Unstructured human activity detection from RGBD images

Sung, Jaeyong; Ponce, Colin; Selman, Bart; Saxena, Ashutosh

doi:10.1109/icra.2012.6224591

Cited by 193 publications

(59 citation statements)

References 32 publications

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“…However, most of these studies focus on the recognition of gaming actions (e.g., wave, punch, kick, clap, jogging) [33, 37], or very specific daily life activities (e.g., talking on the phone, drinking water, working on computer, cooking) [34–36], where standing and/or walking are often considered as random activities.…”

Section: Related Workmentioning

confidence: 99%

System for automatic gait analysis based on a single RGB-D camera

et al. 2018

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Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable, portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 ± 25 ms and 1 ± 29 ms (walking towards the sensor), and 12 ± 23 ms and 2 ± 24 ms (walking away from the sensor). Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.

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Section: Related Workmentioning

confidence: 99%

System for automatic gait analysis based on a single RGB-D camera

et al. 2018

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“…Regarding the use of specialised sensors such as the Kinect, the main problems of their implementation lies (a) in the increase in costs and (b) in the fact that there are already effective methods to extract a person from the background without their use. As in the previous case, the Kinect sensor is used in another approach [34]. Instead of subtracting the background, this paper makes use of the skeleton delivered by the proper device.…”

Section: Resultsmentioning

confidence: 99%

“…The most traditional approach to identify specific human events by analysing images is the identification of the human's posture [29][30][31]. This can be achieved by analysing the silhouette of the person [16,25], obtaining the skeleton [32][33][34], processing the complete information of the person's image, which is the approach used in this work, or even a combination of techniques [35].…”

Section: Related Workmentioning

confidence: 99%

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

et al. 2019

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Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.

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“…Additionally this work aims to test the system in realistic environments with a number of events and participants, comparable with other public dataset and works [24,39,43,44]. In order to achieve the proposed goal, a strict methodology has been applied to recognize eight common daily activities.…”

Section: Methodsmentioning

confidence: 99%

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Fiorini

Bonaccorsi

Betti

et al. 2018

AIS

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Abstract. Thanks to the pervasiveness of smart technologies, researchers could aggregate data and investigate user's activities thus to deliver personalized home-care services. Activity recognition system have been widely developed, however some challenges still need to be addressed. This paper presents a system where information on body movement, vital signs and user indoor location are aggregated to improve the activity recognition. The system was tested in a realistic environment with a total of 3279 instances acquired from ten healthy users. These results encouraged the use of this approach in activity recognition applications, indeed, the overall accuracy values are satisfactory increased.

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Unstructured human activity detection from RGBD images

Cited by 193 publications

References 32 publications

System for automatic gait analysis based on a single RGB-D camera

System for automatic gait analysis based on a single RGB-D camera

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

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