Human fall detection using machine vision techniques on RGB–D images

Panahi, Leila; Ghods, Vahid

doi:10.1016/j.bspc.2018.04.014

Cited by 57 publications

(34 citation statements)

References 14 publications

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“…With the development of computer vision and image processing technology, computer vision-based fall detection [46][47][48][49][50][51][52] has become an important method, as the systems are less invasive to elderly and higher precision and robustness, in the period of 2003-2018. The algorithm includes background subtraction and feature classification.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…Unlike [50], two Hidden Markov Models (HMMs) are applied to classify falls and activities of daily living. Leila et al [52] employed two shape features and one 2D position feature to distinguish different postures including standing, sitting, bending, squatting, lying on the side and lying toward the camera. Posture classification is completed by SVM algorithm.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…For the purpose of objective comparison, we employ the method [52] proposed by Panahi as fall detection algorithm to evaluate metrics of the background subtraction method (BGS for short). In this method, the ATC (composed of aspect ratio, tilt angle, centroid height of the contour of the human body) features were extracted from the binary image of human foreground.…”

Section: T2-fgmm-um With Markov Random Fieldmentioning

confidence: 99%

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Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other stateof-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.

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Section: Related Work and Contributionmentioning

confidence: 99%

Section: Related Work and Contributionmentioning

confidence: 99%

Section: T2-fgmm-um With Markov Random Fieldmentioning

confidence: 99%

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Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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“…Kong et al [27] propose an effective fall detection method based on computer vision-based framework, which learned to take full advantage of the appearance and motion information. Leila et al [28] propose a machine vision-based system combined with Support Vector Machine (SVM) classifier, which has high sensitivity and specificity of 100% and 97.5% respectively. Lee et al [30] propose a new probabilistic gait representation to characterize human walking for recognition by gait.…”

Section: Introductionmentioning

confidence: 99%

Walking Assist Robot: A Novel Non-Contact Abnormal Gait Recognition Approach Based on Extended Set Membership Filter

et al. 2019

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This work develops intelligent walking assist robot and gait rehabilitation robot for people with walking disabilities, where distinguishing of abnormal gait is crucial during safe human-robot interaction. Although vision sensors and wearable inertial devices are widely used in fall detection system, shielding of robot mechanical structure brings instability for visual detection method, and utilizing a large number of wearable sensors brings discomfort and complexity for users. Meanwhile, the frequent drag-to-drop gait has never been considered, which is a kind of intermittent state between normal state and fall. It's a major challenge to achieve an abnormal gait recognition system with high stability and good comfort. In this paper, a novel non-contact gait recognition method is proposed, which can identify fall and drag-to-drop gait in a stable and comfortable way. Leg movement data is obtained by a built-in-robot camera, which accurately describes gait law through contrast test and error analysis. New-type ESMF algorithm is proposed to evaluate knee angle precisely and resolve the issue of marker point loss effectively. The comprehensive experiments have shown that this innovative approach calculates accurately knee angle in the walking process so as to recognize the phenomenon of drag-to-drop gait and fall. Additionally, this method having a sound theoretical basis and higher stability solves the issue of marker point loss, which could be applied to a walker with a similar structure for elderly and disabled in a comfortable way. INDEX TERMS Walking assist robot, fall recognition, drag-to-drop gait, marker point loss, non-contact detection, extended set membership filter.

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“…1 With video analysis method, one or multiple video cameras are usually installed in the elderly activity areas to determine whether the elderly falling occurs via certain image processing techniques. [3][4][5][6][7] The environmental variable analysis method uses one or more sensors to detect environmental changes in a certain space to collect information of the body so as to determine whether the falls occur. The commonly used sensors include infrared sensors, 8 audio sensor, 9,10 vibration sensor, 11,12 radio frequency (RF) signal, 13,14 and so on.…”

Section: Introductionmentioning

confidence: 99%

The real-time elderly fall posture identifying scheme with wearable sensors

Sun

et al. 2019

International Journal of Distributed Sensor Networks

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The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

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Human fall detection using machine vision techniques on RGB–D images

Cited by 57 publications

References 14 publications

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Walking Assist Robot: A Novel Non-Contact Abnormal Gait Recognition Approach Based on Extended Set Membership Filter

The real-time elderly fall posture identifying scheme with wearable sensors

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