Systems and methods for automatic pain monitoring and assessment are described herein. In one example, the system may include a wearable facial expression capturing system that is placed over a subject's face. The system may be embedded with a plurality of sensors configured to detect biosignals from facial muscles and may additionally include a sensor node that recognizes facial expressions based on the detected biosignals. Pain experienced by the subject is assessed based on the facial expressions in conjunction with physiological signals obtained by other wearable sensors.
Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient's contextual information-including health and activity status-can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient's contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a run-time distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of decision making process over a certain threshold. Our optimization algorithm resides in the Fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy-efficiency with a bounded loss in accuracy.
Developments in technology have shifted the focus of medical practice from treating a disease to prevention. Currently, a significant enhancement in healthcare is expected to be achieved through the Internet of Things (IoT). There are various wearable IoT devices that track physiological signs and signals in the market already. These devices usually connect to the Internet directly or through a local smart phone or a gateway. Home-based and in hospital patients can be continuously monitored with wearable and implantable sensors and actuators. In most cases, these sensors and actuators are resource constrained to perform computing and operate for longer periods. The use of traditional gateways to connect to the Internet provides only connectivity and limited network services. With the introduction of the Fog computing layer, closer to the sensor network, data analytics and adaptive services can be realized in remote healthcare monitoring. This chapter focuses on a smart e-health gateway implementation for use in the Fog computing layer, connecting a network of such gateways, both in home and in hospital use. To show the application of the services, simple healthcare scenarios are presented. The features of the gateway in our Fog implementation are discussed and evaluated.
Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to measure vital signs (e.g., heart rate). The method is, however, highly susceptible to motion artifacts, which are inevitable in remote health monitoring. Noise reduces signal quality, leading to inaccurate decision-making. In addition, unreliable data collection and transmission waste a massive amount of energy on battery-powered devices. Studies in the literature have proposed PPG signal quality assessment (SQA) enabled by rule-based and machine learning (ML)-based methods. However, rule-based techniques were designed according to certain specifications, resulting in lower accuracy with unseen noise and artifacts. ML methods have mainly been developed to ensure high accuracy without considering execution time and device’s energy consumption. In this paper, we propose a lightweight and energy-efficient PPG SQA method enabled by a semi-supervised learning strategy for edge devices. We first extract a wide range of features from PPG and then select the best features in terms of accuracy and latency. Second, we train a one-class support vector machine model to classify PPG signals into “Reliable” and “Unreliable” classes. We evaluate the proposed method in terms of accuracy, execution time, and energy consumption on two embedded devices, in comparison to five state-of-the-art PPG SQA methods. The methods are assessed using a PPG dataset collected via smartwatches from 46 individuals in free-living conditions. The proposed method outperforms the other methods by achieving an accuracy of 0.97 and a false positive rate of 0.01. It also provides the lowest latency and energy consumption compared to other ML-based methods.
Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.
Internet of Things (IoT) paradigm raises challenges for devising efficient strategies that offload applications to the fog or the cloud layer while ensuring the optimal response time for a service. Traditional computation offload-ing policies assume the response time is only dominated by the execution time. However, the response time is a function of many factors including contextual parameters and application characteristics that can change over time. For the computation offloading problem, the majority of existing literature presents efficient solutions considering a limited number of parameters (e.g., computation capacity and network bandwidth) neglecting the effect of the application characteristics and dataflow configuration. In this paper, we explore the impact of the computation offloading on total application response time in three-layer IoT systems considering more realistic parameters, e.g., application characteristics, system complexity, communication cost, and dataflow configuration. This paper also highlights the impact of a new application characteristic parameter defined as Output-Input Data Generation (OIDG) ratio and dataflow configuration on the system behavior. In addition, we present a proof-of-concept end-to-end dynamic computation offloading technique, implemented in a real hardware setup, that observes the afore-mentioned parameters to perform real-time decision-making.
