experiment results showed that the recognition accuracy reached 97.21%, which was the highest compared to Deep convolutional neural networks (DCNN) and CNN-LSTM.

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Section: Data Preprocessingmentioning

confidence: 99%

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors

Liu

et al. 2023

Sensors

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“…Zhou et al [23]. have proposed a model based on Long Short-Term Memory (LSTM) and is combined with orthogonalization method to separate out and enhance the generalization ability of the model for different groups and follow the exoskeleton more precisely.…”

Section: Relevant Workmentioning

confidence: 99%

Gait Recognition Using Convolutional Neural Network

Sheth¹,

Sharath²,

Reddy³

et al. 2023

Int. J. Onl. Eng.

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Biometrics are the body measurements and calculations related to individuals. Biometrics validation is used as a form of identification of individual. Gait recognition system is one of the most advanced technology that people have been working on for a while now that takes center stage in the field of biometrics. Compared to the other types of existing systems of biometric recognition such as fingerprint detection, iris-scanning systems etc., Gait Recognition system ensures no human intervention. This paper focuses on recognition based on a person’s gait. Every person has a distinct gait pattern that is unique to every other person. To train the model CASIA-B dataset has been used. The dataset includes 124 subjects where each sample has undergone Gait Energy Image extraction. Samples with clothing and baggage have been included which changes the silhouette of the person. Therefore, the model has been trained for a wider application where people wear different type of clothing and carry-ons. A Convolutional Neural Network consisting of 8 layers has been trained which performs well on both samples of dataset and an accuracy of 95.45% was obtained on dataset not involving layers of clothing and accuracy of 91.80% was obtained for the sample with clothing and baggage.

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A perspective on human activity recognition from inertial motion data

Gomaa,

Khamis

2023

Neural Comput & Applic

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Human activity recognition (HAR) using inertial motion data has gained a lot of momentum in recent years both in research and industrial applications. From the abstract perspective, this has been driven by the rapid dynamics for building intelligent, smart environments, and ubiquitous systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc., which necessitate and subsume activity recognition aiming at recognizing the actions, characteristics, and goals of one or more agent(s) from a temporal series of observations streamed from one or more sensors. From a more concrete and seemingly orthogonal perspective, such momentum has been driven by the ubiquity of inertial motion sensors on-board mobile and wearable devices including smartphones, smartwatches, etc. In this paper we give an introductory and a comprehensive survey to the subject from a given perspective. We focus on a subset of topics, that we think are major, that will have significant and influential impacts on the future research and industrial-scale deployment of HAR systems. These include: (1) a comprehensive and detailed description of the inertial motion benchmark datasets that are publicly available and/or accessible, (2) feature selection and extraction techniques and the corresponding learning methods used to build workable HAR systems; we survey classical handcrafted datasets as well as data-oriented automatic representation learning approach to the subject, (3) transfer learning as a way to overcome many hurdles in actual deployments of HAR systems on a large scale, (4) embedded implementations of HAR systems on mobile and/or wearable devices, and finally (5) we touch on adversarial attacks, a topic that is essentially related to the security and privacy of HAR systems. As the field is very huge and diverse, this article is by no means comprehensive; it is though meant to provide a logically and conceptually rather complete picture to advanced practitioners, as well as to present a readable guided introduction to newcomers. Our logical and conceptual perspectives mimic the typical data science pipeline for state-of-the-art AI-based systems.

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Personal‐specific gait recognition based on latent orthogonal feature space

Cited by 4 publications

References 19 publications

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors

Gait Recognition Using Convolutional Neural Network

A perspective on human activity recognition from inertial motion data

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