Different types of wearable sensors and health monitoring systems: A survey

Bhelkar, Vaibhavi; Shedge, D. K.

doi:10.1109/icatcct.2016.7911963

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…It is worth noting that only a few surveys have investigated the usage of LPWAN communication technologies in HCM applications, while its suitability and utilization is yet to be considered at the intersection of WBAN and HCM. For examples of the existing surveys that investigated the legacy short-range data communication technologies used in WBANs, see [5,6,7,8,9,10,11,12,13,14,15]. However, a review of LPWAN technologies for WBAN solutions based on HCM applications does not exist in the literature to the best of the authors’ knowledge.…”

Section: Related Workmentioning

confidence: 99%

A Survey on LPWAN Technologies in WBAN for Remote Health-Care Monitoring

Olatinwo

Abu‐Mahfouz

Hancke

2019

Sensors

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In ubiquitous health-care monitoring (HCM), wireless body area networks (WBANs) are envisioned as appealing solutions that may offer reliable methods for real-time monitoring of patients’ health conditions by employing the emerging communication technologies. This paper therefore focuses more on the state-of-the-art wireless communication systems that can be explored in the next-generation WBAN solutions for HCM. Also, this study addressed the critical issues confronted by the existing WBANs that are employed in HCM. Examples of such issues include wide-range health data communication constraint, health data delivery reliability concern, and energy efficiency, which are attributed to the limitations of the legacy short range, medium range, and the cellular technologies that are typically employed in WBAN systems. Since the WBAN sensor devices are usually configured with a finite battery power, they often get drained during prolonged operations. This phenomenon is technically exacerbated by the fact that the legacy communication systems, such as ZigBee, Bluetooth, 6LoWPAN, and so on, consume more energy during data communications. This unfortunate situation offers a scope for employing suitable communication systems identified in this study to improve the productivity of WBANs in HCM. For this to be achieved, the emerging communication systems such as the low-power wide-area networks (LPWANs) are investigated in this study based on their power transmission, data transmission rate, data reliability in the context of efficient data delivery, communication coverage, and latency, including their advantages, as well as disadvantages. As a consequence, the LPWAN solutions are presented for WBAN systems in remote HCM. Furthermore, this research work also points out future directions for the realization of the next-generation of WBANs, as well as how to improve the identified communication systems, to further enhance their productivity in WBAN solutions for HCM.

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Section: Related Workmentioning

confidence: 99%

A Survey on LPWAN Technologies in WBAN for Remote Health-Care Monitoring

Olatinwo

Abu‐Mahfouz

Hancke

2019

Sensors

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“…Commonly used sensors are environmental sensors [ 51 ], body sensors [ 52 ] and multimedia sensors [ 53 ]. Environmental sensors, which are generally small enough to be embedded in a bicycle, can extract temperature, humidity, the concentration of some hazard gases or particulate matter (e.g., SO

, CO

, PM2.5 and PM10) and some other parameters from environment [ 54 , 55 ].…”

Section: Key Technologies Of Iosbmentioning

confidence: 99%

“…Body sensors are designed to monitor human health parameters including body or skin temperature, oxygen saturation, ECG, respiration rate and so on [ 52 ]. With the development of microelectronic technology and the rise of Body Area Network (BAN) [ 57 , 58 ], Body sensors become smaller and more energy efficient, which is ideal for small embedded systems like IoSB.…”

Section: Key Technologies Of Iosbmentioning

confidence: 99%

The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey

Shen

Wei

Sun

et al. 2018

Sensors

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With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.

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“…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 ). However, their challenges ( Saboor et al, 2020 ; Liu and Schultz, 2019 ; Bhelkar and Shedge, 2016 ) concerning battery life and user acceptance underscore the importance of a balanced approach.…”

Section: Introductionmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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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.

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Different types of wearable sensors and health monitoring systems: A survey

Cited by 12 publications

References 16 publications

A Survey on LPWAN Technologies in WBAN for Remote Health-Care Monitoring

A Survey on LPWAN Technologies in WBAN for Remote Health-Care Monitoring

The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey

Robust human locomotion and localization activity recognition over multisensory

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