Bio-Inspired Human Action Recognition With a Micro-Doppler Sonar System

Murray, Thomas G.; Mendat, Daniel R.; Sanni, Kayode; Pouliquen, Philippe O.; Andreou, Andreas G.

doi:10.1109/access.2017.2732919

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…Additionally, a human action recognition algorithm is displayed in ref. [10], specifically, the statistical models are trained simultaneously on both the micro‐Doppler modulations induced by human actions and symbolic representations of skeletal poses, the training, meanwhile, enables the model to learn the relations between the rich temporal structure of the micro‐Doppler modulations and the high‐dimensional pose sequences of human action. An approach used for real‐time human sensing based on micro‐Doppler estimation for radar target is displayed in ref.…”

Section: Introductionmentioning

confidence: 99%

Sparse time‐frequency analysis for aircraft target classification with low sampling rate and short observation time

Wang

Yang

Yin

et al. 2021

Int J Numerical Modelling

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The traditional time‐frequency analysis (TFA) techniques are instruments for target classification, which can reflect the feature of the target in the time‐frequency domain. However, it will lead to a serious decrease in the recognition accuracy as the decline of the sampling rate. To ease this problem, in this article, the sparse time‐frequency feature analysis (STFFA) is implemented for aircraft classification, and the genetic algorithm is adopted to solve the sparse problem quickly for saving time. Firstly, the sparse time‐frequency decomposition and recovery signal are obtained by matching pursuit. Then, three novel kinds of aircraft features are extracted from the sparse time‐frequency decomposition, which are sparse recovery time‐frequency entropy (SRTFE), frequency entropy by time, and first‐order sparse time‐frequency moment. Thus, the feature combination modes based on the three features are applied to realize the classification of aircraft and compared with the traditional TFA techniques. Besides, a support vector machine is also used to classify the three kinds of aircraft. The accuracy and efficiency of the STFFA method have been investigated by employing the parametric model data and electromagnetic scattering model simulated trials. Furthermore, in contrast to the traditional TFA instrument, our method can reach a recognition accuracy of more than 90% from the numerical experiment results, which demonstrates that the feature extraction by sparse time‐frequency analysis improves the accuracy of aircraft classification under a low sampling rate.

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Section: Introductionmentioning

confidence: 99%

Sparse time‐frequency analysis for aircraft target classification with low sampling rate and short observation time

Wang

Yang

Yin

et al. 2021

Int J Numerical Modelling

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“…The Thalmann model that has been used in this research provides a very detailed movement description, but for the purpose it is used in it would also be sufficient to have a simpler approximation of the movement model that would be faster to evaluate. A possibility for easily building movement models for many types of movement could be using a 3D pointcloud sensor as described in (Murray et al, 2018).…”

Section: Future Workmentioning

confidence: 99%

An Ultrasonic Sensor for Human Presence Detection to Assist Rescue Work in Large Buildings

Groeningen

Driessen

Söhl

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> When the fire brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. Smart buildings should be able to expose this location data to the fire brigade working in a smart city. In this paper the feasibility is researched of using ultrasonic sound sensors for human presence detection in smoke-filled spaces. This type of sensor could assist the fire brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research both a preliminary ultrasound measuring device and signal processing algorithm have been designed. Testing results show that the walking movement of a person in an indoor area can be detected with the combination of the sensor and the algorithms. In addition, tests of the signal strength in smoke have shown that ultrasound is capable of “looking through” the smoke. The algorithm based on a particle filter allows for more information to be extracted from the relatively simple sensor signal by detecting human walking movement specifically and opens up the way for an ultrasound based indoor positioning system that can be used in emergency situations.</p>

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“…According to the different types of data used for human action recognition, human action recognition is usually divided into the following three main categories: human action recognition based on vision, human action recognition based on acoustics, and human action recognition based on inertial sensors [ 13 ]. Visual action recognition extracts human action data from image or video data obtained by optical sensors [ 14 , 15 ], acoustic action recognition uses sound signals for high-precision hand action tracking and gesture recognition [ 16 , 17 ], inertial sensor action recognition focuses on extracting human action inertial data from wearable inertial sensors [ 18 , 19 ]. Liu Yutao [ 20 ] summarized the three methods, as shown in the following Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Yanan

Jilong

Zhang

2021

Sensors

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Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.

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Bio-Inspired Human Action Recognition With a Micro-Doppler Sonar System

Cited by 10 publications

References 46 publications

Sparse time‐frequency analysis for aircraft target classification with low sampling rate and short observation time

Sparse time‐frequency analysis for aircraft target classification with low sampling rate and short observation time

An Ultrasonic Sensor for Human Presence Detection to Assist Rescue Work in Large Buildings

Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

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