human motion tracking
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Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 292
Author(s):  
Kai-Yu Chen ◽  
Li-Wei Chou ◽  
Hui-Min Lee ◽  
Shuenn-Tsong Young ◽  
Cheng-Hung Lin ◽  
...  

Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.


Nano Energy ◽  
2021 ◽  
pp. 106601
Author(s):  
Yuanming Zeng ◽  
Huijing Xiang ◽  
Ning Zheng ◽  
Xia Cao ◽  
Ning Wang ◽  
...  

2021 ◽  
Author(s):  
Du Ming

Tracking human motion from monocular video sequences has attracted a great deal of interests in recent years. The difficulty in solving this problem is largely due to the nonlinear property of human dynamics and the high dimensionality of the state vector space required to model human motion. Traditional particle filtering methods usually fail in this situation because the distributions they sample from are ill-defined. In this thesis we propose a novel tracking algorithm, namely the Differential Evolution - Markov Chain (DE-MC) particle filtering. It is based on the particle filter framework but makes substantial changes to its core, i.e. the sampling strategy. In this new approach, the Differential Evolution algorithm and the Markov Chain Monte Carlo algorithm are integrated, aiming at improving both the accuracy and efficiency in approximating the posterior distribution. Global optimization and importance sampling are spirits of the proposed method. To apply the DE-MC particle filter to articulated model-based human motion tracking, we also integrate multiple image cues including the area of silhouettes, color histograms and boundaries to measure the image likelihoods. We find the Fourier Descriptor (FD) to be a new and effective image feature in human motion tracking applications. Our other contributions, such as a modified color cue-based measurement function and a simple adaptive strategy for sampling, also help to improve the performance of the human tracker. Experimental results including the comparison with the performance of other particle filtering methods demonstrate the power of the proposed approach.


2021 ◽  
Author(s):  
Du Ming

Tracking human motion from monocular video sequences has attracted a great deal of interests in recent years. The difficulty in solving this problem is largely due to the nonlinear property of human dynamics and the high dimensionality of the state vector space required to model human motion. Traditional particle filtering methods usually fail in this situation because the distributions they sample from are ill-defined. In this thesis we propose a novel tracking algorithm, namely the Differential Evolution - Markov Chain (DE-MC) particle filtering. It is based on the particle filter framework but makes substantial changes to its core, i.e. the sampling strategy. In this new approach, the Differential Evolution algorithm and the Markov Chain Monte Carlo algorithm are integrated, aiming at improving both the accuracy and efficiency in approximating the posterior distribution. Global optimization and importance sampling are spirits of the proposed method. To apply the DE-MC particle filter to articulated model-based human motion tracking, we also integrate multiple image cues including the area of silhouettes, color histograms and boundaries to measure the image likelihoods. We find the Fourier Descriptor (FD) to be a new and effective image feature in human motion tracking applications. Our other contributions, such as a modified color cue-based measurement function and a simple adaptive strategy for sampling, also help to improve the performance of the human tracker. Experimental results including the comparison with the performance of other particle filtering methods demonstrate the power of the proposed approach.


Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2340
Author(s):  
Ashok Kumar Patil ◽  
Adithya Balasubramanyam ◽  
Jae Yeong Ryu ◽  
Bharatesh Chakravarthi ◽  
Young Ho Chai

Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems.


Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2259
Author(s):  
Silje Ekroll Jahren ◽  
Niels Aakvaag ◽  
Frode Strisland ◽  
Andreas Vogl ◽  
Alessandro Liberale ◽  
...  

Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.


Author(s):  
Mattia Guidolin ◽  
Razvan Andrei Budau Petrea ◽  
Oboe Roberto ◽  
Monica Reggiani ◽  
Emanuele Menegatti ◽  
...  

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