Experimental Characterization of Artificial Human Skin with Melanomas for Accurate Modelling and Detection in Healthcare Application

A preliminary investigation is carried out on the artificial human skin tissues with and without metastatic melanomas using Terahertz Time Domain Spectroscopy (THz-TDS). Both the refractive indexes and absorption coefficients of artificial skin with melanomas are higher than the normal artificial skin samples over the entire frequency range between 0.2 THz to 1.6 THz. The reason is that tumour cells degrade the contraction of fibroblasts causing more water content in malignant tissues. This study quantifies the impact of melanomas on the optical parameters of artificial skin tissue and can help in techniques that will diagnose and prevent tumours at the early stage

Non-invasive RF sensing for detecting breathing abnormalities using software defined radios

The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data ( 91%) each time the trial was made. DT (17.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations

Non-contact smart sensing of physical activities during quarantine period using SDR technology

The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases

Impact of cell density and collagen concentration on the electromagnetic properties of dermal equivalents in the terahertz band

In this paper, the measurement of collagen gels and dermal equivalents (DEs) with three kinds of fibroblast cell densities and collagen concentrations is performed, respectively, using terahertz time-domain spectroscopy (THz-TDS) in transmission mode. With the objective to reduce the thickness uncertainty, an algorithm to extract material parameters and sample thickness of thin samples simultaneously is employed. The statistical mean and standard deviation of the refractive indexes and absorption coefficients in a THz band from 0.2 to 1.5 THz are presented. It is shown that the refractive indexes of DEs drop by around 10%, whereas the absorption coefficients decrease by 20% when cell density increases by one order from 0.1 to 1 M/mL. In addition, the complex refractive indexes of DEs with 3-mg/mL collagen are higher than those with 2 mg/mL, whereas the THz optical parameters of DEs with 2.5 mg/mL are the lowest. The obtained results indicate that the difference in the intrinsic biological features, initial cell density, and collagen concentration changes the tissue hydration, which in turn brings changes to the electromagnetic properties

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children

The last decade’s developments in sensor technologies and artificial intelligence applications have received extensive attention for daily life activity recognition. Autism spectrum disorder (ASD) in children is a neurological development disorder that causes significant impairments in social interaction, communication, and sensory action deficiency. Children with ASD have deficits in memory, emotion, cognition, and social skills. ASD affects children’s communication skills and speaking abilities. ASD children have restricted interests and repetitive behavior. They can communicate in sign language but have difficulties communicating with others as not everyone knows sign language. This paper proposes a body-worn multi-sensor-based Internet of Things (IoT) platform using machine learning to recognize the complex sign language of speech-impaired children. Optimal sensor location is essential in extracting the features, as variations in placement result in an interpretation of recognition accuracy. We acquire the time-series data of sensors, extract various time-domain and frequency-domain features, and evaluate different classifiers for recognizing ASD children’s gestures. We compare in terms of accuracy the decision tree (DT), random forest, artificial neural network (ANN), and k-nearest neighbour (KNN) classifiers to recognize ASD children’s gestures, and the results showed more than 96% recognition accuracy