Recent advances in pervasive Internet of Things (IoT) technologies and edge computing have opened new avenues for development of ubiquitous health monitoring applications. Delivering an acceptable level of usability and accuracy for these healthcare IoT applications requires optimization of both system-driven and data-driven aspects, which are typically done in a disjoint manner. While decoupled optimization of these processes yields local optima at each level, synergistic coupling of the system and data levels can lead to a holistic solution opening new opportunities for optimization. In this paper, we present an edge-assisted resource manager that dynamically controls the fidelity and duration of sensing w.r.t. changes in the patient’s activity and health state, thus fine-tuning the trade-off between energy-efficiency and measurement accuracy. The cornerstone of our proposed solution is an intelligent low-latency real-time controller implemented at the edge layer that detects abnormalities in the patient’s condition and accordingly adjusts the sensing parameters of a reconfigurable wireless sensor node. We assess the efficiency of our proposed system via a case study of PPG-based medical Early Warning Score (EWS) system. Our experiments on a real full hardware-software EWS system reveal up to 49% power savings while maintaining the accuracy of the sensory data.
Healthcare applications supported by the Internet of Things (IoT) enable personalized monitoring of a patient in everyday settings. Such applications often consist of battery powered sensors coupled to smart gateways at the edge layer. Smart gateways offer several local computing and storage services (e.g., data aggregation, compression, local decision making, etc.), and also provide an opportunity for implementing local closed-loop optimization of different parameters of the sensor layer, in particular, energy consumption. To implement efficient optimization methods, information regarding the context and state of patients need to be considered to find opportunities to adjust energy to demanded accuracy. Edge-assisted optimization can manage energy consumption of the sensor layer, but may also adversely affect the quality of sensed data, which could compromise the reliable detection of health deterioration risk factors. In this paper, we propose two approaches, myopic and Markov Decision Processes (MDP) to consider both energy constraints and risk factor requirements for achieving a two-fold goal: energy savings while satisfying accuracy requirements of abnormality detection in a patient's vital signs. Vital signs, including heart rate, respiration rate and oxygen saturation SpO2, are extracted from a Photoplethysmogram (PPG) signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor's sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable PPG sensor in an IoT system, and show that compared to non-adaptive methods, myopic reduces an average of 16.9% in sensing energy consumption with the maximum probability of abnormality mis-detection on the order of 0.17 in a 24-hour health monitoring system. In addition, during four weeks of monitoring, we demonstrate that our MDP policy can extend the battery life on average of more than 2x while fulfilling the same average probability of misdetection comparing to myopic method. We illustrate results comparing, myopic, MDP and non-adaptive methods to monitor 14 subjects during one month.
Cardiovascular diseases are one of the world’s major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability - even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that - under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions) - our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.
Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.
The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life- or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient’s condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs).
Current developments in ICTs such as in Internet-of-Things (IoT) and Cyber–Physical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e., gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such as mobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our system on addressing a medical case study. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability.
The Internet of Things (IoT) is emerging with the pace of technology evolution, connecting people and things through the Internet. IoT devices enable large-scale data collection and sharing for a wide range of applications. However, it is challenging to securely manage interconnected IoT devices because the collected data could contain sensitive personal information. The authors believe that attribute-based encryption (ABE) could be an effective cryptographic tool for secure management of IoT devices. However, little research has addressed ABE's actual feasibility in the IoT thus far. This article investigates such feasibility considering well-known IoT platforms--specifically, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero. A thorough evaluation confirms that adopting ABE in the IoT is indeed feasible.
The selective laser sintering (SLS) is a rapid prototyping (RP) process which uses laser surface treatment to produce consolidation of powder materials. To obtain an efficient SLS, the optical parameters such as laser power, scanning velocity as well as the material properties must be optimized. In this paper, the SLS of biocomposite of Hydroxyapatite (HA) and polytetrafluoroethylene (PTFE) as secondary polymeric binder is investigated. Microstructural assessments of the samples were conducted using scanning electron microscopy (SEM). To study the effect of laser power on the strength of specimens, pressure test were carried out. Depth of sintering layer and its correlation with laser power numerically is explored. In our case, the best sintering condition was achieved at 3W and 1 mm/s.
