An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities

Flexible and scalable software defined radio based testbed for large scale body movement

Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems

Role of N-acetylcysteine in adults with non-acetaminophen-induced acute liver failure in a center without the facility of liver transplantation.

PURPOSE: We aimed to study the role of N-acetylcysteine (NAC) in non-acetaminophen-induced acute liver failure (NAI-ALF). METHODS: A total of 47 adult patients were prospectively enrolled with NAI-ALF (group 1 or NAC group) and oral NAC was given. The primary outcome was reduction in mortality with the use of NAC in NAI-ALF. The secondary outcomes were to evaluate safety of NAC and to assess factors predicting mortality. We compared these results with records of NAI-ALF patients admitted in our hospital from 2000 to 2003 (n = 44) who were not given NAC (group 2 or historical controls). RESULTS: The two groups were comparable for the etiology of ALF, prothrombin time (PT), alanine aminotransferase, creatinine, albumin, etc. The mean age in group 1 was 27.7 ± 11.8 years and in group 2 37.5 ± 18.8 years (P = 0.004). Bilirubin was 20.63 ± 11.03 and 14.36 ± 8.90 mg/dl in groups 1 and 2, respectively (P = 0.004). There were 8 (17%) and 1 (2.3%) pregnant ALF women with acute hepatitis E virus (HEV) infection in groups 1 and 2, respectively (P = 0.031). All patients were given supportive care, including mechanical ventilation. A total of 34 (37.36%) patients survived; 22 (47%) in group 1 (NAC group) and 12 (27%) in group 2 (controls) (P = 0.05). On multivariable regression analysis, patients not given NAC (odds ratio [OR] = 10.3, 95% confidence interval [CI] = 1.6-65.7), along with age older than 40 years (OR = 10.3, 95% CI = 2.0-52.5), PT more than 50 s (OR = 15.4, 95% CI = 3.8-62.2), patients requiring mechanical ventilation (OR = 20.1, 95% CI = 3.1-130.2), and interval between jaundice and hepatic encephalopathy (OR = 5.0, 95% CI = 1.3-19.1) were independent predictors of mortality

Agentless approach for security information and event management in industrial IoT

The Internet of Things (IoT) provides ease of real-time communication in homes, industries, health care, and many other dependable and interconnected sectors. However, in recent years, smart infrastructure, including cyber-physical industries, has witnessed a severe disruption of operation due to privilege escalation, exploitation of misconfigurations, firmware hijacking, malicious node injection, botnets, and other malware infiltrations. The proposed agentless module for Wazuh security information and event management (SIEM) solution contributes to securing small- to large-scale IoT networks of industry 4.0. An agentless module is implemented by vigilantly examining the IoT device traffic without installing any agent or software on the endpoints. In the proposed research scheme, a module sniffs the network traffic of IoT devices captured from the gateway and passes it to a machine learning model for initial detection and prediction. The output of the ML model is embedded in the JSON log format and passed through the Wazuh agent to the Wazuh server where a decoder is added that decodes the network traffic logs. For event monitoring in Wazuh, industrial protocols are also thoroughly analyzed, and the feature set is determined. These features are used to write rules which are tested on the SWaT dataset, utilizing a common industrial protocol (CIP) for communication. Custom and dynamic rules are written at the Wazuh end to generate alerts to respond to any anomaly detected by the machine learning (ML) model or in the protocols used. Finally, in case of any event or an attack is detected, the alerts are fired on the Wazuh dashboard. This agentless SIEM solution has practical implications for the security of the industrial control systems of industry 4.0

Comparative Evaluation of Efficacy, Safety and Haemostatic Parameters of Enoxaparin and Fondaparinux in Unstable Coronary Artery Disease Pharmacology Section

