Application of High-Level Fuzzy Petri Nets to fall detection system using smartphone

Shen, Victor R.L.; Lai, Horng-Yih; Lai, Ah-Fur

doi:10.1109/icmlc.2013.6890807

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The accuracy of the systems fluctuated a lot based on the technology used in the systems as well as the position of the device. For TBA based systems, whereas the system employing tri-axial accelerometers [26] achieved 80% accuracy score, the wrist-bound accelerometer based system [31] achieved 99.38% accuracy score. ML based systems performed better for specific positions but not for every position.…”

Section: Discussion and Recommendation For Future Workmentioning

confidence: 97%

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A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam¹,

Neom²,

Imtiaz³

et al. 2019

ISI

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Accidental falls have become one of the most frequent general health issues in recent years due to the rate of occurrence. Individuals aged above 65 are more prone to accidental falls. Accidental falls result in severe injuries such as concussion, head trauma, physical disabilities even to deaths in serious cases if the patients are not rescued in time. Thus, researchers are focusing on developing fall detection systems that facilitate the detection and quick rescue of fall victims. The smartphone-based fall detection systems use various built-in sensors of smartphones mostly Tri-axial accelerometer, magnetometer, gyroscope, and camera. The majority of the systems employ threshold based algorithms (TBA). Some systems use machine learning (ML) based algorithms or a combination of ML and TBA based algorithms to detect falls. Each of these types of systems has its trade-offs. The goal of this paper is to review fall detection systems based on data from smartphone sensors that employ either one of TBA, ML or combination of both. We also present the taxonomy based on systematic comparisons of existing studies for smartphone-based fall detection solutions.

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Section: Discussion and Recommendation For Future Workmentioning

confidence: 97%

“…Victor et al [26] proposed a fall detection system where the users were required to place a smartphone in their thigh pocket. The signal produced by the tri-axial accelerometer sensor was stored and converted into signal vector magnitudes to detect falls.…”

Section: Threshold Based (Tba) Fall Detection Systemsmentioning

confidence: 99%

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam¹,

Neom²,

Imtiaz³

et al. 2019

ISI

View full text Add to dashboard Cite

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Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This paper provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

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Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

et al. 2021

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Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

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Application of High-Level Fuzzy Petri Nets to fall detection system using smartphone

Cited by 4 publications

References 9 publications

A Review on Fall Detection Systems Using Data from Smartphone Sensors

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Deep Learning Based Systems Developed for Fall Detection: A Review

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

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