A machine learning approach for non-invasive fall detection using Kinect

Mansoor, Mahrukh; Amin, Rashid; Mustafa, Zaid; Sengan, Sudhakar; Aldabbas, Hamza; Alharbi, Mafawez

doi:10.1007/s11042-022-12113-w

Cited by 14 publications

(23 citation statements)

References 25 publications

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“…The body is an essential part of the human body. The motion feature information of some waists of the human body comes from this part of the joint points, and the motion feature information of the hands and feet comes from the joint points of the limbs [20].…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Motion Standard Based on Kinect Human Bone and Joint Data Acquisition

Xiao-ning

2022

Wireless Communications and Mobile Computing

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In order to improve human bone and joint data, we propose a method to collect data and judge the standard of motion. Kinect is a 3D somatosensory camera released by Microsoft. It has three cameras in total. The middle is a color camera, which can take color images and obtain 30 images per second; on the left is the infrared projector, which irradiates the object to form speckle. On the right is the depth camera to analyze the infrared spectrum. On both sides are two depth sensors to detect the relative position of people. On both sides of Kinect are a set of quaternion linear microphone arrays for speech recognition and filtering background noise, which can locate the sound source. There is also a base with built-in motor below, which can adjust the elevation angle. It can not only complete the collection of color images, but also measure the depth information of objects. The experimental results show that we use MSRAction3D data set and compare the same cross-validation method with other latest research methods in the figures. The highest recognition rate of this method (algorithm 10) is the second, and the lowest and average recognition rates are the highest. The improvement in the lowest recognition rate is obvious, which can show that this method has good recognition performance and better stability than other research methods. Kinect plays a relatively important role in the movement of human bone and joint data acquisition.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Motion Standard Based on Kinect Human Bone and Joint Data Acquisition

Xiao-ning

2022

Wireless Communications and Mobile Computing

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“…Therefore, this paper chooses to construct a multi-vision sensor sampling system composed of three Kinect V2, which ensures that any joint can be photographed by at least one camera at any time. 5 Using multi-threaded tools, the simultaneous sampling of three cameras can be achieved, and the system time corresponding to each sampling point can be recorded in real-time. These coordinate systems are different, so obtaining the transformation relationship between them is necessary.…”

Section: Methodsmentioning

confidence: 99%

“…If only one visual sensor is used to obtain data, there will be some cases that the data cannot be obtained in the experiment, such as the subject happens to act with his/her back to the sensor and the mutual occlusion between the joint points. Therefore, this paper chooses to construct a multi‐vision sensor sampling system composed of three Kinect V2, which ensures that any joint can be photographed by at least one camera at any time 5 . Using multi‐threaded tools, the simultaneous sampling of three cameras can be achieved, and the system time corresponding to each sampling point can be recorded in real‐time.…”

Section: Methodsmentioning

confidence: 99%

Sanda training program based on multi‐sensor data fusion in the internet of things environment

Zhou

2023

Internet Technology Letters

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Sanda, also known as Chinese kickboxing, is a Wushu that combines elements of traditional Kung Fu, kickboxing, and grappling. Considering that the use of action recognition can effectively improve the level of Sanda players, this study uses inertial sensors and visual sensors to collect the motion data of Sanda. A federated Kalman filter with time‐varying parameters is designed according to the error form of each sensor to complete the data fusion. The main idea is that the whole system is divided into several local filtering systems, and a primary filter is designed for these systems. The main structure of the filter is a cascaded decentralized filtering structure composed of several local filters and a primary filter. The experimental results show that the proposed Sanda training scheme based on multi‐sensor data fusion under the Internet of Things environment has high action recognition accuracy, and the joint inertial‐vision model produces significantly better results than the other two models.

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“…Random forest, Support vector machine, k-Nearest neighbors, Multi layer perceptron Depth Images: Depth images, which measure the distance between objects in the frame and allow for the monitoring of changes in an object's depth, are used to detect falls. In [34], skeletal information is tracked in depth images to enhance privacy preservation. A fall is presumed to have occurred if the head's motion history images show greater variance in head position over time.…”

Section: Approachmentioning

confidence: 99%

Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection

Inturi

Manikandan

Kumar

et al. 2023

Sensors

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According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.

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A machine learning approach for non-invasive fall detection using Kinect

Cited by 14 publications

References 25 publications

Evaluation of Motion Standard Based on Kinect Human Bone and Joint Data Acquisition

Evaluation of Motion Standard Based on Kinect Human Bone and Joint Data Acquisition

Sanda training program based on multi‐sensor data fusion in the internet of things environment

Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection

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