Wearable Patient Home Monitoring Based on ECG and ACC Sensors

Smoleń, Magdalena; Kańtoch, Eliasz; Augustyniak, Piotr; Kowalski, Paweł

doi:10.1007/978-3-642-23508-5_244

Cited by 17 publications

(10 citation statements)

References 3 publications

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“…This can be achieved by integrating the system into clothing what opens new possibilities in delivering healthcare services. Based on our experiments performed in home conditions and automatic analysis of biomedical signals, the evaluation of the monitoring of the human body is possible [5,6,7,8,11]. Furthermore, the following key findings were formulated:…”

Section: Discussionmentioning

confidence: 99%

Wearable Mobile Network as an Integrated Part of Assisted Living Technologies

Kańtoch

Augustyniak

2012

Information Technologies in Biomedicine

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Abstract. Advanced developments in existing information technologies are extending the range of treatment and health services available at patient's home. Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into clothing what opens a wide range of possibilities for health monitoring. This paper discusses existing solution in field of intelligent clothing and examines technical possibilities of integrating physiological sensors into clothing. It describes developments and trends all over the world in the areas of mobile sensors, telemedicine and smart fabrics. Finally, this paper presents the design of a health monitoring system based on sensors included in clothes.

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Section: Discussionmentioning

confidence: 99%

Wearable Mobile Network as an Integrated Part of Assisted Living Technologies

Kańtoch

Augustyniak

2012

Information Technologies in Biomedicine

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“…Various research initiatives proposed solutions that integrate wearable devices within an ECG monitoring system [14,21,22,29,30,[125][126][127]147]. These solutions are either integrated within an ambulatory setup, home environment, or patient/user setup and are used for the monitoring of various vital signs.…”

Section: Monitoring Devicesmentioning

confidence: 99%

“…Nowadays, ECG monitoring systems are used in hospitals [14][15][16][17], homes [18][19][20], outpatient ambulatory settings [21][22][23], and in remote contexts [24]. They also employ a wide range of technologies such as IoT [25][26][27], edge computing [28,29], and mobile computing [30][31][32]. In addition, they implement various computational settings in terms of processing frequencies, as well as monitoring schemes.…”

Section: Introductionmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

211

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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“…State-of-the-art techniques on time-dependent biomedical signal analysis significantly exhibit their successful progress both on robust sensor architecture and elegant statistical learning-based approaches [1,2]. Prospective experimental schemes and research in recent years has focused on the interpretations between physiological and functional expressions in terms of biomedical signals from heart rate variations, brainwave monitoring, respiration rates, and so forth [3][4][5][6]. Additionally, several methods of rehabilitation based on statistical learnings have also fulfilled fruitful performances for the recovery of human activities damaged by injury or known diseases, such as heart failure [7], stroke [8][9][10][11], Parkinson's disease [12] and osteoporosis [13].…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Human Postures Using the Data Density Functional Method

Huang

Chen

2018

Applied Sciences

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In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring.

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Wearable Patient Home Monitoring Based on ECG and ACC Sensors

Cited by 17 publications

References 3 publications

Wearable Mobile Network as an Integrated Part of Assisted Living Technologies

Wearable Mobile Network as an Integrated Part of Assisted Living Technologies

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Pattern Recognition of Human Postures Using the Data Density Functional Method

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