Atrial fibrillation (AFib) is the most common heart arrhythmia in the world but detecting it can be challenging. For this reason, a detection system consisting of a wearable electrocardiogram (ECG) device, a smart phone application and an algorithm was created. The wearable device was designed to be aesthetically simple yet attractive and be worn either as a necklace or a keychain so that it would always be within reach. The overall usability was also a design goal from the start, requiring the user to only touch the device and start a measurement from the smartphone application with a press of a button. The recorded data was processed with an AFib detection algorithm created based on the Chapman university’s database with over 10,000 patients with different heart rhythms. The algorithm is a rule-based detection method, which uses heart rate variability and auto-correlation features. Motion artifacts were also taken into account by using an accelerometer signal measured with the device. The algorithm had an accuracy of 95.3% for the original database while all of the healthy volunteers (n = 14) tested with the developed system were correctly predicted to have sinus rhythms. The aim is to continue the study by increasing the test set size and to measure ECG with the device from AFib patients.
The purpose of this study is to attempt to identify acute myocardial infarction with high frequency serial electrocardiogram which both are ECG analyzing techniques. The idea is to combine these two techniques and see if changes between different ECGs from the same person can provide us with some information, whether it being in the high frequency or normal frequency range of ECG. A heart attack can occur at any time and therefore the possibility of using a wearable device was also researched. To answer the questions, an existing database which contained multiple ECGs for each person with high sampling frequency was used. On top of this, a new serial ECG database was gathered using a wearable device designed by the University of Turku. Using multiple ECGs, features were extracted from the signals and then used in different machine learning methods in order to classify the subjects. All of the methods seem to be relevant. High frequency ECG was found to be useful, while serial ECG provided us good results with both databases. The device was also found to be able to produce good quality ECG.
In the era of Fog computing where one can decide to compute certain time-critical tasks at the edge of the network, designers often encounter a question whether the sensor layer provides the optimal response time for a service, or the Fog layer, or their combination. In this context, minimizing the total response time using computation migration is a communication-computation co-optimization problem as the response time does not depend only on the computational capacity of each side. In this paper, we aim at investigating this question and addressing it in certain situations. We formulate this question as a static or dynamic computation migration problem depending on whether certain communication and computation characteristics of the underlying system is known at design-time or not. We first propose a static approach to find the optimal computation migration strategy using models known at design-time. We then make a more realistic assumption that several sources of variation can affect the system's response latency (e.g., the change in computation time, bandwidth, transmission channel reliability, etc.), and propose a dynamic computation migration approach which can adaptively identify the latency optimal computation layer at runtime. We evaluate our solution using a case-study of artificial neural network based arrhythmia classification using a simulation environment as well as a real test-bed.
Smartphones and wearable devices such as smartwatches are realized as mobile gateways and sensor nodes in IoT applications, respectively. In conventional IoT systems, wearable devices are responsible for gathering and transmitting data to mobile gateways where most of the computation is performed. However, the improvement of wearable devices, in recent years, have decreased the gap in terms of computation capability with mobile gateways. Therefore, some recent works presented offloading schemes for utilizing wearable devices capability and reducing the burden of mobile gateways for specific applications. However, a comprehensive study of offloading methods at wearable devices has not been conducted in a large number of applications. In this paper, 9 applications of the LOCUS's benchmark, therefore, have been utilized and tested on different boards having hardware specification close to wearable devices and mobile gateways. Via the results of execution time and energy consumption of the boards for running 9 applications, the paper provides some useful hints for a system administrator when designing and choosing a suitable computation method for IoT systems to achieve a high quality of service (QoS). The results show that depending on the applications, offloading methods can be used for achieving some levels of energy efficiency. In addition, the paper compares the energy consumption of a mobile gateway when running the applications in both serial and multi-threading manners.
Management of energy dissipation and battery life is a challenge in health monitoring wearables. Low-quality data collection, non-reliable monitoring process, and missing important health events are consequences of single-goal fixed-policy solutions. In this research, energy dissipation of IoT-based wearable systems is managed through a dynamic multi-goal approach. Health status of the user of a wearable device, the continuity of monitoring, and the accuracy of collected data are parameters we consider in our goal hierarchy to select a proper system management policy at run-time to achieve the most significant goal at a given time. In our approach, a dynamic observation process assesses the user and system data and a fog-assisted control engine detects the states, enforces the proper policy, and reconfigures the wearable sensor. To demonstrate our solution, we develop a real reconfigurable wireless sensor node with an ability to follow a set of parametrically defined policies and performed a set of experiments to find the most efficient setting. Our evaluation shows that the proposed system is able to reduce the power consumption by 44% and prevent the data loss due to battery shortage in 0.78% of total data collection time compared to a baseline system without a goal manager.
