Genetic Algorithm-Based Human Mental Stress Detection and Alerting in Internet of Things
Hatem S. A. HamattaKakoli BanerjeeHarishchander AnandaramMohammad Shabbir AlamC. Anand Deva DuraiB. Parvathi DeviHemant PalivelaR. RajagopalAlazar Yeshitla
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Healthcare is one of the emerging application fields in the Internet of Things (IoT). Stress is a heightened psycho-physiological condition of the human that occurs in response to major objects or events. Stress factors are environmental elements that lead to stress. A person's emotional well-being can be negatively impacted by long-term exposure to several stresses affecting at the same time, which can cause chronic health issues. To avoid strain problems, it is vital to recognize them in their early stages, which can only be done through regular stress monitoring. Wearable gadgets offer constant and real information collecting, which aids in experiencing an increase. An investigation of stress discovery using detecting devices and deep learning-based is implemented in this work. This proposed work investigates stress detection techniques that are utilized with detecting hardware, for example, electroencephalography (EEG), photoplethysmography (PPG), and the Galvanic skin reaction (GSR) as well as in various conditions including traveling and learning. A genetic algorithm is utilized to separate the features, and the ECNN-LSTM is utilized to classify the given information by utilizing the DEAP dataset. Before that, preprocessing strategies are proposed for eliminating artifacts in the signal. Then, the stress that is beyond the threshold value is reached the emergency/alert state; in that case, an expert who predicts the mental stress sends the report to the patient/doctor through the Internet. Finally, the performance is evaluated and compared with the traditional approaches in terms of accuracy, f1-score, precision, and recall.Keywords:
Photoplethysmogram
Mental stress
Wearable Technology
Wearables today play a key role in E-Health computing, with investments expected to exceed $70 billion by 2024. With the massive use of apps on wearable devices, it is crucial to improve safety when using wearables, considering that important information about user information is stored on these devices. We present SOMEONE ensemble learning, a set machine learning algorithm for body recognition of wearable devices, which operates on the basis of both PhotoPlethysmoGram (PPG) and ElectroCardioGram (ECG) signals. We consider an individual's PPG and ECG signals, where algorithms process these signals stored on the wearable device to identify the user. The SOMEONE algorithm achieves better results on metrics such as F1 score, accuracy, false acceptance rate (FAR) and false rejection rate (FRR) for human recognition in MIMIC dataset of ECG signals and CapnoBase dataset of PPG signal.
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Abstract Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
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The rise of wearable devices being used into our daily life have been observed the disputes, when it is utilized by the clients for durable is quiet an issue with growth of Internet of Things (IOT). In this paper we reviewed the dissipates in ethical, social and environmental associated to wearable technologies from client point of views. Research has been pointed numerous issues in the ecological, social and ethical values are jointly investigated in this research work, shown in the area of wearable Internet of Things. This study mainly focus on dispute of IOT, which are found to be important in wearable technologies. The disputes which have been mentioned are significant effects for reducing the negative adaptation sprints of wearables in our daily basis.
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As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.
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Cardiovascular diseases (CVD) are among those with the highest mortality rates, and various wearable devices for continuous monitoring are emerging as a complement to medical procedures. Blood pressure (BP) monitoring in wearable devices, in order to be continuous, must be performed noninvasively, thus involving photoplethysmography (PPG), a technology that has been widely studied in recent years as a non-invasive solution for BP estimation. However, continuous data acquisition in a wearable system is still a challenge, one of the reasons being the noise caused by movement, the correct use of the PPG signal, and the estimation method to be used. This paper reviews the advances in blood pressure estimation based on photoplethysmography, focusing on the analysis of the preprocessing (ICA, FIR, adaptive filters) of the signals. Among the filters reviewed, the most suitable for dealing with Motion Artifacts (MA) of a wearable system are the adaptive filters, because conventional filters are limited to work only in the band for which they are designed, which does not always cover the spectrum of the MA. A review of the estimation methods is also carried out, among them machine learning stands out because it shows greater growth due to the new proposals that use more signals and obtain better results in terms of accuracy. The objective is to know and analyze the appropriate preprocessing filters and estimation methods from the perspective of wearable systems using PPG sensors affected by AM. Keywords— Blood Pressure Estimation, PAT, PTT, Machine Learning, Photoplethysmography, adaptive filtering.
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The physical and mental tension experience due to a challenge or a demand of a situation is known as stress. It is said that it is a normal human reaction as any other. Stress can help you detect a danger or meet a work deadline. Hence, stress can be positive but at the same time if an individual gets exposed to stress for a long period of time or multiple situations inducing stress simultaneously, it can affect their physical and mental health tremendously. To reduce health risks, one need to keep these stress levels low and controlled. To detect and monitor the stress levels there are many approaches and algorithms available. A multimodal dataset can be produced using wearable device like smart-watch. There are different noticeable shifts in human body if it is stressed and due to which various bio signals are created. This paper reviews the machine learning methods and algorithm which contribute in detecting stress levels and managing it for an individual using wearable sensors and devices.
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ABSTRACTThe advancement of the internet to the paradigm of the Internetof Things (IoT) has brought to society new ways of generating,sharing and using information. The evolution of computing capacityand energy savings in IoT equipament combined with bettersoftware can enabled several new applications, among which wecan highlight the monitoring of people’s health through pervasivedevices connected to the body. In view of this, this work proposesan algorithm to detect atypical situations such as falls in the elderlyand other groups that need health care using accelerometerscontained in wearable devices, particularly smartwatches. For theexperimental evaluation of the proposed algorithm, a database thatcontains data from wearable sensors, environmental sensors, andvisual devices was employed. The metrics used in the evaluationwere accuracy, precision, recall and f1-score, with recall being themost relevant metric in the context. Results show that the bestconfiguration of the algorithm is able to identify falls with 96%recall and F1-score of 90%.
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In this article, a technological solution is proposed to identify and reduce the level of mental stress of a person through a wearable device. The proposal identifies a physiological variable: heart rate, through the integration between a wearable and a mobile application through text recognition using the back camera of a smartphone. As part of the process, the technological solution shows a list of guidelines depending on the level of stress obtained in a given time. Once completed, it can be measured again in order to confirm the evolution of your stress level. This proposal allows the patient to keep his stress level under control in an effective and accessible way in real time. The proposal consists of four phases: 1. Collection of parameters through the wearable; 2. Data reception by the mobile application; 3. Data storage in a cloud environment and 4. Data collection and processing; this last phase is divided into 4 sub-phases: 4.1. Stress level analysis, 4.2. Recommendations to decrease the level obtained, 4.3. Comparison between measurements and 4.4. Measurement history per day. The proposal was validated in a workplace with people from 20 to 35 years old located in Lima, Peru. Preliminary results showed that 80% of patients managed to reduce their stress level with the proposed solution.
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