A Review of Computational Approaches for Human Behavior Detection

Nigam, Swati; Singh, Rajiv; Misra, A. K.

doi:10.1007/s11831-018-9270-7

Cited by 14 publications

(9 citation statements)

References 165 publications

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“…Although the BRIEF algorithm is very fast, its performance is poor due to its sensitivity to noise and lack of rotation invariance. Haarlike features are robust to noise and lighting changes since Haar-like features describe the ratio between dark and bright areas in a kernel, which can effectively express the details in the descripted region [56]. The feature description method in SIFT algorithm is superior to Haar-like method in the performance of rotation invariance, but inferior to Haar-like method in the brightness invariance [61].…”

Section: Experimental Results For Solid Anatomical Poimentioning

confidence: 99%

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Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

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Section: Experimental Results For Solid Anatomical Poimentioning

confidence: 99%

“…The Haar-like feature reflects the change in image gray scale [56]. The feature description algorithm based on Haar-like features takes a feature point as the center and constructs an S × S square window.…”

Section: B Feature Descriptionmentioning

confidence: 99%

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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“…DTW is an algorithm used for measuring similarities between two temporal sequences at different speeds [31]. DTW was originally designed for speech recognition and has been applied to temporal sequences in audio, video, and graphical data [32]. It can be used to analyze any data in a linear sequence.…”

Section: Dynamic Time Warping (Dtw) Classifiermentioning

confidence: 99%

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

Chin-Shyurng

Lee

2019

Applied Sciences

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Gesture recognition is a human−computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.

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“…Observational studies mainly comprise of cross-sectional, longitudinal and case-control designs [ 12 ], and have often been mistakenly considered as merely qualitative. Conversely, current observational analysis techniques allow one to collect quantitative data and to employ more sophisticated computational approaches for the systematic observation of behavior [ 13 ] This opportunity is highly relevant in the context of developmental and clinical research, where observational techniques have the great advantage of being almost completely non-invasive, or minimally invasive in many cases [ 3 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Of interest, micro-coding scales have the potential for advanced computational analysis. However, it is necessary to objectively and quantitatively measure interaction variables and dynamics first [ 13 , 49 ]. Multidisciplinary approaches based on social signal processing (SSP) can be used to acquire behavioral data from different sources [ 49 ].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Child–Therapist Interaction in ASD Intervention: An Observational Coding System

et al. 2021

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Background: Observational research plays an important part in developmental research due to its noninvasiveness. However, it has been hardly applied to investigate efficacy of the child–therapist interaction in the context of naturalistic developmental behavioral interventions (NDBI). In particular, the characteristics of child–therapist interplay are thought to have a significant impact in NDBIs in children with autism spectrum disorder (ASD). Quantitative approaches may help to identify the key features of interaction during therapy and could be translated as instruments to monitor early interventions. Methods: n = 24 children with autism spectrum disorder (ASD) were monitored from the time of the diagnosis (T0) and after about one year of early intervention (T1). A novel observational coding system was applied to video recorded sessions of intervention to extract quantitative behavioral descriptors. We explored the coding scheme reliability together with its convergent and predictive validity. Further, we applied computational techniques to investigate changes and associations between interaction profiles and developmental outcomes. Results: Significant changes in interaction variables emerged with time, suggesting that a favorable outcome is associated with interactions characterized by increased synchrony, better therapist’s strategies to successfully engage the child and scaffold longer, more complex and engaging interchanges. Interestingly, data models linked interaction profiles, outcome measures and response trajectories. Conclusion: Current research stresses the need for process measures to understand the hows and the whys of ASD early intervention. Combining observational techniques with computational approaches may help in explaining interindividual variability. Further, it could disclose successful features of interaction associated with better response trajectories or to different ASD behavioral phenotypes that could require specific dyadic modalities.

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A Review of Computational Approaches for Human Behavior Detection

Cited by 14 publications

References 165 publications

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

Quantifying the Child–Therapist Interaction in ASD Intervention: An Observational Coding System

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