The Internet of Things is a key enabler of mobile health-care applications. However, the inherent constraints of mobile devices, such as limited availability of energy, can impair their ability to produce accurate data and, in turn, degrade the output of algorithms processing them in real-time to evaluate the patient’s state. This paper presents an edge-assisted framework, where models and control generated by an edge server inform the sensing parameters of mobile sensors. The objective is to maximize the probability that anomalies in the collected signals are detected over extensive periods of time under batteryimposed constraints. Although the proposed concept is general, the control framework is made specific to a use-case where vital signs – heart rate, respiration rate and oxygen saturation – are extracted from a Photoplethysmogram (PPG) signal to detect anomalies in real-time. Experimental results show a 16.9% reduction in sensing energy consumption in comparison to a constant energy consumption with the maximum misdetection probability of 0.17 in a 24-hour health monitoring system.
Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations at risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.
Excessive stress during pregnancy could cause adverse effects for the mother and her unborn baby, disrupting the normal maternal adaptation throughout pregnancy. Such conditions could be tackled to some degree via traditional clinical techniques, although an automated healthcare system is required for providing a continuous stress management system. Internet of Things (IoT) systems are promising alternatives for such real-time stress monitoring. In conventional IoT-based stress monitoring, stress-related data is collected, and the stress level is determined using a pre-defined model. However, these systems are insufficient for pregnant women whose physiological data are changing over the course of their pregnancy. Therefore, an adaptive monitoring system is needed to estimate stress levels, considering the maternal adaptation such as heart rate elevation in pregnancy. In this paper, we propose a stress-level estimation algorithm based on heart rate and heart rate variations during pregnancy. The algorithm is distributed in an edge-enabled IoT system. We test the performance of our algorithm using supervised and unsupervised learning via an unlabelled set of data from a 7-month monitoring. The monitoring was fulfilled for 20 pregnant women using wearable smart wristbands. Our results show a 97.9% accuracy with 10-fold cross validation using Random Forests.
As the Internet of Things (IoT) penetrates ever more application domains, many IoT-based systems are increasingly becoming more complex, versatile and resource-rich, and need to serve one or more applications with diverse and changing goals. These systems face new challenges in dynamic goal management due to a combination of limited shared resources, and multiple goals that may not only conflict with each other, but which may also change dynamically. We motivate the need for hierarchical, dynamic goal management for this class of complex IoT systems and substantiate our arguments with case studies from two application domains: patient health monitoring and Cyber-Physical Production Systems (CPPSs).
Early Warning Score (EWS) systems are a common practice in hospitals. Health-care professionals use them to measure and predict amelioration or deterioration of patients’ health status. However, it is desired to monitor EWS of many patients in everyday settings and outside the hospitals as well. For portable EWS devices, which monitor patients outside a hospital, it is important to have an acceptable level of reliability. In an earlier work, we presented a self-aware modified EWS system that adaptively corrects the EWS in the case of faulty or noisy input data. In this paper, we propose an enhancement of such data reliability validation through deploying a hierarchical agent-based system that classifies data reliability but using Fuzzy logic instead of conventional Boolean values. In our experiments, we demonstrate how our reliability enhancement method can offer a more accurate and more robust EWS monitoring system.
Physical indicators are directly related with health and fitness of human body. By employing real-time e-health monitoring systems for acquiring, and analyzing bio-signals by measurements such as electrocardiogram (ECG) and electromyography (EMG), it is possible to extract information to achieve better health-care in terms of observation, diagnosis, and treatment. However, those systems are limited in acquiring and sending data at high rates, are not energy efficient, or, are restricted in terms of portability due to large size and weight. In this paper, a compact portable bio-signal acquisition device for wearables has been designed and implemented. The developed hardware is capable of acquiring and reliably sending the data wirelessly at a high transfer rate in real-time while keeping the overall energy consumption low. Finally, the signal acquisition performance of the device has been evaluated for both ECG and EMG at 8 channel 24 bit resolution/channel 500 samples/s configuration. Measurement of energy consumption has been conducted using professional tool and it is found that the device can continuously work for up to 13.6 hours with a 3.7V 1700 mAh battery. In addition, the device has been used in an IoT-based system as an example of possible integration.
