کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6953656 | 1451822 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fault classification with discriminant analysis during sit-to-stand movement assisted by a nursing care robot
ترجمه فارسی عنوان
طبقه بندی گسل با تجزیه و تحلیل اختیاری در طی حرکت نشستن به ایستاده با کمک یک ربات مراقبت پرستاری
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کلمات کلیدی
مرکز جرم، تشخیص گسل، ربات مراقبت پرستاری، حرکت به ایستادن، نیروی واکنش زمین عمودی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Many nursing care robots have been developed to assist patients with sit-to-stand (STS) movement. However, little research has focused on user's negative psychological changes during STS movement when assisted by a robot. STS movement accompanied with a negative psychological change is defined as a fault. The main purpose of this study was to propose a method of conveying faults to a nursing care robot through the vertical ground reaction force (vGRF). Experiments on STS movement were executed five times with ten healthy subjects under four conditions: two self-performed STSs with seat heights of 43 and 62Â cm, and two robot-assisted STSs with a seat height of 43Â cm and end-effector speeds of 2 and 5Â s. Subjects answered a questionnaire on how they felt under the four experimental conditions. Time series data on the vGRF were measured with a Wii Balance Board (WBB). A classifier was designed according to the data on the STS smoothness in the frequency domain. The results showed that the proposed classifier had a high probability of discriminating fault classes from others. Furthermore, faults were found to result in larger standard deviations of the peak values of smoothness. The center of mass trajectories of the human body under the same experimental conditions were used to crosscheck the experimental results. Then, the angles and angular velocities of the trunk and ankle were utilized to discuss the synchrony of the body segments. Other works on more advanced signal analysis and superior fault classification approaches were also discussed. It was concluded that faults in the assistance of nursing care robots can be detected from the STS smoothness by measuring the vGRF.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 113, December 2018, Pages 90-101
Journal: Mechanical Systems and Signal Processing - Volume 113, December 2018, Pages 90-101
نویسندگان
Tianyi Wang, Hieyong Jeong, Mikio Watanabe, Yoshinori Iwatani, Yuko Ohno,