scholarly journals Measurement of chest wall motion using a motion capture system with the one-pitch phase analysis method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroyuki Tamiya ◽  
Akihisa Mitani ◽  
Hideaki Isago ◽  
Taro Ishimori ◽  
Minako Saito ◽  
...  

AbstractSpirometry is a standard method for assessing lung function. However, its use is challenging in some patients, and it has limitations such as risk of infection and inability to assess regional chest wall motion. A three-dimensional motion capture system using the one-pitch phase analysis (MCO) method can facilitate high precision measurement of moving objects in real-time in a non-contacting manner. In this study, the MCO method was applied to examine thoraco-abdominal (TA) wall motion for assessing pulmonary function. We recruited 48 male participants, and all underwent spirometry and chest wall motion measurement with the MCO method. A significant positive correlation was observed between the vital capacity (Spearman’s ρ = 0.68, p < 0.0001), forced vital capacity (Spearman’s ρ = 0.62, p < 0.0001), and tidal volume (Spearman’s ρ = 0.61, p < 0.0001) of spirometry and the counterpart parameters of MCO method. Moreover, the MCO method could detect regional rib cage and abdomen compartment contributions and could assess TA asynchrony, indicating almost complete synchronous movement (phase angle for each compartment: − 5.05° to 3.86°). These findings suggest that this technique could examine chest wall motion, and may be effective in analyzing chest wall volume changes and pulmonary function.

2020 ◽  
Vol 81 ◽  
pp. 238-239
Author(s):  
K. Nicholson ◽  
J. Salazar-Torres ◽  
P. Gabos ◽  
F. Miller ◽  
J. Henley ◽  
...  

2014 ◽  
Vol 37 (9) ◽  
pp. 719-725 ◽  
Author(s):  
Satoko Naitoh ◽  
Katsuyuki Tomita ◽  
Keita Sakai ◽  
Akira Yamasaki ◽  
Yuji Kawasaki ◽  
...  

2004 ◽  
Vol 38 (6) ◽  
pp. 369-374 ◽  
Author(s):  
Ásdís Kristjánsdóttir ◽  
María Ragnarsdóttir ◽  
Pétur Hannesson ◽  
Hans Jakob Beck ◽  
Bjarni Torfason

Author(s):  
Unai Zabala ◽  
Igor Rodriguez ◽  
José María Martínez-Otzeta ◽  
Elena Lazkano

AbstractNatural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation.


Sign in / Sign up

Export Citation Format

Share Document