In healthcare, effective monitoring of patients plays a key role in detecting health deterioration early enough. Many signs of deterioration exist as early as 24 hours prior having a serious impact on the health of a person. As hospitalization times have to be minimized, in-home or remote early warning systems can fill the gap by allowing in-home care while having the potentially problematic conditions and their signs under surveillance and control. This work presents a remote monitoring and diagnostic system that provides a holistic perspective of patients and their health conditions. We discuss how the concept of self-awareness can be used in various parts of the system such as information collection through wearable sensors, confidence assessment of the sensory data, the knowledge base of the patient's health situation, and automation of reasoning about the health situation. Our approach to self-awareness provides (i) situation awareness to consider the impact of variations such as sleeping, walking, running, and resting, (ii) system personalization by reflecting parameters such as age, body mass index, and gender, and (iii) the attention property of self-awareness to improve the energy efficiency and dependability of the system via adjusting the priorities of the sensory data collection. We evaluate the proposed method using a full system demonstration.
Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current IoT-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new IoT-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in ECG signal.
In hospital, a patient often has a stationary position and predictable daily activities in a standard environment. Conversely, such a reliability cannot be easily achieved in non-clinical situations due to the susceptibility of vital signs to situation variations. Therefore, an adaptive system is required to behave autonomously with respect to the changes in patient’s activities and the situations. In this work, we introduce a self-aware EWS system to provide patients with a personalized remote monitoring system. We also propose an adaptive solution inspired by where we also consider ”Attention” as a beneficial feature to improve the energy efficiency, sensitivity, and specificity of our approach by adjusting the priorities of sensory data.
Early Warning Score (EWS) system is specified to detect and predict patient deterioration in hospitals. This is achievable via monitoring patient’s vital signs continuously and is often manually done with paper and pen. However, because of the constraints in healthcare resources and the high hospital costs, the patient might not be hospitalized for the whole period of the treatments, which has lead to a demand for in-home or portable EWS systems. Such a personalized EWS system needs to monitor the patient at anytime and anywhere even when the patient is carrying out daily activities. In this paper, we propose a self-aware EWS system which is the reinforced version of the existing EWS systems by using the Internet of Things technologies and the self-awareness concept. Our self-aware approach provides (i) system adaptivity with respect to various situations and (ii) system personalization by paying attention to critical parameters. We evaluate the proposed EWS system using a full system demonstration.
Biopotentials including Electrocardiography (ECG), Electromyography (EMG) and Electroencephalography (EEG) measure the activity of heart, muscles and brain, respectively. They can be used for noninvasive diagnostic applications, assistance in rehabilitation medicine and human-computer interaction. The concept of Internet of Things (IoT) can bring added value to applications with biopotential signals in healthcare and human-computer interaction by integrating multiple technologies such as sensors, wireless communication and data science. In this work, we present a wireless biopotentials remote monitoring and processing system. A prototype with the case study of facial expression recognition using four channel facial sEMG signals is implemented. A multivariate Gaussian classifier is trained off-line from one person's surface EMG (sEMG) signals with four facial expressions: neutral, smile, frown and wrinkle nose. The presented IoT application system is implemented on the basis of an eight channel biopotential measurement device, Wi-Fi module as well as signal processing and classification provided as a Cloud service. In the system, the real-time sEMG data stream is filtered, feature extracted and classified within each data segment and the processed data is visualized in a browser remotely together with the classification result.
Early warning score (EWS) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing EWS systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.
Early warning score (EWS) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, EWS procedure is mostly used for in-hospital clinical cases and is performed in a manual paper-based fashion. In this paper, we propose an automated EWS health monitoring system to intelligently monitor vital signs and prevent health deterioration for in-home patients using Internet-of-Things (IoT) technologies. IoT enables our solution to provide a real-time 24/7 service for health professionals to remotely monitor in-home patients via Internet and receive notifications in case of emergency. We also demonstrate a proof-of-concept EWS system where continuous reading, transferring, recording, and processing of vital signs have been implemented.