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.

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.

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.

scholarly journals An HMM-based averaging approach for creating mean motion data from a full-body Motion Capture system to support the development of a biomechanical model

2018 ◽  
Vol 4 (1) ◽  
pp. 389-393
Author(s):  
Andreas Kitzig ◽  
Julia Demmer ◽  
Tobias Bolten ◽  
Edwin Naroska ◽  
Gudrun Stockmanns ◽  
...  
Keyword(s):  
Motion Capture ◽  
Hidden Markov ◽  
Biomechanical Model ◽  
Human Movement ◽  
Body Motion ◽  
Game Development ◽  
Motion Data ◽  
Motion Sequence ◽  
Motion Capture Systems ◽  
Full Body

AbstractMotion capture systems or MoCap systems are used for game development and in the field of sports for the assessment and digitalization of human movement. Furthermore, MoCap systems are also used in the medical and therapeutic field for the analysis of human movement patterns. As examples gait analysis or examination of the musculoskeletal system and its function should be mentioned. Most application relate to a specific person and their movement or to the comparison of movements of different people. Within the scope of this paper an averaged motion sequence is supposed to be generated from MoCap data in order to be able to use it in the field of biomechanical modeling and simulation. For the averaging of individual movement sequences of different persons a Hidden Markov Model (HMM) based approach is presented.

Research on athlete’s action recognition based on acceleration sensor and deep learning

2020 ◽  
pp. 1-12
Author(s):  
Xiao Geng
Keyword(s):  
Human Body ◽  
Simulation Analysis ◽  
Recognition Algorithm ◽  
Body Motion ◽  
Computer Hardware ◽  
Quality Analysis ◽  
Experimental Comparison ◽  
Acceleration Sensor ◽  
Motion Recognition ◽  
Motion Data

Due to the difficulty of athletes’ motion recognition, there are few studies on athletes’ specific motion recognition. Based on this, this study uses the acceleration sensor as the carrier, and uses human-computer interaction to transform the action of the athlete into a machine-identifiable action unit. At the same time, this paper combines the actual situation of human body motion to construct a human body motion model and builds a corresponding computer hardware and software platform. Moreover, this paper designs a classification recognition algorithm that can recognize the movement of athletes and builds SVM model based on machine learning for classification and recognition. In addition, in this study, the effectiveness of the algorithm was studied through experimental comparison. Finally, the simulation analysis was carried out to obtain the corresponding research results, and the results were analyzed by combing statistics. The research shows that the proposed algorithm can classify and recognize the collected motion data, and it has certain effects on the theoretical analysis of athletes’ motion recognition. Moreover, the algorithm can perform motion quality analysis and provide theoretical reference for subsequent related research.

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.

Recyclable conductive nanoclay for direct in situ printing flexible electronics

2021 ◽  
Author(s):  
Pengcheng Wu ◽  
Zhenwei Wang ◽  
Xinhua Yao ◽  
Jianzhong Fu ◽  
Yong He
Keyword(s):  
Flexible Electronics ◽  
Self Healing ◽  
Stamping Process

A recyclable, self-healing conductive nanoclay and corresponding stamping process are developed for printing flexible electronics directly and quickly in situ.

Laboratory Testing for HER2/neu in Breast Carcinoma: An Evolving Strategy to Predict Response to Targeted Therapy

2001 ◽  
Vol 8 (5) ◽  
pp. 415-418 ◽  
Author(s):  
Nils M. Diaz
Keyword(s):  
Targeted Therapy ◽  
Breast Carcinoma ◽  
Gene Amplification ◽  
Laboratory Testing ◽  
False Positives ◽  
Response To Therapy ◽  
Fish Analysis ◽  
Breast Carcinomas ◽  
Testing Strategies

Background Laboratory testing of HER2/neu in breast carcinoma has become vital to patient care following the approval of trastuzumab as the first therapy to target the HER2/neu oncoprotein. Initial clinical trials used immunohistochemistry (IHC) to test for HER2/neu overexpression in order to select patients for therapy. Fluorescence in situ hybridization (FISH), which tests for gene amplification, is more specific and sensitive than IHC when either assay is compared with HER2/neu overexpression as determined by Northern or Western blot analysis. Many weak overexpressors on IHC testing are not gene amplified on FISH analysis. Such weak overexpressors may be considered false-positives and raise the question of how best to test for HER2/neu. Methods The literature was surveyed regarding testing for HER2/neu overexpression in breast carcinomas and alternative testing strategies. Results False-positive results are a significant problem when IHC is exclusively used to test for HER2/neu overexpression. The false-positives are overwhelmingly confined to the group of 2+ positives and do not respond to targeted therapy. In contrast, concordance between IHC and FISH is high when immunostaining is interpreted as either negative or strongly positive (3+). Whereas some recent studies have suggested that FISH may better predict response to anti-HER2/neu therapy than IHC, others have indicated that IHC is as effective a predictor as FISH. IHC is less technically demanding and costly than FISH. Conclusions IHC analysis of HER2/neu in breast carcinoma is a useful predictor of response to therapy with trastuzumab when strongly positive. Negative immunostaining is highly concordant with a lack of gene amplification by FISH. Most weakly positive overexpressors are false-positives on testing with FISH. Thus, screening of breast carcinomas with IHC and confirmation of weakly positive IHC results by FISH is an effective evolving strategy for testing HER2/neu as a predictor of response to targeted therapy.

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