Fall Detection System-Based Posture-Recognition for Indoor Environments

Iazzi, Abderrazak; Rziza, Mohammed; Thami, Rachid Oulad Haj

doi:10.3390/jimaging7030042

Cited by 22 publications

(11 citation statements)

References 59 publications

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“…Iazzi et al [12] proposed a fall detection system that uses different routine postures of a person. e authors' main aim is to classify the abnormal postures, often produced by accidental falls other than the general ones.…”

Section: Work Related To Fall Detectionmentioning

confidence: 99%

Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

Saxena

Moulik

Nayak

et al. 2021

Mathematical Problems in Engineering

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We attempt to predict the accidental fall of human beings due to sudden abnormal changes in their health parameters such as blood pressure, heart rate, and sugar level. In medical terminology, this problem is known as Syncope. The primary motivation is to prevent such falls by predicting abnormal changes in these health parameters that might trigger a sudden fall. We apply various machine learning algorithms such as logistic regression, a decision tree classifier, a random forest classifier, K-Nearest Neighbours (KNN), a support vector machine, and a naive Bayes classifier on a relevant dataset and verify our results with the cross-validation method. We observe that the KNN algorithm provides the best accuracy in predicting such a fall. However, the accuracy results of some other algorithms are also very close. Thus, we move one step further and propose an ensemble model, Majority Voting, which aggregates the prediction results of multiple machine learning algorithms and finally indicates the probability of a fall that corresponds to a particular human being. The proposed ensemble algorithm yields 87.42% accuracy, which is greater than the accuracy provided by the KNN algorithm.

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Section: Work Related To Fall Detectionmentioning

confidence: 99%

Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

Saxena

Moulik

Nayak

et al. 2021

Mathematical Problems in Engineering

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“…In addition, a shallow CNN is applied to find out the correlation between the two elliptic contour features for detecting indoor falls. Iazi et al 4 proposed a fall detection system for elderly people based on their postures. They syggested to recognize the postures from the human silhouette which is an advantage to preserve the privacy of the elderly.…”

Section: Related Workmentioning

confidence: 99%

Saliency based human fall detection in smart home environments using posture recognition

Mousse

Atohoun²

2021

Health Informatics J

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The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.

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“…In a vision-based approach, fall detection is modeled based on images or videos obtained from different types of cameras [9]. Iazzi et al [12] proposed a vision-based fall detector by isolating the different activities using a simple threshold method. The approach first applied background subtraction to images obtained from an RGB camera to compute a human silhouette.…”

Section: Related Workmentioning

confidence: 99%

“…• Sensitivity (Se): also called recall; the ratio means the accuracy among all predictions of falling. • Accuracy (Ac): the accuracy among all predictions, both positive and negative.The equations for specificity, sensitivity, and accuracy are shown in Equations (10)-(12), respectively.Ac =TP + TN TP + TN + FP + FN(12)…”

mentioning

confidence: 99%

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting

Chai

Pike

et al. 2021

Sensors

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During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter’s personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter’s fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively.

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Fall Detection System-Based Posture-Recognition for Indoor Environments

Cited by 22 publications

References 59 publications

Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

Saliency based human fall detection in smart home environments using posture recognition

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting

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