Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

Alaoui, Abdessamad Youssfi; Fkihi, Sanaa El; Thami, Rachid Oulad Haj

doi:10.1109/access.2019.2946522

Cited by 29 publications

(36 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Classification: SVM has been widely applied in fall detection studies [16], [21], [27], [38]- [41]. We used SVM to classify the PCA values ( P k×l ) obtained in Subsection III-B-2 into cycling status ( y i = 1) and falling status ( y i = −1).…”

Section: B Design Of Softwarementioning

confidence: 99%

“…The proposed system applies principal component analysis (PCA), which has been widely used in engineering fields to reduce the dimensionality [16], [20]- [22], [36], [37], to these 24 MARG features to reduce their dimensionality. To classify bicycle accident events, the proposed system adopts support vector machines (SVM), which has been widely used for classification in science and engineering studies [16], [21], [27], [38]- [41]. The rest of this article is organized as follows: Section II describes the materials for our proposed accident detection system.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Accident Detection System for Bicycle Riders

2021

View full text Add to dashboard Cite

Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders' injuries is higher compared to the risk of injuries for motorists. Crashes, riders' errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (X, Y, and Z). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system. Index Terms-Bicycle accident, fall detection, MARG, principal component analysis, support vector machines. I. INTRODUCTION A CCORDING to the Centers for Disease Control and Prevention's (CDC) report published in 2017 [1], bicycle-related accidents lead to around 1,000 fatal and 467,000 non-fatal injuries. There are various causes associated with these bicycle-related accidents such as rider's carelessness, environmental hazards, and crashes with motor vehicles or other bicycles [2]. More than 45% of bicycle-related accidents are reported to occur in dark conditions and urban areas [1], [3]. Moreover, in some situations, the rider may be alone or unable to ask for help due to shock, injuries, or unconsciousness, which can delay medical treatment [4]. There have been studies on analyzing and monitoring riding conditions to increase the safety of bicycle riders [3], [5]-[10]. In [9], a smartphone is used to recognize four different types of riding status-right turn, left turn, straight run, and stop. Here, the smartphone was attached to a bicycle handlebar and collected accelerometer and gyroscope data to recognize the movement type of the bicycle based on Manuscript

show abstract

Section: B Design Of Softwarementioning

confidence: 99%

mentioning

confidence: 99%

Accident Detection System for Bicycle Riders

2021

View full text Add to dashboard Cite

show abstract

“…Our algorithm is based on using a CNN model to detect the person’s skeleton into every frame, which is similar to other works (e.g., [ 25 , 26 , 39 ]). With respect to other works in the literature, we compute different features from the skeleton of an imaged person.…”

Section: Resultsmentioning

confidence: 98%

“…Their model achieved a success rate in fall recognition of

. Alaoui et al [ 39 ] developed an algorithm to detect falls by using the variation of a person’s skeleton into the video. Firstly, they detected the joints of the person into the video by using OpenPose.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

et al. 2021

Self Cite

View full text Add to dashboard Cite

Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons.

show abstract

“…Ref. [54] uses Principal Component Analysis (PCA) combined with Support Vector Machine (SVM) reaches 97.5%. Compared [51]- [54], the precondition filter used in the proposed work with LSTM has higher accuracy.…”

mentioning

confidence: 99%

A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

Chang

Hsu

Chen³

2021

IEEE Access

View full text Add to dashboard Cite

As the population worldwide continues to age and the percentage of elderly people continues to increase, falls have been become the second leading cause of death from accidental or unintentional injuries. Although many imaging sensing devices have been used to detect falls for elderly people, most involve using the Internet to transfer images taken by a camera to a large back-end server, which performs the necessary calculations; however, algorithm limitations and computational complexity may cause bottlenecks in image outflow, and the image transfer can result in privacy problems. To address these problems, in this paper, an artificial intelligence (AI) fall detection method is proposed that operates using an edge computing architecture, called the pose estimation-based fall detection methodology (PEFDM), which is based on a human body posture recognition technique. The proposed PEFDM can effectively reduce the computational load, runs smoothly on mainstream edge computing systems and possesses artificial intelligence computing capabilities. By using edge computing, the privacy and upload bandwidth issues caused by image outflow can be eliminated. Experiments with real humans show that the PEFDM can detect falls for elderly people with a recognition accuracy of up to 98.1%.

show abstract

Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

Cited by 29 publications

References 48 publications

Accident Detection System for Bicycle Riders

Accident Detection System for Bicycle Riders

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

Contact Info

Product

Resources

About