Application of Human Body Posture Recognition Technology in Robot Platform for Nursing Empty-Nesters

Liang, Man; Hu, Yingrui

doi:10.1109/iccar49639.2020.9108070

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…They used surface normal associated with patches of the point cloud as features to be fed to 1vs1-SVM classifier for human nonhuman classification and different postures. In [53], a depth sensor mounted on a mobile robot is used to estimate the posture of the subject for lying down, sitting, standing, and bending over.…”

Section: Related Workmentioning

confidence: 99%

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

Rasoulidanesh

Payandeh

2022

Journal of Sensors

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Inspired by the notion of swarm robotics, sensing, and minimalism, in this paper, we study and analyze how a collection of only 1D depth scans can be used as a part of the minimum feature for human body detection and its segmentation in a point cloud. In relation to the traditional approaches which require a complete point cloud model representation for skeleton model reconstruction, our proposed approach offers a lower computation and power consumption, especially in sensor and robotic networks. Our main objective is to investigate if the reduced number of training data through a collection of 1D scans of a subject is related to the rate of recognition and if it can be used to accurately detect the human body and its posture. The method takes advantage of the frequency components of the depth images (here, we refer to it as a 1D scan). To coordinate a collection of these 1D scans obtained through a sensor network, we also proposed a sensor scheduling framework. The framework is evaluated using two stationary depth sensors and a mobile depth sensor. The performance of our method was analyzed through movements and posture details of a subject having two relative orientations with respect to the sensors with two classes of postures, namely, walking and standing. The novelty of the paper can be summarized in 3 main points. Firstly, unlike deep learning methods, our approach would require a smaller dataset for training. Secondly, our case studies show that the method uses very limited training dataset and still can detect the unseen situation and reasonably estimate the orientation and detail of the posture. Finally, we propose an online scheduler to improve the energy efficiency of the network sensor and minimize the number of sensors required for surveillance monitoring by employing a mobile sensor to recover the occluded views of the stationary sensors. We showed that with the training data captured on 1 m from the camera, the algorithm can detect the detailed posture of the subject from 1, 2, 3, and 4 meters away from the sensor during the walking and standing with average accuracy of 93% and for different orientation with respect to the sensor by 71% accuracy.

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Section: Related Workmentioning

confidence: 99%

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

Rasoulidanesh

Payandeh

2022

Journal of Sensors

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“…If the human skeleton can be extracted and tracked reliably, the human behavior then can be classified by action recognition. At present, the detection of human joints has been widely used in the fields of virtual reality [4], automatic driving [5] and elderly care system [6].…”

Section: Introductionmentioning

confidence: 99%

3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Wang

et al. 2020

IEEE Access

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Joint estimation of human body in point cloud is a key step for tracking human movements. In this work, we present a geometric method to achieve detection of the joints from a single-frame point cloud captured using a Time-of-Flight (ToF) camera. Three-dimensional (3D) human silhouette, as global feature of the single-frame point cloud, is extracted based on the pre-processed data, the angle and aspect ratio of the silhouette are subsequently utilized to perform pose recognition, and then 14 joints of human body are derived via geometric features of 3D silhouette. To verify this method, we test on an in-house captured 3D dataset containing 1200-frame depth images, which can be categorized into four different poses (upright, raising hands, parallel arms, and akimbo). Furthermore, we test on a subset of the G3D dataset. By hand-labelling the joints of each human body as the ground truth for validation and benchmarks, the average normalized error of our geometric method is less than 5.8 cm. When the distance threshold from the ground truth is 10 cm, the results demonstrate that our proposed method delivers improved performance with an average accuracy in the range of 90%. INDEX TERMS Depth camera, human pose detection, joint detection, sensor systems and applications.

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Application of Human Body Posture Recognition Technology in Robot Platform for Nursing Empty-Nesters

Cited by 2 publications

References 2 publications

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

3D Joints Estimation of the Human Body in Single-Frame Point Cloud

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