Gait signals classification and comparison

Barua, Arnab; Yang, Xiaodong; Ren, Aifeng; Fan, Dou; Zhao, Nan; Haider, Daniyal

doi:10.1002/jnm.2577

Cited by 8 publications

(13 citation statements)

References 39 publications

(72 reference statements)

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“…The system works, as AP pings at 20 packets / seconds and the receiver receive the WCI of different body motions in the form of matrix H (channel matrix) of 30 x 1 [13] [14]. The system captures the CFR of 30 subcarriers and the obtained CFR gives the information regarding frequencies and number of antennas used in the sensing process.…”

Section: Resultsmentioning

confidence: 99%

Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm

Haider

Romain

Kernec

et al. 2019

2019 UK/ China Emerging Technologies (UCET)

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The modern wireless technology exploiting the full potential of 5G IoT is the future for healthcare sector. In the healthcare sector, the 5G technology will maximize the performance and will reduce the chances of damage to the patient by providing careful and advance activity monitoring scenarios. We have proposed the idea of monitoring different body posture in Huntington disease by exploiting the low cost wireless devices operating at 4.8 GHz frequency. The system captures the wireless channel information for three body motions and classification of these motions was performed by using support vector machine. The SVM used 10 time-domain features for the classification process by using three main kernel functions, such as, Linear, Polynomial and Radial basis function. The system minimizes all the external noise by using the microwave absorbing materials. This increases the performance and feasibility of sensing body motions.

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Section: Resultsmentioning

confidence: 99%

Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm

Haider

Romain

Kernec

et al. 2019

2019 UK/ China Emerging Technologies (UCET)

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“…This system can identify the particular condition of a patient efficiently [ 70 ]. The wireless signal technology successfully detects human motions and related diseases in a non-contact manner [ 71 ]. Heart rate and breathing patterns of a person are major indicators of a physical condition.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SVM algorithm is widely applied because it is applicable to both linear and non-linear data. The classification accuracy achieved by ML and DL algorithms is over 90% [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. The average accuracy achieved by various non-contact sensing technologies to monitor health issues is shown in Figure 2 .…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan

Zhang

et al. 2020

Micromachines

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The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.

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“…SVM proposed by Vipnik [36] and capable of providing high performance in classification tasks than other machine learning algorithms [39]- [41]. SVM maximizes the geometrical margin and minimizes the empirical classification error that why it's called a maximum margin classifier and belongs to the family of generalized linear classification [42]. SVM creates a map of spread vectors in a high dimensional space where a maximal separating hyperplane created [43] and two parallel hyperplanes created with a maximum separating hyperplane in the middle.…”

Section: B Support Vector Machinementioning

confidence: 99%

“…k-nearest neighbor (k-NN) known as an admirable statistical machine learning algorithm and can be used for classification and regression predictive issues. k-NN is very straightforward in use and broadly use for data classification issues in the industry [42]. Measuring the distance between all the training and testing samples, the appropriate choice of the neighbors with greater distance, is the common behavior of k-NN [12].…”

Section: K-nearest Neighbormentioning

confidence: 99%

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

et al. 2020

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Wireless localization systems have significant impact in the field of human-driven edge computing (HEC). It became very attractive among the researchers and used in applications of numerous areas such as medical, industrial, public safety, logistics, and so on. Ultra-wideband (UWB) technology used in localization systems owing to achieving high accuracy in real-time. In this paper, we exhibit a UWB based localization system based on the edge computing (EC) paradigm to analyze the wandering behavior of the patients who are suffering from dementia disease in the large-scale form. Physical changes in the brain are responsible for dementia disease. The appearance of wandering behavior is a common manner of the patients, which also a threat and interference for caregivers. We used the UWB standard appliance to symbolize various sorts of wandering patterns, including pacing, lapping, and two random movements in the large 2D map. The flow of all the movements illustrated in the X and Y-axis. Support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms used to classify all the patterns and accuracy result is above 99%. The result shows that the proposed system can achieve high accuracy in classification and satisfactory for applications in the medical area.

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Gait signals classification and comparison

Cited by 8 publications

References 39 publications

Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm

Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

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