Acceleration Factor Modeling of Flexible Electronic Substrates From Actual Human Body Measurements

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Vikas Yadav ◽  
Jinesh Narangaparambil ◽  
Wei Liu
Keyword(s):  
Human Body ◽  
Flexible Electronics ◽  
Human Motion ◽  
Acceleration Factor ◽  
Body Motion ◽  
Motion Data ◽  
Factor Modeling ◽  
Electronic Substrates ◽  
Resistance Data ◽  
Full Body

Abstract The use of flexible electronics wearable applications has prompted the need to understand the stresses imposed during human motion for a range of activities. Wearable applications may involve situations in which the electronics may be flexed-to-install, stretched or subjected to thousands cycles of dynamic flexing. In order to develop meaningful test-levels, a better understanding is needed of the use-cases, variance, and the acceleration factors. In this study, the human body motion data for walking, jumping, squats, lunges, and bicep curls were measured using a set of ten Vicon cameras to measure the position, velocity, and accelerations of a standard full-body sensor location of the human body. In addition, reliability data has been gathered on test vehicles subjected to dynamic flexing. Continuous resistance data have been gathered on circuits subjected to dynamic flexing till failure for some of the commonly used trace geometries in electronic circuits. Experimental measurements during the accelerated tests of the boards were combined with the human body motion data to model the acceleration factor for different human activities based on the flexing angles. Human motion for multiple subjects and multiple joints has been correlated to the test levels for the development of acceleration factors. Statistical analysis on the variation of the joint angles with hypothesis testing has been conducted for different subjects and for different human body actions. Acceleration factors models have been developed for walking, jumping, squats, lunges, and bicep curls.

Acceleration Factors for Flexible Electronics in Wearable Applications From Actual Human Body Measurements

2019 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Vikas Yadav ◽  
Jinesh Narangaparambil ◽  
Wei Liu
Keyword(s):  
Human Body ◽  
Flexible Electronics ◽  
Acceleration Factor ◽  
Body Motion ◽  
Joint Angles ◽  
Motion Data ◽  
Testing Strategies ◽  
Movement Characteristics ◽  
Full Body

Abstract The increase in use of flexible electronics in wearable applications has prompted in analyzing the movement characteristics of human body under various day to day actions. The flexible electronics that are attached on the human body were tested for reliability under various conditions of human activity such as walking, jumping, squats, lunges and bicep curls. The human body motion data during these different actions were measured using a set of ten Vicon cameras to measure the position, velocity and accelerations of a standard full body sensor location of a human body. The reliability model presented in this study uses the angle variations of each joint in the human body for all the five human activities listed above. Statistical analysis on the variation of each joint angles were tested with hypothesis testing strategies with different subjects and with different human body actions as well. Acceleration factor modelling on the reliability of the electronics were carried out using test data of flexible electronics subjected to bending, twisting, stretching and folding experiments. These experiments were conducted on flexible electronics till failure with in-situ resistance measurements to monitor the changes in the board during each of these experiments. The experimental measurements of the boards were combined with the human body motion data to model the acceleration factor for each of these tests.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals Sonifying the Shape of Human Body Motion using Motiongrams

2013 ◽  
Vol 8 (2) ◽  
pp. 73 ◽  
Author(s):  
Alexander Refsum Jensenius ◽  
Rolf Inge Godøy
Keyword(s):  
Human Body ◽  
Video Recording ◽  
Visual Display ◽  
Human Motion ◽  
Visual Similarity ◽  
Body Motion ◽  
Auditory Displays ◽  
Musical Sound ◽  
Starting Point ◽  
Spatial Shape

<p class="author">The paper presents sonomotiongram, a technique for the creation of auditory displays of human body motion based on motiongrams. A motiongram is a visual display of motion, based on frame differencing and reduction of a regular video recording. The resultant motiongram shows the spatial shape of the motion as it unfolds in time, somewhat similar to the way in which spectrograms visualise the shape of (musical) sound. The visual similarity of motiongrams and spectrograms is the conceptual starting point for the sonomotiongram technique, which explores how motiongrams can be turned into sound using &ldquo;inverse FFT&rdquo;. The paper presents the idea of shape-sonification, gives an overview of the sonomotiongram technique, and discusses sonification examples of both simple and complex human motion.</p>

Computer Simulation of Lower Extremities Movement during Stair Climbing

2013 ◽  
Vol 330 ◽  
pp. 407-411 ◽  
Author(s):  
Vesna Raspudić
Keyword(s):  
Human Body ◽  
Human Locomotion ◽  
Great Difficulty ◽  
Human Motion ◽  
Body Motion ◽  
Metabolic Energy ◽  
Stair Climbing ◽  
Interactive Methods ◽  
Limb Amputation ◽  
Magnetic Markers

