Baptizo: A sensor fusion based model for tracking the identity of human poses

Bazo, Rodrigo; Reis, Eduardo Souza dos; Seewald, Lucas Adams; Rodrigues, Vinicius Facco; Costa, Cristiano André da; Gonzaga, Luiz; Antunes, Rodolfo Stoffel; Righi, Rodrigo da Rosa; Maier, Andreas; Eskofier, Bjoern M.; Fahrig, Rebecca; Horz, Tim

doi:10.1016/j.inffus.2020.03.011

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…The RTLS is much better than RGBD devices. The authors used the sensor devises in recent advancement that enable the computing devices use opt methods to estimate human posture [6].…”

Section: Human Pose Estimationmentioning

confidence: 99%

Yoga Pose Estimation along with Human Posture Detection using Deep Learning Approach

Deepa¹,

Velumani²,

Selvaraj³

2023

ACE

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The yoga is the ancient art for keeping the human body healthy and fit and acquiring mental peace. This position in yoga almost matches with human posture for comfortable physical activity. The different sequence of body joint action leads to a specific yoga asana that has its own benefit for health. If asana are done in improper way, it would create a bad impact not only in health but also in mental peace. The advancement in computer vision technology help the yoga practitioner identify if they are doing right asana by applying various machine learning and deep learning algorithms for pose estimation. This article has done the concise literature survey on different machine and deep learning algorithm available in computer vision technology. The survey portraits the different classification for human posture estimation and yoga pose estimation along with their contributions and specific difference in between them. Especially the CNN, RNN and LSTM algorithm evolution is briefly narrated in this concise survey for yoga pose estimation.

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“…The RTLS is much better than RGBD devices. The authors used the sensor devises in recent advancement that enable the computing devices use opt methods to estimate human posture [6].…”

Section: Human Pose Estimationmentioning

confidence: 99%

Yoga Pose Estimation along with Human Posture Detection using Deep Learning Approach

Deepa¹,

Velumani²,

Selvaraj³

2023

ACE

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“…It demonstrated improved reliability and accuracy in real-life traffic testing scenarios. To enhance tracking accuracy in VR-based HCI, Bazo et al [ 53 ] proposed a system that integrates radiofrequency-based positioning with computer vision-based human pose estimation techniques. By fusing radiofrequency sensor identities with unidentified body poses and estimated body parts, the system achieved a substantial reduction in positioning errors of nearly 46%.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

et al. 2023

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The rapidly evolving field of Virtual Reality (VR)-based Human–Computer Interaction (HCI) presents a significant demand for robust and accurate hand tracking solutions. Current technologies, predominantly based on single-sensing modalities, fall short in providing comprehensive information capture due to susceptibility to occlusions and environmental factors. In this paper, we introduce a novel sensor fusion approach combined with a Long Short-Term Memory (LSTM)-based algorithm for enhanced hand tracking in VR-based HCI. Our system employs six Leap Motion controllers, two RealSense depth cameras, and two Myo armbands to yield a multi-modal data capture. This rich data set is then processed using LSTM, ensuring the accurate real-time tracking of complex hand movements. The proposed system provides a powerful tool for intuitive and immersive interactions in VR environments.

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“…Sensor fusion is a common approach to complementing sensor modalities for HIAR [ 1 ]. Bazo et al proposed a sensor fusion model, Baptizo, in [ 43 ] that it leverages active RF positioning data captured with ultra-wideband (UWB) devices and RGB-Depth (RGBD) human pose estimation for the reduction of human positioning error to assist with the eventual activity recognition classification. This work can be applied to clinical environments, where a human subject may be behind an occlusion in a tight environment and unseen by the RGBD, allowing for sensor fusion to enhance the robustness and classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…One frequency modulated continuous wave (FMCW) radar and three UWB radars were combined to achieve 0.84 accuracy of twelve different activities using the VGG-16 convolutional neural network (CNN) model and hard fusion with the Naïve Bayes combiner (NBC). The works presented in the [ 43 , 44 ] present video modalities are intrusive to the monitored subjects, and the work presented in [ 45 ] presents a solution that utilizes active RF sensors that come with negative energy and health concerns. Therefore, a non-intrusive, inexpensive, pollution-free, and accurate monitoring solution can fill the gaps in the systems introduced in the literature.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

Yuan

Andrews

et al. 2022

Sensors

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Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.

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Baptizo: A sensor fusion based model for tracking the identity of human poses

Cited by 8 publications

References 40 publications

Yoga Pose Estimation along with Human Posture Detection using Deep Learning Approach

Yoga Pose Estimation along with Human Posture Detection using Deep Learning Approach

A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

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