Non invasive skin hydration level detection using machine learning

Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions

Detection of atrial fibrillation using a machine learning approach

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate

A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies

Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing

Personalized hydration level monitoring play vital role in sports, health, wellbeing and safety of a person while performing particular set of activities. Clinical staff must be mindful of numerous physiological symptoms that identify the optimum hydration specific to the person, event and environment. Hence, it becomes extremely critical to monitor the hydration levels in a human body to avoid potential complications and fatalities. Hydration tracking solutions available in the literature are either inefficient and invasive or require clinical trials. An efficient hydration monitoring system is very required, which can regularly track the hydration level, non-invasively. To this aim, this paper proposes a machine learning (ML) and deep learning (DL) enabled hydration tracking system, which can accurately estimate the hydration level in human skin using galvanic skin response (GSR) of human body. For this study, data is collected, in three different hydration states, namely hydrated, mild dehydration (8 hours of dehydration) and extreme mild dehydration (16 hours of dehydration), and three different body postures, such as sitting, standing and walking. Eight different ML algorithms and four different DL algorithms are trained on the collected GSR data. Their accuracies are compared and a hybrid (ML+DL) model is proposed to increase the estimation accuracy. It can be reported that hybrid Bi-LSTM algorithm can achieve an accuracy of 97.83%

An Improved Radio Channel Characterisation for Ultra Wideband On-body Communications Using Regression Method

In body centric wireless communication (BCWC), radio propagation modelling is an important parameter for an accurate system design like any other wireless system. To investigate and analyse the performance of single and multiple antennas for body-centric wireless communication channels, various approaches can be adopted. It can either be predicted through detailed simulations using numerical digital phantom, by real time measurements or by using a statistical channel model, which completely characterises the channels and the environment. The statistical model plays an important role in BCWC radio propagation characterization. However, a traditional statistical model is not necessarily the best choice for limited samples. In this paper statistical modeling is performed using regression method on the mean delay data to improve the density estimation of body-centric radio propagation channel

Numerical Radio Propagation Characterisation and System Level Modelling for Ultra Wideband On-body Communications

In this paper a radio propagation studies in addition to system performance evaluation for ultra wideband (UWB) (3-10.6 GHz) frequencies is being presented based on parallel finite-difference time-domain technique. In an indoor environment, effect of human body postures on the propagation channel and system performance for body area network is being studied. Results show the dependence of propagation channel characteristic and system performance on the human subject postures. Therefore, careful consideration of postures in addition to subject are important for reliable radio channels and system performance

Multiple Input Multiple Output Radio Channel Characterisation for Ultra Wideband Body Centric Wireless Communication

Complete channel knowledge and its accuracy are the basis of an optimized system design. In this paper ultrawideband multiple input multiple output (UWB-MIMO) radio channel characterization for body centric wireless communication is being presented. Body shadowing and body obstruction limits the channel capacity. It has been observed that MIMO antenna technique provide significant increase in capacity for body-centric wireless networks (BCWN). Spatial correlation matrices (complex and power correlation) have been computed and it has been noticed that the correlation values are much higher, where antennas are located in line-of-sight scenario as compared to none-line of sight case. In addition to this, capacity comparison of MIMO antenna with single input single output (SISO) and single input multiple output (SIMO) antenna system shows significant improvement for MIMO case, which supports the applicability of MIMO in BCWN

Comparison of two measurement techniques for UWB off-body radio channel characterisation

This paper presents comparison of two measurement techniques for ultra wideband (UWB) off-body radio channel characterization. A measurement campaign was performed in indoor environment using UWB wireless active tags and reader installed with the tag antenna and same set of measurement was repeated in the frequency domain using Vector Network Analyser (VNA) and cable connecting two standalone tag antennas for comparison/with a view to finding out the cable effects. Nine different off-body radio channels were experimentally investigated. Comparison of path loss parameters and path loss model for nine different off-body radio channels for the propagation in indoor environment both measurement cases are shown and analyzed. Results show that measurement taken by VNA connecting two standalone antennas through cables experiences lower path loss value for all nine different off-body channels. Least square fit technique is obtained to extract the path loss exponent. Increase of 12.96% path loss exponent is noticed when measurements are made using UWB tags and reader, i.e., without cable measurement scenario

A hybrid posture detection framework: Integrating machine learning and deep neural networks

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%