Name
Title | ||
|---|---|---|
Utilizing mobile devices for evaluating body trunk coordination: Feasibility and preliminary results |
Abstract | ||
|---|---|---|
Enhancing body trunk coordination is an important aspect in personal health, as week coordination ability of the body trunk is associated to increasing risk of lower back pain and falling. Many people are doing core training, but there is no affordable tool that they can use to examine the effect of exercise on their trunk coordination ability. In this study we aim to utilize mobile devices for automatic evaluation of trunk coordination ability. Firstly, we designed two standard movements that require smooth coordination of body trunk and that are easy to perform by the general population. Secondly, we conducted experiments to collect acceleration data of the movements using two mobile devices from 13 participants when they performed the standard movements. The two mobile devices were fixed in front of the chest and behind the pelvis using belts. These movements were evaluated by a sport professional and the data were labelled as either good or poor coordination. We extracted time and frequency domain features from the data and we applied support vector machine to classify good and poor coordination. The results showed that the SVM models achieved reasonably high classification accuracy (all above 80%) for both movements. In addition, the iPod placed on the chest yielded better accuracy than that placed on the pelvis for hip joint rotation, whereas the opposite result was observed for chest rotation. Our study suggested that it is feasible to use iPod for automatic evaluation of body trunk coordination, and the results provided implications to future researches on portable and affordable solutions to physical health in home settings. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.23919/ICMU.2017.8330103 | 2017 Tenth International Conference on Mobile Computing and Ubiquitous Network (ICMU) |
Keywords | Field | DocType |
Health,mobile computing,pervasive computing,accelerometer,trunk strength | Frequency domain,Population,Simulation,Computer science,Support vector machine,Back pain,Feature extraction,Mobile device,Acceleration,Trunk | Conference |
ISBN | Citations | PageRank |
978-1-5386-0594-3 | 0 | 0.34 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zilu Liang | 1 | 38 | 10.72 |
| Takuichi Nishimura | 2 | 576 | 65.34 |
| Nami Iino | 3 | 0 | 1.35 |
| Yasuyuki Yoshida | 4 | 0 | 1.01 |
| Satoshi Nishimura | 5 | 1 | 5.14 |