Data Analysis Methods for Health Monitoring Sensors: A survey

Sobhan, Shahriar; Islam, Saiful; Valero, Maria; Shahriar, Hossain; Ahamed, Sheikh Iqbal

doi:10.1109/compsac51774.2021.00097

Cited by 5 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advancement of sensing technologies ( Jiang and He, 2020 ; Zheng et al, 2023a ), notably has catalyzed progress in human activity recognition (HAR). These sensors, pivotal in health ( Sobhan et al, 2021 ; Hussain et al, 2022 ; Zheng et al, 2023b ) and safety monitoring ( Reddy et al, 2016 ; Mao et al, 2022a ) in smart environments ( Guo et al, 2022 ; Jiawei et al, 2022 ; Liu et al, 2023g ), aim to be both accurate and nonintrusive. Wearable sensors ( Saboor et al, 2020 ; Bhelkar and Shedge, 2016 ; Perez and Zeadally, 2021 ) with their potential to capture granular movement data, have introduced new possibilities in HAR ( Liu et al, 2023a ).…”

Section: Introductionmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…Another study in [ 26 ] discusses the advantages of blockchain for combating the Covid-19 pandemic. Authors in [ 27 ] review various data analysis methods for health monitoring sensors. They have classified the related research works based on the types of sensors used, i.e., contactless and contact sensors.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

Krishna

Kumar

Kushwaha

2023

Wireless Pers Commun

View full text Add to dashboard Cite

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients’ health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients’ vitals and storing them securely have risen more than ever. Analyzing the stored patients’ data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients’ data, and processing/analyzing the stored patients’ data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

show abstract

“…The authors in [18] present a comprehensive survey on machine learning-based big data analytics for IoT smart healthcare systems to overcome the challenges of the traditional healthcare system. Sobhan et al [19] also surveyed machine learning techniques integrated with various sensors and their technologies to monitor patients remotely if they are suffering from heart or breathing-related diseases. Now, most researchers have presented architecture to overcome the security and privacy issues of the smart healthcare system for remote patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

et al. 2022

View full text Add to dashboard Cite

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

show abstract

Data Analysis Methods for Health Monitoring Sensors: A survey

Cited by 5 publications

References 31 publications

Robust human locomotion and localization activity recognition over multisensory

Robust human locomotion and localization activity recognition over multisensory

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

Contact Info

Product

Resources

About