Evaluation of the Daily Living Activity in Patients with Miastenia Gravis

This study analyses the residual femur motion of a single amputee within a trans-femoral socket during a series of daily living activities. Two simultaneously transmitting, socket mounted transducers were connected to two ultrasound scanners. Displacement measurements of the ultrasound image of the femur were video recorded and measured on “paused” playback. Abduction/adduction and flexion/extension of the residual femur within the socket at any instant during these activities were estimated, knowing the relative positions of the two transducers and the position of the residual femur on the ultrasound image. Consistent motion patterns of the residual femur within the trans-femoral socket were noted throughout each monitored daily living activity of the single amputee studied. Convery and Murray (2000) reported that during level walking, relative to the socket, the residual femur extends 6° and abducts 9° by mid-stance while flexing 6° and adducting 2° by toe-off. Uphill/downhill, turning to the right and stepping up/down altered this reported pattern of femoral motion by approximately 1°. During the standing activity from a seated position the femur initially flexed 4° before moving to 7° extension, while simultaneously adducting 6°. During the sitting activity from a standing position the femur moved from 7° extension and 6° adduction to 3° flexion and 1° abduction. The activity of single prosthetic support to double support introduced only minor femoral motion whereas during the activity of prosthetic suspension the femur flexed 8° while simultaneously adducting 9°. Additional studies of more amputees are required to validate the motion patterns presented in this investigation.

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A Study on Psychological Wellbeing and daily living activity of the elderly in home in Korea and Japan

Asia-pacific Journal of Multimedia services convergent with Art Humanities and Sociology ◽

10.14257/ajmahs.2016.12.16 ◽

2016 ◽

Vol 6 (12) ◽

pp. 293-307

Author(s):

Mi-Ran Lee ◽

Seon-Hee Kim

Keyword(s):

Daily Living ◽

The Elderly ◽

Psychological Wellbeing ◽

Daily Living Activity

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Fall Detection and Daily Living Activity Recognition Logic Regression

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9223 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3520-3525

Author(s):

J. Refonaa ◽

Bandaru Suhas ◽

B. V. S. Bhaskar ◽

S. L. JanyShabu ◽

S. Dhamodaran ◽

...

Keyword(s):

Daily Living ◽

Detection System ◽

Fall Detection ◽

Learning System ◽

Detection Accuracy ◽

Logic Regression ◽

Daily Living Activity ◽

Average Value ◽

Different Types ◽

Angular Velocities

It is a must to bring the fall detection system in to use with the increasing number of elder people in the world, because the most of them tend live voluntarily and at risk of injuries. Falls are dangerous in a few cases and could even lead to deadly injuries. A very robust fall detection system must be built in order to counter this problem. Here, we establish fall detection and recognition of daily live behavior through machine learning system. In order to detect different types of activities, including the detection of falls and day to-day activities, We use 2 shared archives for the accelerating and lateral speed data during this development. Logistic regression is used to determine motions such as drop, walk, climb, sit, stand and lie bases on the accelerating data and data on angular velocities. More specifically, the triaxial acceleration average value is used to achieve fall detection accuracy.

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