scholarly journals Statistical Analysis of Human Body Movement and Group Interactions in Response to Music

2009 ◽  
pp. 399-408 ◽  
Author(s):  
Frank Desmet ◽  
Marc Leman ◽  
Micheline Lesaffre ◽  
Leen De Bruyn
Keyword(s):  
Statistical Analysis ◽  
Human Body ◽  
Body Movement ◽  
Group Interactions

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Biomechanics of human walking and stability descriptive parameters

2016 ◽  
Vol 2 (1) ◽  
pp. 4
Author(s):  
Arturo Bertomeu-Motos
Keyword(s):  
Human Body ◽  
Center Of Pressure ◽  
Body Movement ◽  
Human Motion ◽  
Human Walking ◽  
Dynamic Motion ◽  
Mechanical Aspect ◽  
Zero Moment

From the time of Aristotle onward, there have been countless books written on the topic of movement in animals and humans. However, research of human motion, especially walking mechanisms, has increased over the last fifty years. The study of human body movement and its stability during locomotion involves both neuronal and mechanical aspect. The mechanical aspect, which is in the scope of this thesis, requires knowledge in the field of biomechanics. Walking is the most common maneuver of displacement for humans and it is performed by a stable dynamic motion. In this article it is introduced the bases of the human walking in biomechanical terms. Furthermore, two stability descriptive parameters during walking are also explained - Center of Pressure (CoP) and Zero-Moment Pint (ZMP).

scholarly journals Human sweat monitoring using polymer-based fiber

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Taekyung Lim ◽  
Youngseok Kim ◽  
Sang-Mi Jeong ◽  
Chi-Hyeong Kim ◽  
Seong-Min Kim ◽  
...  
Keyword(s):  
Sodium Chloride ◽  
Human Body ◽  
Pure Water ◽  
Body Movement ◽  
Wearable Sensors ◽  
Chloride Concentration ◽  
Chloride Ions ◽  
Coagulation Bath ◽  
Future Research ◽  
Simple Geometry

AbstractLightweight nano/microscale wearable devices that are directly attached to or worn on the human body require enhanced flexibility so that they can facilitate body movement and overall improved wearability. In the present study, a flexible poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) fiber-based sensor is proposed, which can accurately measure the amount of salt (i.e., sodium chloride) ions in sweat released from the human body or in specific solutions. This can be performed using one single strand of hair-like conducting polymer fiber. The fabrication process involves the introduction of an aqueous PEDOT:PSS solution into a sulfuric acid coagulation bath. This is a repeatable and inexpensive process for producing monolithic fibers, with a simple geometry and tunable electrical characteristics, easily woven into clothing fabrics or wristbands. The conductivity of the PEDOT:PSS fiber increases in pure water, whereas it decreases in sweat. In particular, the conductivity of a PEDOT:PSS fiber changes linearly according to the concentration of sodium chloride in liquid. The results of our study suggest the possibility of PEDOT:PSS fiber-based wearable sensors serving as the foundation of future research and development in skin-attachable next-generation healthcare devices, which can reproducibly determine the physiological condition of a human subject by measuring the sodium chloride concentration in sweat.

Electric Wheelchair Controlled by Human Body Motion -Classification of Body Motion and Improvement of Control Method-

2010 ◽  
Vol 22 (4) ◽  
pp. 439-446 ◽  
Author(s):  
Sho Yokota ◽  
◽  
Hiroshi Hashimoto ◽  
Yasuhiro Ohyama ◽  
Jinhua She ◽  
...  
Keyword(s):  
Pressure Distribution ◽  
Human Body ◽  
Control Method ◽  
Body Movement ◽  
Single Point ◽  
Movement Pattern ◽  
Body Motion ◽  
Self Organizing Map ◽  
Electric Wheelchair ◽  
Wheelchair Control

This paper classifies human body movements when an electric wheelchair was controlled using a Human Body Motion Interface (HBMI) by a Self-Organizing Map (SOM) and proposes control based on classification results. The Human Body Motion Interface (HBMI) uses body movement following voluntary motion. This study focuses on electric wheelchair control as an application of the HBMI. The viability of the HBMI was confirmed using Center Of Weight (C.O.W.) from pressure distribution information on backrest in the wheelchair to control it. If body movement concentrated on a single point at C.O.W. in pressure distribution, a problem occurred because the system would recognize even different body-movement patterns as the same movement. We call body movement taking the same C.O.W. even if it has a different body-movement pattern movement confusion. We solve the movement confusion problem and enhance wheelchair control, classifying body movement using the SOM and reflecting this classification result to improve wheelchair control. Experimental results showed that movement confusion is solved and wheelchair control improved.

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