An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors

Sun, Yingnan; Yang, Guang‐Zhong; Lo, Benny

doi:10.1109/bsn.2018.8329676

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…19 Fang et al [73] 79.24 Maitre et al [74] 84.89 Rasnayaka et al [75] 85 O'Halloran et al [76] 90. 55 Sun et al [77] 88 Tahir et al [23] 90.91 Badawi et al [25] 88 Masum et al [78] 91. 68 Kumari et al [79] 91.1 Ha et al [80] 91.94 --Guo et al [81] 92.…”

Section: Methods Accuracy Using Mhealth (%) Methods Accuracy Using Hugamentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

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Section: Methods Accuracy Using Mhealth (%) Methods Accuracy Using Hugamentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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“…As stated in [15], the frequency of acceleration introduced by arm swing overlaps with the frequency of the torso movement, so they cannot be separated simply by applying filters. To solve this problem, we propose the use of ANN-based gait signal estimation [19] to project the gait signals acquired from body worn sensors onto the chest, to minimize the gait signal differences among sensors and improve the performance of the security scheme. The estimated gait signals will have similar signal patterns and energy variations, from which similar binary keys can be extracted for the symmetric BCS approach.…”

Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network Framework for Gait-Based Biometrics

Sun

2019

IEEE J. Biomed. Health Inform.

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As the popularity of wearable and implantable Body Sensor Network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN devices are often not able to provide strong security mechanisms to protect sensitive personal and health information, such as one's physiological data. Consequently, many new methods of securing Wireless Body Area Networks (WBANs) have been proposed recently. One effective solution is the Biometric Cryptosystem (BCS) approach. BCS exploits physiological and behavioral biometric traits, including face, iris, fingerprints, Electrocardiogram (ECG), and Photoplethysmography (PPG). In this paper, we propose a new BCS approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an Artificial Neural Network (ANN) framework. By simultaneously extracting similar features from BSN sensors using our approach, binary keys can be generated on demand without user intervention. Through an extensive analysis on our BCS approach using a gait dataset, the results have shown that the binary keys generated using our approach have high entropy for all subjects. The keys can pass both NIST and Dieharder statistical tests with high efficiency. The experimental results also show the robustness of the proposed approach in terms of the similarity of intra-class keys and the discriminability of the inter-class keys.

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“…The main difference between them is that the shallow uses a single hidden layer, whereas the DNN use multiple hidden layers. Therefore, a DNN is an artificial neural network that has multi-hidden layers located between the input and output layers where every layer utilizes the former layer output as an input so, the neurons in DNN layers form the hierarchy [12]. Therefore, when deep learning first appeared it was known as hierarchical learning [13].…”

Section: Neural Network Typesmentioning

confidence: 99%

Enhancing the stability of the deep neural network using a non-constant learning rate for data stream

Al-Khamees

Al-A’araji

Al-Shamery

2023

IJECE

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The data stream is considered the backbone of many real-world applications. These applications are most effective when using modern techniques of machine learning like deep neural networks (DNNs). DNNs are very sensitive to set parameters, the most prominent one is the learning rate. Choosing an appropriate learning rate value is critical because it is able to control the overall network performance. This paper presents a new developing DNN model using a multi-layer perceptron (MLP) structure that includes network training based on the optimal learning rate. Thereupon, this model consists of three hidden layers and does not adopt the stability of the learning rate but has a non-constant value (varying over time) to obtain the optimal learning rate which is able to reduce the error in each iteration and increase the model accuracy. This is done by deriving a new parameter that is added to and subtracted from the learning rate. The proposed model is evaluated by three streaming datasets: electricity, network security layer-knowledge discovery in database (NSL-KDD), and human gait database (HuGaDB) datasets. The results proved that the proposed model achieves better results than the constant model and outperforms previous models in terms of accuracy, where it achieved 88.16%, 98.67%, and 97.63% respectively.

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An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors

Cited by 11 publications

References 10 publications

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

An Artificial Neural Network Framework for Gait-Based Biometrics

Enhancing the stability of the deep neural network using a non-constant learning rate for data stream

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