Pervasive Healthcare Monitoring Research Articles

Secure and Privacy-Preserving Human Interaction Recognition of Pervasive Healthcare Monitoring

A cloud-based Artificial Intelligence (AI) service has recently empowered the Internet of Medical Things (IoMT) in many applications on the remote Human Interaction Recognition of Pervasive Healthcare Monitoring (HIR-PHM). In this work, a framework of the skeleton-based HIR-PHM under secure Edge-Fog-Cloud computing (EFCC) was proposed to manage the computation and storage resources, latency, cyber-attack, and privacy-preserving simultaneously. At the Edge, IoMT with a camera, record the human interaction as videos and sends them to the Fog, which installed a human pose estimation model, PoseNet, to convert the videos into human skeleton data. At the Cloud, a skeleton-based Spatial-Temporal Graph Convolution Network with Pairwise Adjacency Matrix (STGCN-PAM) was employed to recognize the human interaction. A Hybrid of One Time Password (OTP), Hashed Messages Authentication Code (HMAC), and symmetric cipher were employed to secure the skeleton data. The UT-Interaction dataset was used to evaluate the proposed framework. Besides, the computation performance and latency under EFCC were compared with Edge-Fog and Edge-Cloud deployments. Experimental results confirmed two major contributions: 1) the proposed framework has promising performance compared with other methods on the dataset. 2) The skeleton-based HIR-PHM with secure EFCC shows the advantages of better computation performance in terms of frame per second.

Empirical analysis of chronic disease dataset for multiclass classification using optimal feature selection based hybrid model with spark streaming

Recent advancement in the field of pervasive healthcare monitoring systems causes the generation of a huge amount of lifelog data in real-time. Chronic diseases are one of the most serious health challenges in developing and developed countries. According to WHO, this accounts for 73% of all deaths and 60% of the global burden of diseases. Chronic disease classification models are now harnessing the potential of lifelog data to explore better healthcare practices. This paper basically constructs an optimal feature selection-based hybrid model which is designed by integrating k-means clustering for handling unlabeled data, applying Synthetic Minority Oversampling Technique (SMOTE), selecting optimal features using PCA, and applying Random Forest (RF) classifier to classify chronic diseases. Since lifelog data analysis is crucial due to its sensitive nature; thus the conventional classification models show limited performance. Therefore, designing new classifiers for the classification of chronic diseases using lifelog data is the need of the age. The vital part of building a good model depends on pre-processing of the dataset, identifying important features, and then training a learning algorithm with suitable hyper parameters for better performance. The proposed approach improves the performance of existing methods using a series of steps such as (i) removing redundant or invalid instances, (ii) making the data labeled using clustering and partitioning the data into classes, (iii) making the classes balance to avoid biased result across majority class (iv) identifying the suitable subset of features by applying either some domain knowledge or selection algorithm, (v) hyper parameter tuning for models to get best results (vi) developing a new hybrid model i.e. optimal feature selection based unsupervised random forest classifier (OFS-URFC), which gives best results in binary classification as well as multiclass classification and (vii) performance of this newly designed classifier is also evaluated using Spark streaming environment for processing the data in real time fashion. For this purpose, two-time series datasets are used in the experiment to compute the accuracy, recall, precision, and f1-score etc. The experimental analysis proves the suitability of the proposed approach as compared to the conventional classifiers and our newly constructed model achieved highest accuracy and reduced training complexity among all.

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

PP-Net: A Deep Learning Framework for PPG-Based Blood Pressure and Heart Rate Estimation

This paper presents a deep learning model ’PP-Net’ which is the first of its kind, having the capability to estimate the physiological parameters: Diastolic blood pressure (DBP), Systolic blood pressure (SBP), and Heart rate (HR) simultaneously from the same network using a single channel PPG signal. The proposed model is designed by exploiting the deep learning framework of Long-term Recurrent Convolutional Network (LRCN), exhibiting inherent ability of feature extraction, thereby, eliminating the cost effective steps of feature selection and extraction, making less-complex for deployment on resource constrained platforms such as mobile platforms. The performance demonstration of the PP-Net is done on a larger and publically available MIMIC-II database. We achieved an average NMAE of 0.09 (DBP) and 0.04 (SBP) mmHg for BP, and 0.046 bpm for HR estimation on total population of 1557 critically ill subjects. The accurate estimation of HR and BP on a larger population compared to the existing methods, demonstrated the effectiveness of our proposed deep learning framework. The accurate evaluation on a huge population with CVD complications, validates the robustness of the proposed framework in pervasive healthcare monitoring especially cardiac and stroke rehabilitation monitoring.

Printable elastic silver nanowire-based conductor for washable electronic textiles

Printable elastic conductors promote the wide application of consumable electronic textiles (e-textiles) for pervasive healthcare monitoring and wearable computation. To assure a clean appearance, the e-textiles require a washing process to clean up the dirt after daily use. Thus, it is crucial to develop low-cost printable elastic conductors with strong adhesion to the textiles. Here, we report a composite elastic conductor based on Ag nanowires (NWs) and polyurethane elastomer. The composite could be dispersed into ink and easily printed onto textiles. One-step print could form robust conductive coatings without sealing on the textiles. Interestingly, the regional concentration of Ag NWs within the polyurethane matrix was observed during phase inversion, endowing the elastic conductor with a low percolation threshold of 0.12 vol.% and high conductivity of 3,668 S·cm−1. Thanks to the high adhesion of the elastic conductors, the resulted e-textiles could withstand repeated stretching, folding, and machine washing (20 times) without obvious performance decay, which reveals its potential application in consumable e-textiles.

Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare.

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.

A zero power harmonic transponder sensor for ubiquitous wireless μL liquid-volume monitoring.

Autonomous liquid-volume monitoring is crucial in ubiquitous healthcare. However, conventional approach is based on either human visual observation or expensive detectors, which are costly for future pervasive monitoring. Here we introduce a novel approach based on passive harmonic transponder antenna sensor and frequency hopping spread spectrum (FHSS) pattern analysis, to provide a very low cost wireless μL-resolution liquid-volume monitoring without battery or digital circuits. In our conceptual demonstration, the harmonic transponder comprises of a passive nonlinear frequency multiplier connected to a metamaterial-inspired 3-D antenna designed to be highly sensitive to the liquid-volume within a confined region. The transponder first receives some FHSS signal from an interrogator, then converts such signal to its harmonic band and re-radiates through the antenna sensor. The harmonic signal is picked up by a sniffer receiver and decoded through pattern analysis of the high dimensional FHSS signal strength data. A robust, zero power, absolute accuracy wireless liquid-volume monitoring is realized in the presence of strong direct coupling, background scatters, distance variance as well as near-field human-body interference. The concepts of passive harmonic transponder sensor, metamaterial-inspired antenna sensor, and FHSS pattern analysis based sensor decoding may help establishing cost-effective, energy-efficient and intelligent wireless pervasive healthcare monitoring platforms.

Fuzzy Reasoning of Accident Provenance in Pervasive Healthcare Monitoring Systems

In pervasive healthcare monitoring environments, data provenance, as one metadata, can help people analyze the reasons for medical accidents that are generated by complex events. This reasoning processing often encounters inaccurate time and irreversible reasoning problems. How to solve the uncertain process and fuzzy transformation time presents many challenges to the study of data provenance. In this paper, we propose a backward derivation model with the provenance semantic, backward fuzzy time reasoning net (BFTRN), to solve these two problems. We design a backward reasoning algorithm motivated by time automation theory based on this model. With regard to given life-critical alarms and some constraints, it cannot only derive all evolution paths and the possibility distribution of paths from historical information, but also efficiently compute the value of fuzzy time function for each transition of lift-critical complex alarms in the healthcare monitoring system. We also analyze the properties of BFTRN model in this paper. Experiments on real dataset show that the proposed model is efficient.

Secured Framework for Pervasive Healthcare Monitoring Systems

‘Pervasive Healthcare Monitoring System (PHMS) ’ is one of the important pervasive computing applications aimed at providing healthcare services to all the people through mobile communication devices. Pervasive computing devices are resource constrained devices such as battery power, memory, processing power and bandwidth. In pervasive environment data privacy is a key issue. In this application a secured frame work is developed for receiving the patient’s medical data periodically, updates automatically in Patient Record Database and generates a Checkup Reminder. In the present work a light weight asymmetric algorithm proposed by the authors [26] is used for encrypting the data to ensure data confidentiality for its users. Challenge response onetime password mechanism is applied for authentication process

Beyond the second skin: an experimental approach to addressing garment style and fit variables in the design of sensing garments

Garment-integrated body sensing is a crucial element for many compelling ubiquitous computing and pervasive health care monitoring applications. However, the trade-off between garment comfort and sensor accuracy often poses a significant obstacle to successful application design. This article describes the development and implementation of a hybrid experimental approach for design and evaluation of a garment-integrated spinal posture sensor. The design variables of garment ease and style are assessed in two experimental evaluations, and the results of these tests are implemented as design parameters for a final prototype. The resulting prototype is evaluated on the initial performance criteria and demonstrates the requisite sensor accuracy in a garment that is more socially acceptable and usable than the original laboratory garment.

A Survey on Context-Aware Sensing for Body Sensor Networks

Context awareness in Body Sensor Networks (BSNs) has the significance of associating physiological user activity and the environment to the sensed signals of the user. The context information derived from a BSN can be used in pervasive healthcare monitoring for relating importance to events and specifically for accurate episode detection. In this paper, we address the issue of context-aware sensing in BSNs, and survey different techniques for deducing context awareness.

Pervasive Healthcare and Wireless Health Monitoring

With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, the wireless infrastructure can support many current and emerging healthcare applications. This could fulfill the vision of "Pervasive Healthcare" or healthcare to anyone, anytime, and anywhere by removing locational, time and other restraints while increasing both the coverage and the quality. In this paper, we present applications and requirements of pervasive healthcare, wireless networking solutions and several important research problems. The pervasive healthcare applications include pervasive health monitoring, intelligent emergency management system, pervasive health-care data access, and ubiquitous mobile telemedicine. One major application in pervasive healthcare, termed comprehensive health monitoring is presented in significant details using wireless networking solutions of wireless LANs, ad hoc wireless networks, and, cellular/GSM/3G infrastructure-oriented networks. Many interesting challenges of comprehensive wireless health monitoring, including context-awareness, reliability, and, autonomous and adaptable operation are also presented along with several high-level solutions. Several interesting research problems have been identified and presented for future research.