scholarly journals Stature Data and Measurement Unit in Chosŏn Korea

2012 ◽  
Vol 41 (null) ◽  
pp. 125-159
Author(s):  
Young Jun Cho
Keyword(s):  
Measurement Unit ◽  
Choson Korea

Mantle Of The Expert Strategy Effectiveness In The Development Of Creative Thinking

2019 ◽  
Author(s):  
Ibrahim Arman
Keyword(s):  
Creative Thinking ◽  
Role Playing ◽  
Thinking Skills ◽  
Measurement Unit ◽  
Quasi Experimental ◽  
Males And Females ◽  
The Mean ◽  
Girls School ◽  
And Control ◽  
Experimental Group

<p>This study aimed at investigating the Effectiveness of Mantle of the Expert in creative thinking Skills among the 7<sup>th</sup> Graders. The study conducted on a sample of 7<sup>th</sup> Graders at Kober Secondary Boys School and Upper Kober Elementary Girls School. The study sample consisted of (100) students split into two groups (experimental and control). The researcher adopting the Torrance test for creative thinking the verbal image "A" by examining the tests used in the Ristow study (1988), Edwards and Baldov (1987) study, and designing a teacher book for the (engineering and measurement) unit according to the integration between the strategies of the mantle of the expert and role playing.</p> <p>This study adopted quasi-experimental design. It included two groups (experimental and controlled) in two branches (males and females) for each group. The controlled group was taught by using the traditional method whereas the experimental group by the mantle of the expert. The data analyzed using (ANCOVA) test to measure the differences in the development of creative thinking between the control and experimental groups.</p> <p>The Conclusions showed that there are statistically significant differences in the mean scores of the creative thinking test due to the way, gender and interaction between them.</p> <p>Based on the Conclusions of the study, the researcher recommends the need to use the mantles of the expert in the teaching of mathematics.</p>

scholarly journals Coordination Pattern of the Thigh, Pelvic, and Lumbar Movements during the Gait of Patients with Hip Osteoarthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Takuya Ibara ◽  
Masaya Anan ◽  
Ryosuke Karashima ◽  
Kiyotaka Hada ◽  
Koichi Shinkoda ◽  
...  
Keyword(s):  
Time Series Data ◽  
Hip Osteoarthritis ◽  
Series Data ◽  
Measurement Unit ◽  
Control Group ◽  
Coordination Pattern ◽  
Rotational Movement ◽  
Vector Coding ◽  
Lateral Tilt ◽  
Angular Velocities

There are limited reports on segment movement and their coordination pattern during gait in patients with hip osteoarthritis. To avoid the excessive stress toward the hip and relevant joints, it is important to investigate the coordination pattern between these segment movements, focusing on the time series data. This study aimed to quantify the coordination pattern of lumbar, pelvic, and thigh movements during gait in patients with hip osteoarthritis and in a control group. An inertial measurement unit was used to measure the lumbar, pelvic, and thigh angular velocities during gait of 11 patients with hip osteoarthritis and 11 controls. The vector coding technique was applied, and the coupling angle and the appearance rate of coordination pattern in each direction were calculated and compared with the control group. Compared with the control group, with respect to the lumbar/pelvic segment movements, the patients with hip osteoarthritis spent more rates in anti-phase and lower rates in in-phase lateral tilt movement. With respect to the pelvic/thigh segment movements, the patients with hip osteoarthritis spent more rates within the proximal- and in-phases for lateral tilt movement. Furthermore, patients with osteoarthritis spent lower rates in the distal-phase for anterior/posterior tilt and rotational movement. Patients with hip osteoarthritis could not move their pelvic and thigh segments separately, which indicates the stiffness of the hip joint. The rotational movement and lateral tilt movements, especially, were limited, which is known as Duchenne limp. To maintain the gait ability, it seems important to pay attention to these directional movements.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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