scholarly journals Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements

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.

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Yi Li ◽  
Zhengxing Sun

We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA) algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.


2020 ◽  
Vol 67 (10) ◽  
pp. 8659-8669 ◽  
Author(s):  
Jun-Hao Zhang ◽  
Peng Li ◽  
Cong-Cong Jin ◽  
Wen-An Zhang ◽  
Steven Liu

2016 ◽  
Vol 147 ◽  
pp. 655-658 ◽  
Author(s):  
Daniel Dinu ◽  
Martin Fayolas ◽  
Marine Jacquet ◽  
Elsa Leguy ◽  
Jean Slavinski ◽  
...  

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.


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