Tracking of human body motion is applied in many fields, such as virtual reality, clinical biomechanics, the study of man-machine-environment relationship, the analysis of sports movements, etc. Nowadays, the preferred approach to tracking human body motion is based on the use of appropriate optical or magnetic markers, which are placed on specific landmark points, and real-time estimating of their spatial coordinates. With the improvements introduced in computerized monitoring of human motion kinematics, it is important to emphasize the significance of combining motion capture data with commercial CAD packages. The aim of this research was to develop new interactive methods in creating virtual models within the highly sophisticated CAD computer technologies, as well as computer simulations for analyzing the various forms of human locomotion. Within this research, special attention is focused on the study of locomotion when climbing stairs, as an activity that requires large amount of metabolic energy, and thus represents great difficulty in performing daily activities for people with disorders of the musculoskeletal system, and particularly for people with lower limb amputation.

scholarly journals A Specific Algorithm Based on Motion Direction Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhesen Chu ◽  
Min Li
Keyword(s):  
Human Body ◽  
Body Position ◽  
Motion Vector ◽  
Human Motion ◽  
Generative Model ◽  
Experimental Results ◽  
Body Motion ◽  
Motion Vectors ◽  
Motion Direction ◽  
Current Frame

In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.

LOG-TRACKER: AN ATTRIBUTE-BASED APPROACH TO TRACKING HUMAN BODY MOTION

1991 ◽  
Vol 05 (03) ◽  
pp. 439-458 ◽  
Author(s):  
WARREN LONG ◽  
YEE-HONG YANG
Keyword(s):  
Human Body ◽  
Moving Objects ◽  
Human Motion ◽  
The Body ◽  
Body Motion ◽  
Body Parts ◽  
Short Period ◽  
Recognition Of Objects ◽  
Analysis System ◽  
Key Steps

Motion provides extra information that can aid in the recognition of objects. One of the most commonly seen objects is, perhaps, the human body. Yet little attention has been paid to the analysis of human motion. One of the key steps required for a successful motion analysis system is the ability to track moving objects. In this paper, we describe a new system called Log-Tracker, which was recently developed for tracking the motion of the different parts of the human body. Occlusion of body parts is termed a forking condition. Two classes of forks as well as the attributes required to classify them are described. Experimental results from two gymnastics sequences indicate that the system is able to track the body parts even when they are occluded for a short period of time. Occlusions that extend for a long period of time still pose problems to Log-Tracker.

scholarly journals Incremental learning of full body motion primitives and their sequencing through human motion observation

2011 ◽  
Vol 31 (3) ◽  
pp. 330-345 ◽  
Author(s):  
Dana Kulić ◽  
Christian Ott ◽  
Dongheui Lee ◽  
Junichi Ishikawa ◽  
Yoshihiko Nakamura
Keyword(s):  
Incremental Learning ◽  
Human Motion ◽  
Body Motion ◽  
Motion Primitives ◽  
Full Body

scholarly journals An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Chengyu Guo ◽  
Jie Liu ◽  
Xiaohai Fan ◽  
Aihong Qin ◽  
Xiaohui Liang
Keyword(s):  
Low Cost ◽  
Recognition Rate ◽  
Recognition Task ◽  
Graph Model ◽  
Autoregressive Process ◽  
Human Motion ◽  
Lower Limbs ◽  
Body Motion ◽  
Fusion Model ◽  
Full Body

This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM) and autoregressive process, a predictive fusion model (PFM) is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

scholarly journals Analysis and synthesis of function data of human movement

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yunlong Ma ◽  
Sanaa Sharaf ◽  
Basel Jamal Ali
Keyword(s):  
Human Body ◽  
Recognition Rate ◽  
Human Movement ◽  
Human Motion ◽  
Motion Data ◽  
Periodic Data ◽  
Analysis And Synthesis ◽  
Capture Method ◽  
Operational Data

Abstract The article proposes a human motion capture method based on operational data. The thesis first uses the human body wear system to perform functional processing on the captured periodic motion data, and then extracts the data sequence for the few motions. Thereafter, the classification of the vector calculation method is carried out according to the characteristics of periodic data. Through experimental research, it is found that the functional data analysis (FDA) algorithm proposed in the thesis can accurately identify human motion behaviour, and the automatically collected data has a recognition rate that is as high as 98.9%. Therefore, we have concluded that the human body data functional analysis algorithm has higher recognition accuracy than the traditional optical capture system. Thus, it is worthy of further research and discussion.

Human Body Motion Capture System Based on Inertia Sensing Technology

2013 ◽  
Vol 722 ◽  
pp. 454-458
Author(s):  
Shu Ai Li ◽  
Yong Sheng Wang ◽  
Rui Pai Xiang
Keyword(s):  
Sensor Network ◽  
Motion Capture ◽  
Human Body ◽  
The Body ◽  
Body Motion ◽  
Network Nodes ◽  
Motion Data ◽  
Time Motion ◽  
Sensing Technology ◽  
Reconstruction Software

To solve the bottleneck problem of defining motion trajectory of virtual role in animation creation process, this paper presents a solution of mechanical human body motion capture technology, mainly involving inertia sensing technology, Bluetooth, the design of sensor network nodes and the development of reconstruction software of human body motion model. The system uses sensor network to collect motion data of the body key joints, and the data are delivered to workstation through Bluetooth, the software on workstation uses analytical inverse kinematics algorithm to analyze the motion data. So the system has advantages of lower cost and high precision. Meanwhile, the paper also provides a solid foundation for the research of multiplayer real-time motion capture technology.

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