Continuous Monitoring of Overt Human Body Movement by Radio Telemetry: A Brief Review

1967 ◽  
Vol 24 (3_suppl) ◽  
pp. 1303-1308 ◽  
Author(s):  
R. E. Herron ◽  
R. W. Ramsden
Keyword(s):  
Human Body ◽  
Systematic Study ◽  
Continuous Monitoring ◽  
Radio Telemetry ◽  
Body Movement ◽  
Human Movement ◽  
Major Obstacle ◽  
Analog Signals

Although radio transmission of analog signals has been known for over 100 yr., very few investigators have exploited this approach to the systematic study of overt human movement. The small number of known previous applications are critically reviewed and suggestions made regarding future possibilities. Now that micro-electronic transmitters are commonplace, the only major obstacle to future use of this technique seems to rest with the design of unobtrusive motion transducers which are parsimonious in the acquisition of relevant data.

scholarly journals Accurate Recognition Method of Human Body Movement Blurred Image Gait Features Using Graph Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Yu
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Feature Recognition ◽  
Body Movement ◽  
Human Movement ◽  
Recognition Method ◽  
Gait Features ◽  
Gait Feature ◽  
Blurred Image ◽  
Blurred Images

In view of the problems of low precision, poor quality, and long time of gait feature recognition due to the influence of human body movement environment on the recognition process of the current gait feature recognition method of human body movement blurred image, a new method of gait feature recognition based on graph neural network (GNN) method is proposed. The gait features of human movement blurred images were extracted, and the fusion clustering recognition of the GNN algorithm was used to locate the gait features of human movement blurred images. The gait features of human body movement blurred images were located by the GNN method. According to the contour feature point info of the human body movement blurred image, the standard deviation of gait feature location of the human body movement blurred image was calculated, the gait feature of the blurred image of human body movement was reconstructed, and the gait recognition of the human body movement blurred image was achieved. The results show that the extraction of human movement is good, with high positioning confidence, good recognition quality, average recognition accuracy of 92%, and greatly shortened recognition time.

The Visual Simulation Analysis of Human Body Movement Model

2014 ◽  
Vol 556-562 ◽  
pp. 3913-3916
Author(s):  
Jun Jie Wang
Keyword(s):  
Human Body ◽  
Simulation Analysis ◽  
Body Movement ◽  
Linear Optimization ◽  
Human Movement ◽  
The Body ◽  
Body Parts ◽  
Optimization Strategy ◽  
Matching Accuracy ◽  
Movement Model

This paper proposes the re-built human body movement model with multiple cameras. In the tracking frame of the non-linear optimization strategy, the paper builds the body dynamic model to dynamically simulate the human movement which effectively solves the issues of the body parts overlap and tracking errors accumulate. Compared with traditional methods, the required equipment is very economic and the matching accuracy of the algorithm is quite high. The paper applies the athletes as the experimental examples which illustrate the proposed algorithm can effectively increase the 3D image tracking matching accuracy in dynamic videos as the analysis basis.

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.

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