A Novel Approach for Smart and Cost Effective IoT Based Elderly Fall Detection System Using Pi Camera

Waheed, Shaikh Abdul; Khader, P. Sheik Abdul

doi:10.1109/iccic.2017.8524486

Cited by 31 publications

(14 citation statements)

References 4 publications

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“…Current studies on vision-based systems use suitable video cameras for real-time monitoring. Usually, these systems use a depth camera [51] or RGB camera such as Raspberry Pi camera [52] and indoor video surveillance camera [53] for image acquisition to detect falls. Depth cameras have the ability to calculate 3D information using a single camera.…”

Section: Systems Based On Image Processingmentioning

confidence: 99%

“…Hence adopted by most of the real-time vision system. For example, the human subject monitored by Raspberry Pi [52] and home surveillance camera [53] is approximated by an ellipse around them and a minimal rectangle is encompassed around the ellipse. The aspect ratio of this rectangle is observed in each frame and compared with a threshold to detect a fall.…”

Section: Systems Based On Image Processingmentioning

confidence: 99%

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Possible Life Saver: A Review on Human Fall Detection Technology

et al. 2020

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Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

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Section: Systems Based On Image Processingmentioning

confidence: 99%

Section: Systems Based On Image Processingmentioning

confidence: 99%

Possible Life Saver: A Review on Human Fall Detection Technology

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In [12], an outdoor vison system for real-time dynamic object tracking and classification is proposed to improve the public security surveillance, handling correctly moving objects with occlusions. A low-cost and high accuracy smart video solution for elderly fall detection is presented in [13]. This paper proposes the use of a Pi Camera mounted on a Raspberry Pi as a low-cost solution.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2019

JUSPN

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This paper presents a new proposal of a low-cost and low-power wireless smart camera device, designed to be used as an inexpensive smart image acquisition edge node for Internet of Things (IoT). Taking the advantages of the ARM Cortex-M7 microcontrollers, a high-performance STM32F7 microcontroller is used as a main digital signal processor (DSP) to acquire and transfer images at full rate, keeping the core idle to do real-time image processing. In order to design a very compact, low-cost and low-power wireless image sensor node, the DSP is supported by an embedded 1.3 Mega Pixel CMOS camera sensor and a low consumption 2.4GHz Wi-Fi module. This setup allows a custom build-in image processing algorithm for specific IoT sensing applications or a simply setup as low-cost streaming Motion JPEG (MJPEG) wireless camera node. In this work, both configurations have been tested and analyzed. Results show that the device can acquire and process images simultaneously at a full rate (30 fps) and the MJPEG transmission reaches 7.23 fps with a QVGA resolution.

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“…ESAEs-OCCCH is first adopted for unsupervised feature extraction to overcome the disadvantages of artificial feature extraction. Yacchirema et al [ 13 ] propose an innovative IoT (Internet of Thing) based online system for detecting falls of the aged. Sensory readings are processed and analyzed using a decision tree based Big Data model running on a Smart IoT Gateway [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction

Cai

Guo

Zhang

et al. 2020

Journal of Healthcare Engineering

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Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.

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A Novel Approach for Smart and Cost Effective IoT Based Elderly Fall Detection System Using Pi Camera

Cited by 31 publications

References 4 publications

Possible Life Saver: A Review on Human Fall Detection Technology

Possible Life Saver: A Review on Human Fall Detection Technology

Untitled

GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction

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