HealthWare telemedicine technology (HWTT) evolution map for healthcare

Pawar, Mohandas; Pawar, Pranav M.; Pawar, Asha M.

doi:10.1016/b978-0-323-85854-0.00005-8

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Health is a crucial priority [ 33 ], despite being the top revenue earner for numerous countries. Each country is exploring innovative practices to deliver quality healthcare [ 34 , 35 ]. The use of technology, particularly the internet, is essential to manage solutions effectively and efficiently.…”

Section: Related Workmentioning

confidence: 99%

IoT and Deep Learning-Based Farmer Safety System

Adhitya

Mulyani

Koeppen

et al. 2023

Sensors

View full text Add to dashboard Cite

Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, receive training, and work in a safe environment. With the wearable device as an Internet of Things (IoT) subsystem, the device can read sensor data as well as compute and send information. We investigated the validation and simulation dataset to determine whether accidents occurred with farmers by applying the Hierarchical Temporal Memory (HTM) classifier with each dataset input from the quaternion feature that represents 3D rotation. The performance metrics analysis showed a significant 88.00% accuracy, precision of 0.99, recall of 0.04, F_Score of 0.09, average Mean Square Error (MSE) of 5.10, Mean Absolute Error (MAE) of 0.19, and a Root Mean Squared Error (RMSE) of 1.51 for the validation dataset, 54.00% accuracy, precision of 0.97, recall of 0.50, F_Score of 0.66, MSE = 0.06, MAE = 3.24, and = 1.51 for the Farming-Pack motion capture (mocap) dataset. The computational framework with wearable device technology connected to ubiquitous systems, as well as statistical results, demonstrate that our proposed method is feasible and effective in solving the problem’s constraints in a time series dataset that is acceptable and usable in a real rural farming environment for optimal solutions.

show abstract

Section: Related Workmentioning

confidence: 99%

IoT and Deep Learning-Based Farmer Safety System

Adhitya

Mulyani

Koeppen

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Wearable appliance demand might reach 27 billion dollars by 2022. This expansion creates enormous opportunities for disruption and innovation, both in the healthcare market and elsewhere [40]. People can utilize type of tracking capabilities to encourage the adoption of healthy habits such as physical activity, a balanced diet, and obesity prevention [39,41].…”

Section: Mhealth Apps In Public Health and Health Promotionmentioning

confidence: 99%

Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak

El‐Sherif

Abouzid

2022

Global Health

View full text Add to dashboard Cite

Background Mobile health applications (mHealth apps) offer enormous promise for illness monitoring and treatment to improve the provided medical care and promote health and wellbeing. Objective We applied bibliometric quantitative analysis and network visualization to highlight research trends and areas of particular interest. We expect by summarizing the trends in mHealth app research, our work will serve as a roadmap for future investigations. Methods Relevant English publications were extracted from the Scopus database. VOSviewer (version 1.6.17) was used to build coauthorship networks of authors, countries, and the co-occurrence networks of author keywords. Results We analyzed 550 published articles on mHealth apps from 2020 to February 1, 2021. The yearly publications increased from 130 to 390 in 2021. JMIR mHealth and uHealth (33/550, 6.0%), J. Med. Internet Res. (27/550, 4.9%), JMIR Res. Protoc. (22/550, 4.0%) were the widest journals for these publications. The United States has the largest number of publications (143/550, 26.0%), and England ranks second (96/550, 17.5%). The top three productive authors were: Giansanti D., Samuel G., Lucivero F., and Zhang L. Frequent authors’ keywords have formed major 4 clusters representing the hot topics in the field: (1) artificial intelligence and telehealthcare; (2) digital contact tracing apps, privacy and security concerns; (3) mHealth apps and mental health; (4) mHealth apps in public health and health promotion. Conclusions mHealth apps undergo current developments, and they remain hot topics in COVID-19. These findings might be useful in determining future perspectives to improve infectious disease control and present innovative solutions for healthcare.

show abstract

HealthWare telemedicine technology (HWTT) evolution map for healthcare

Cited by 2 publications

References 10 publications

IoT and Deep Learning-Based Farmer Safety System

IoT and Deep Learning-Based Farmer Safety System

Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak

Contact Info

Product

Resources

About