ABSTRACT Aim: To compare the safety and efficacy of Enoxaparin (EX) and Fondaparinux (FD) in patients with Unstable Coronary Artery Disease (UCAD). Materials and Methods: A prospective, open label, randomized comparative study was designed to study the comparative efficacy and safety of EX and FD in UCAD patients. Recovery, recurrence, major and minor bleeding and biochemical investigations were evaluated and compared among two arms. Results: The baseline demographic characteristics were similar in both groups, with mean age of 56.05 and 56.05 years in EX and FD group respectively. Recovery was equal in two arms. Recurrent MI or angina was seen numerically more in EX group, but it did not statistically vary from that in the FD group. Incidence of haemorrhage was similar in both groups at 9 days, but at 30 days, EX showed a higher incidence (p<0.05). Deaths were prevented in both the treatment arms. Bleeding parameters such as BT, CT and platelet count were not altered in both groups. Conclusion: FD appeared to be better than EX in efficacy, as was indicated by a numerically more decrease in recurrence of angina or MI. FD regimen group also had better safety profile, as there was no incidence of haemorrhage at 30 days Therefore, we conclude that FD is an attractive option than EX in UCAD patients

Software Defined Radio Based Testbed for Large Scale Body Movements

Monitoring Activities of Daily Livings (ADLs) has opened doors for numerous applications including patient monitoring, search & rescue, intrusion detection and so on. However, the parameterssuch as operating frequency, transmitting power, and antenna design are static where each application requires particular hardware applications. This paper lays the foundation for ADLs and presents the design of the testbed based on Universal Software Radio Peripheral (USRP) in conjunction with omni directional antenna, that can be used for detecting large scale body movements such as walking, sitting, standing, and critical events such as falls and small-scale movements. The core idea is to extract the channel state information (CSI) from the received signal since each body motion produces a unique CSI signature. In this context, we have performed various human activities such as walking, sitting on a chair etc. in indoor environment using two USRPs. The experimental results indicate that each body motion can be visually identified by examining the CSI data

The human homologue of the yeast mitochondrial AAA metalloprotease Yme1p complements a yeast yme1 disruptant

AbstractIn yeast, three AAA superfamily metalloproteases (Yme1p, Afg3p and Rca1p) are localized to the mitochondrial inner membrane where they perform roles in the assembly and turnover of the respiratory chain complexes. We have investigated the function of the proposed human orthologue of yeast Yme1p, encoded by the YME1L gene on chromosome 10p. Transfection of both HEK-293EBNA and yeast cells with a green fluorescent protein-tagged YME1L cDNA confirmed mitochondrial targeting. When expressed in a yme1 disruptant yeast strain, YME1L restored growth on glycerol at 37°C. We propose that YME1L plays a phylogenetically conserved role in mitochondrial protein metabolism and could be involved in mitochondrial pathologies

Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-invasive Sensing

In agriculture science, accurate information of moisture content (MC) in fruits and vegetables in an automated fashion can be vital for astute quality and grading evaluation. This demands for a viable, feasible and cost-effective technique for the defect recognition using timely detection of MC in fruits and vegetables to maintain a healthy sensory characteristic of fruits. Here we propose a non-invasive machine learning (ML) driven technique to monitor variations of MC in fruits using the terahertz (THz) waves with Swissto12 material characterization kit (MCK) in the frequency range of 0.75 THz to 1.1 THz. In this regard, multi-domain features are extracted from time-, frequency-, and time-frequency domains, and applied three ML algorithms such as support vector machine (SVM), knearest neighbour (KNN) and Decision Tree (D-Tree) for the precise assessment of MC in both apple and mango slices. The results illustrated that the performance of SVM exceeded other classifiers results using 10-fold validation and leave-oneobservation-out-cross-validation techniques. Moreover, all three classifiers exhibited 100 accuracy for day 1 and 4 with 80% MC value (freshness) and 2% MC value (staleness) of both fruits’ slices, respectively. Similarly, for day 2 and 3, an accuracy of 95% was achieved with intermediate MC values in both fruits’ slices. This study will pave a new direction for the real-time quality evaluation of fruits in a non-invasive manner by incorporating ML with THz sensing at a cellular level. It also has a strong potential to optimize economic benefits by the timely detection of fruits quality in an automated fashion

Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging

Parkinson’s disease (PD) is a progressive and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson’s patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ~87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ~98% using data fusion

Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring