scholarly journals Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds

2021 ◽  
Vol 9 ◽  
Author(s):  
Lin Yu ◽  
Qichang Mei ◽  
Liangliang Xiang ◽  
Wei Liu ◽  
Nur Ikhwan Mohamad ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Loading Rate ◽  
Reaction Force ◽  
Principal Component ◽  
Component Analysis ◽  
Heel Strike ◽  
Impact Peak ◽  
Reaction Forces ◽  
The Difference ◽  
Second Peak

Ground reaction force (GRF) is a key metric in biomechanical research, including parameters of loading rate (LR), first impact peak, second impact peak, and transient between first and second impact peaks in heel strike runners. The GRFs vary over time during stance. This study was aimed to investigate the variances of GRFs in rearfoot striking runners across incremental speeds. Thirty female and male runners joined the running tests on the instrumented treadmill with speeds of 2.7, 3.0, 3.3, and 3.7 m/s. The discrete parameters of vertical average loading rate in the current study are consistent with the literature findings. The principal component analysis was modeled to investigate the main variances (95%) in the GRFs over stance. The females varied in the magnitude of braking and propulsive forces (PC1, 84.93%), whereas the male runners varied in the timing of propulsion (PC1, 53.38%). The female runners dominantly varied in the transient between the first and second peaks of vertical GRF (PC1, 36.52%) and LR (PC2, 33.76%), whereas the males variated in the LR and second peak of vertical GRF (PC1, 78.69%). Knowledge reported in the current study suggested the difference of the magnitude and patterns of GRF between male and female runners across different speeds. These findings may have implications for the prevention of sex-specific running-related injuries and could be integrated with wearable signals for the in-field prediction and estimation of impact loadings and GRFs.

A principal component analysis (PCA) based assessment of the gait performance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Marija Gavrilović ◽  
Dejan B. Popović
Keyword(s):  
Principal Component Analysis ◽  
Motor Impairment ◽  
Principal Component ◽  
Component Analysis ◽  
Ground Reaction Forces ◽  
Gait Performance ◽  
Reaction Forces ◽  
Measurement Units ◽  
The Difference ◽  
User Friendly

Abstract The gait assessment is instrumental for evaluating the efficiency of rehabilitation of persons with a motor impairment of the lower extremities. The protocol for quantifying the gait performance needs to be simple and easy to implement; therefore, a wearable system and user-friendly computer program are preferable. We used the Gait Master (instrumented insoles) with the industrial quality ground reaction forces (GRF) sensors and 6D inertial measurement units (IMU). WiFi transmitted 10 signals from the GRF sensors and 12 signals from the accelerometers and gyroscopes to the host computer. The clinician was following in real-time the acquired data to be assured that the WiFi operated correctly. We developed a method that uses principal component analysis (PCA) to provide a clinician with easy to interpret cyclograms showing the difference between the recorded and healthy-like gait performance. The cyclograms formed by the first two principal components in the PCA space show the step-to-step reproducibility. We suggest that a cyclogram and its orientation to the coordinate system PC1 vs. PC2 allow a simple assessment of the gait. We show results for six healthy persons and five patients with hemiplegia.

scholarly journals Deteksi Formalin Pada Buah Tomat (Lycopersicum esculentum Mill) Dengan Teknologi Hidung Elektronik (Electronic Nose)

2019 ◽  
Vol 4 (2) ◽  
pp. 359-366
Author(s):  
Irfan Maibriadi ◽  
Ratna Ratna ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Lycopersicum Esculentum ◽  
Olfactory Ability ◽  
Pca Method ◽  
The Difference ◽  
Electronic Nose Technology

Abstrak,  Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on  the olfactory ability can distinguish them. 

RECOGNITION OF SMILING FACES USING NEURAL NETWORKS AND SPCA

2004 ◽  
Vol 04 (02) ◽  
pp. 153-163 ◽  
Author(s):  
MIYOKO NAKANO ◽  
FUMIKO YASUKATA ◽  
MINORU FUKUMI
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
High Speed ◽  
Principal Component ◽  
Component Analysis ◽  
Research Field ◽  
A Value ◽  
Large Matrix ◽  
The Difference ◽  
The Cost

Research on "man-machine interface" has increased in many fields of engineering and its application to facial expressions recognition is expected. The eigenface method by using the principal component analysis (PCA) is popular in this research field. However, it is not easy to compute eigenvectors with a large matrix if the cost of calculation when applying it for time-varying processing is taken into consideration. In this paper, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos θ is calculated using an eigenvector by SPCA as well as a gray-scale image vector of each picture pattern. By using neural networks (NNs), the difference in the value of cos θ between the true and the false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smiles, computer simulations are done with real images. Furthermore, an experiment using the self-organisation map (SOM) is also conducted as a comparison.

Regional Education Development Research Based on Robust Principal Component Analysis

2014 ◽  
Vol 915-916 ◽  
pp. 1361-1366
Author(s):  
Xian Fen Xie ◽  
Bin Hui Wang
Keyword(s):  
Principal Component Analysis ◽  
Basic Education ◽  
Principal Component ◽  
Component Analysis ◽  
Robust Principal Component Analysis ◽  
Education Development ◽  
Comparison Research ◽  
Regional Population ◽  
The Difference ◽  
Regional Education

Education development is the product of endogenous socio-economic; studying on regional differences of education level plays an important role in social and economic development. This paper constructs regional education development index system based on two aspects of basic educational facilities and educational scale, applies robust principal component analysis method to explore education development level differences of China's 31 provinces, and with the traditional principal component analysis for comparison. Research shows that, results obtained by robust principal component analysis is more in line with China's actual situation; the overall level of education is not high and the difference between regions is large; China's basic education is positively correlated with regional economy, while inversely correlated with regional population.

scholarly journals Principal Component Analysis applied directly to Sequence Matrix

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tomokazu Konishi ◽  
Shiori Matsukuma ◽  
Hayami Fuji ◽  
Daiki Nakamura ◽  
Nozomi Satou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sequence Data ◽  
Principal Component ◽  
Environmental Dna ◽  
Distance Matrix ◽  
Component Analysis ◽  
Sequence Motifs ◽  
Tree Shape ◽  
Asiatic Lion ◽  
The Difference

AbstractSequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.

scholarly journals Pengembangan Metode Klasifikasi Biji Kopi Beras Arabika Gayo dan Robusta Gayo dengan Metode PCA (Principal Component Analysis) Berdasarkan Pengolahannya

2020 ◽  
Vol 4 (4) ◽  
pp. 472-481
Author(s):  
Ilka Agusti Febriyansyah ◽  
Rahmat Fadhil ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Coffee Bean ◽  
Wet Process ◽  
Robusta Coffee ◽  
Coffee Beans ◽  
Ft Ir ◽  
The Difference ◽  
Spectrum Data

Abstrak. Kopi merupakan salah satu tanaman yang telah banyak dibudidayakan karena memiliki manfaat dan memiliki nilai jual yang cukup tinggi. Pengolahan kopi secara basah dapat dilakukan dengan dua cara yaitu dengan cara basah (full wash)  dan semi basah (semi wash). Secara visual sulit mengidentifikasi perbedaan dari biji kopi beras robusta proses basah (full wash) dengan kopi semi basah (semi wash). Tujuan yang ingin dicapai dalam  penelitian  ini adalah untuk membangun metode klasifikasi kopi Arabika Gayo dan Robusta Gayo dalam bentuk biji kopi beras menggunakan pengolahan basah (full wash) dan pengolahan semi basah (semi wash). Bahan yang digunakan dalam penelitian ini biji kopi beras Arabika dan Robusta dari tanah Gayo. Penelitian ini menggunakan Principal Component Analysis (PCA) sebagai metode pengolah data spektrum. Pengukuran spektrum kopi menggunakan Self developed FT-IR IPTEK T-1516. Panjang gelombang yang digunakan pada penelitian ini antara 1000-2500 nm dengan interval 0.4 nm. Data spektrum diolah menggunakan unscrambler software® X version 10.1. Hasil penelitian menunjukkan bahwa NIRS dengan metode PCA juga mampu mengklasifikasikan biji kopi beras full wash dengan semi wash pada biji kopi Arabika dan Robusta dimana zat dominan pembeda adalah asam amino dan lemak.Development of Gayo Arabica and Robusta Gayo Arabica Coffee Bean Classification Methods with PCA( Principal Component Analysis) Method Based on ProcessingAbstract. Coffee is a plant that has been widely cultivated because it has benefits and has a high selling value. Wet coffee processing can be done in two ways, namely by means of wet (full wash) and semi-wet (semi wash). It is visually difficult to identify the difference between the wet process robusta coffee beans (full wash) and semi-wash coffee. The aim of this research is to develop a method of classifying Arabica Gayo and Robusta Gayo coffee in the form of rice coffee beans using wet wash (full wash) and semi wash. The material used in this study was Arabica and Robusta rice coffee beans from Gayo soil. This study uses Principal Component Analysis (PCA) as a method for processing spectrum data. The measurement of coffee spectrum uses Self-developed FT-IR IPTEK T-1516. Wavelengths used in this study are between 1000-2500 nm with 0.4 nm intervals. Spectrum data are processed using unscrambler software® X version 10.1. The results showed that NIRS with PCA method was also able to classify full wash coffee beans with semi wash in Arabica and Robusta coffee beans where the dominant differentiating substances were amino acids and fats.

Principal Component Analysis of Processing Properties of Light Wool Fabrics

2011 ◽  
Vol 175-176 ◽  
pp. 539-544
Author(s):  
Tong Hong Xu ◽  
Ping Gu ◽  
Hong Sun
Keyword(s):  
Principal Component Analysis ◽  
Comprehensive Evaluation ◽  
Testing Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Wool Fabrics ◽  
The Difference ◽  
Processing Properties

In order to compare the difference of processing properties between domestic and abroad light wool fabrics, the paper selects 35 kinds of light worsted fabrics by using FAST testing machine and principal component analysis to make a comprehensive evaluation. The results indicate that processing properties of domestic and abroad light wool fabrics can be represented by using seven principal component indexs, such as formability, hygral expansion, thickness, relaxation shrinkage.

A robust anomaly detection algorithm based on principal component analysis

2021 ◽  
Vol 25 (2) ◽  
pp. 249-263
Author(s):  
Yingkun Huang ◽  
Weidong Jin ◽  
Zhibin Yu ◽  
Bing Li
Keyword(s):  
Principal Component Analysis ◽  
Anomaly Detection ◽  
Principal Direction ◽  
Recognition Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Absolute Deviation ◽  
Detection Techniques ◽  
The Difference

Quantifying the abnormal degree of each instance within data sets to detect outlying instances, is an issue in unsupervised anomaly detection research. In this paper, we propose a robust anomaly detection method based on principal component analysis (PCA). Traditional PCA-based detection algorithms commonly obtain a high false alarm for the outliers. The main reason is that ignores the difference of location and scale to each component of the outlier score, this leads to the cumulated outlier score deviates from the true values. To address the issue, we introduce the median and the Median Absolute Deviation (MAD) to rescale each outlier score that mapped onto the corresponding principal direction. And then, the true outlier scores of instances can be obtained as the sum of weighted squares of the rescaled scores. Also, the issue that the assignment of the weight for each outlier score will be solved. The main advantage of our new approach is easy to build with unsupervised data and the recognition performance is better than the classical PCA-based methods. We compare our method to the five different anomaly detection techniques, including two traditional PCA-based methods, in our experiment analysis. The experimental results show that the proposed method has a good performance for effectiveness, efficiency, and robustness.

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