Cardiac monitoring of frail oncological outpatient using wearable devices

2017 ◽  
Author(s):  
Mario Magliulo ◽  
Adriano Tramontano
Keyword(s):  
Wearable Devices ◽  
Cardiac Monitoring

scholarly journals Wearable Devices for Ambulatory Cardiac Monitoring

2020 ◽  
Vol 75 (13) ◽  
pp. 1582-1592 ◽  
Author(s):  
Furrukh Sana ◽  
Eric M. Isselbacher ◽  
Jagmeet P. Singh ◽  
E. Kevin Heist ◽  
Bhupesh Pathik ◽  
...  
Keyword(s):  
Wearable Devices ◽  
Cardiac Monitoring

scholarly journals Implementation of Compressed Sensing in Telecardiology Sensor Networks

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Eduardo Correia Pinheiro ◽  
Octavian Adrian Postolache ◽  
Pedro Silva Girão
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Energy Management ◽  
Wearable Devices ◽  
Wireless Sensor ◽  
Cardiac Monitoring ◽  
Signal Quality ◽  
Wavelet Transformations

Mobile solutions for patient cardiac monitoring are viewed with growing interest, and improvements on current implementations are frequently reported, with wireless, and in particular, wearable devices promising to achieve ubiquity. However, due to unavoidable power consumption limitations, the amount of data acquired, processed, and transmitted needs to be diminished, which is counterproductive, regarding the quality of the information produced. Compressed sensing implementation in wireless sensor networks (WSNs) promises to bring gains not only in power savings to the devices, but also with minor impact in signal quality. Several cardiac signals have a sparse representation in some wavelet transformations. The compressed sensing paradigm states that signals can be recovered from a few projections into another basis, incoherent with the first. This paper evaluates the compressed sensing paradigm impact in a cardiac monitoring WSN, discussing the implications in data reliability, energy management, and the improvements accomplished by in-network processing.

Increased Preference of Wearable Devices Compared with Conventional Cardiac Monitoring

2018 ◽  
Vol 27 ◽  
pp. S170 ◽  
Author(s):  
K. Rajakariar ◽  
A. Koshy ◽  
J. Sajeev ◽  
L. Roberts ◽  
A. Teh
Keyword(s):  
Wearable Devices ◽  
Cardiac Monitoring

Smart technology-assisted Home Nursing Care Program for Persons with Dementia or Hip Fracture: Implementation Difficulties: Thematic Analysis (Preprint)

2020 ◽  
Author(s):  
Yea-Ing Shyu ◽  
Chung-Chih Lin ◽  
Ching-Tzu Yang ◽  
Pei-Ling Su ◽  
Jung-Ling Hsu
Keyword(s):  
Hip Fracture ◽  
Family Caregivers ◽  
Nursing Care ◽  
Care Program ◽  
Wearable Devices ◽  
Elderly Persons ◽  
Home Nursing ◽  
Smart Technology ◽  
Smart Clothing ◽  
Home Nursing Care

BACKGROUND Wearable devices have been developed and implemented to improve data collection in remote health care and smart care. Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. In this study, we developed and implemented a smart-care system using smart clothing for persons with dementia and with hip fracture. We conducted a preliminary study to understand family caregivers’ and care receivers’ experiences of receiving a smart technology-assisted (STA) home-nursing care program. OBJECTIVE This paper reports the difficulties we encountered and strategies we developed during the feasibility phase of studies on the effectiveness of our STA home-nursing care program for persons with dementia and hip fracture. METHODS Our care model, a STA home-nursing care program for persons with dementia and those with hip fracture included a remote-monitoring system for elderly persons wearing smart clothing was used to facilitate family caregivers’ detection of elderly persons’ movements. These movements included getting up at night, staying in the bathroom for more than 30 minutes, not moving more than 2 hours during the day, leaving the house, and daily activities. Participants included 13 families with 5 patients with hip fracture and 7 with dementia. Research nurses documented the difficulties they encountered during the process. RESULTS Difficulties encountered in this smart-care study were categorized into problems setting up the smart-care environment, problems running the system, and problems with participant acceptance/adherence. These difficulties caused participants to drop out, the system to not function or delayed function, inability to collect data, extra costs of manpower, and financial burden. Strategies to deal with these problems are also reported. CONCLUSIONS During the implementation of smart care at home for persons with dementia or hip fracture, different aspects of difficulties were found and strategies were taken. The findings of this study can provide a reference for future implementation of similar smart-home devices.

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices

2021 ◽  
Vol 141 (2) ◽  
pp. 89-96
Author(s):  
Hsin-Yen Yen ◽  
Hao-Yun Huang
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Short Form ◽  
Wearable Devices ◽  
Wearable Device ◽  
World Health ◽  
World Health Organization Quality ◽  
Quality Of Life Scale

Aims: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. Methods: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire–Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. Results: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. Conclusion: Wearable devices inspire users’ motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

Sign in / Sign up

Export Citation Format

Share Document