Towards Automatic Recognition of Sounds Observed in Daily Living Activity

2019 ◽  
Author(s):  
Arslan Shaukat ◽  
Ammar Younis ◽  
Usman Akram ◽  
Muhammad Mohsin ◽  
Zartasha Mustansar
Keyword(s):  
Daily Living ◽  
Automatic Recognition ◽  
Daily Living Activity

scholarly journals Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Muchun Su ◽  
Diana Wahyu Hayati ◽  
Shaowu Tseng ◽  
Jiehhaur Chen ◽  
Hsihsien Wei
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Activities Of Daily Living ◽  
Video Processing ◽  
Daily Living ◽  
Deep Neural Network ◽  
Recognition System ◽  
Automatic Recognition ◽  
Data Filtering ◽  
Accuracy Rate

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

Type A Behavior and Daily Living Activity among Older Minority Women

2002 ◽  
Vol 11 (2) ◽  
pp. 137-146 ◽  
Author(s):  
Karen E. Eason ◽  
Louise C. Mâsse ◽  
Susan R. Tortolero ◽  
Steven H. Kelder
Keyword(s):  
Daily Living ◽  
Type A ◽  
Type A Behavior ◽  
Minority Women ◽  
Daily Living Activity

scholarly journals SEAbIRD: Adaptable Daily Living Activity Identification from Sensor Data Streams

2018 ◽  
Vol 130 ◽  
pp. 939-946
Author(s):  
José Molano-Pulido ◽  
Claudia Jiménez-Guarín
Keyword(s):  
Data Streams ◽  
Daily Living ◽  
Sensor Data ◽  
Daily Living Activity

scholarly journals Ultrasound study of the motion of the residual femur within a trans-femoral socket during daily living activities other than gait

2001 ◽  
Vol 25 (3) ◽  
pp. 220-227 ◽  
Author(s):  
P. Convery ◽  
K. D. Murray
Keyword(s):  
Daily Living ◽  
Ultrasound Image ◽  
Standing Position ◽  
Daily Living Activities ◽  
Motion Patterns ◽  
Double Support ◽  
Daily Living Activity ◽  
Ultrasound Study ◽  
Flexion Extension ◽  
The Right

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.

Fall Detection and Daily Living Activity Recognition Logic Regression

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.

Daily living activity recognition based on statistical feature quality group selection

2012 ◽  
Vol 39 (9) ◽  
pp. 8013-8021 ◽  
Author(s):  
Oresti Banos ◽  
Miguel Damas ◽  
Hector Pomares ◽  
Alberto Prieto ◽  
Ignacio Rojas
Keyword(s):  
Activity Recognition ◽  
Daily Living ◽  
Group Selection ◽  
Statistical Feature ◽  
Daily Living Activity
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