Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches

Nizam, Yoosuf; Jamil, Muhammad Mahadi Abdul

doi:10.1007/978-3-030-38748-8_7

Cited by 9 publications

(1 citation statement)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parameters such as blood pressure, pulse rate, and temperature can be tracked easily and conjointly paving the way for healthcare applications which focus on intelligent and wearable sensors. In context data acquisition by wearable sensors, automatic fall detection has recently attracted the scientific community's attention [107]. As mentioned in section A above, data processing on-board is more practical to reduce data breaching compared to sending data to the cloud server for processing.…”

Section: E Ethical Testingmentioning

confidence: 99%

Fall Detection System using Wearable Sensor Devices and Machine Learning: A Review

Amir,

Dziyauddin,

Mohamed

et al. 2024

Preprint

View full text Add to dashboard Cite

Over the past years, Fall Detection System (FDS) has undergone extensive research to improve living risk, especially for the elderly who are vulnerable to these fall events. Devices employing sensors are crucial components of FDS in achieving high accuracy and sensitivity. This article overviews different sensor modalities, such as ambient-based and vision-based systems, as well as commonly used wearable devices for fall detection, along with the associated data processing algorithms. The critical elements of fall detection, such as architectures and algorithms for processing sensor data, machine learning and deep learning methodologies, and validation of FDS performance, are considered. The article also delves into safety aspects and presents technical challenges and concerns in FDS for researchers in the field to identify areas requiring further improvement. Finally, future research opportunities to improve fall detection for widespread use are outlined.

show abstract

Section: E Ethical Testingmentioning

confidence: 99%

Fall Detection System using Wearable Sensor Devices and Machine Learning: A Review

Amir,

Dziyauddin,

Mohamed

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review

Gaya-Morey,

Manresa-Yee,

Buades-Rubio

2024

Appl Intell

View full text Add to dashboard Cite

As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies. Graphical abstract

show abstract

Semantic segmentation-based system for fall detection and post-fall posture classification

Mobsite

Alaoui

Boulmalf

et al. 2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches

Cited by 9 publications

References 111 publications

Fall Detection System using Wearable Sensor Devices and Machine Learning: A Review

Fall Detection System using Wearable Sensor Devices and Machine Learning: A Review

Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review

Semantic segmentation-based system for fall detection and post-fall posture classification

Contact Info

Product

Resources

About