Efficient Hand Movement Detection Using k-Means Clustering and k-Nearest Neighbor Algorithms

Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques

Collaborative filtering has been very successful in both research and E-commence applications. One of the most popular collaborative filtering algorithms is the k-Nearest Neighbor (KNN) method, which finds k nearest neighbors for a given user to predict his interests. Previous research on KNN algorithm usually suffers from the data sparseness problem, because the quantity of items users voted is really small. The problem is more severe in web-based applications. Cluster-based collaborative filtering has been proposed to solve the sparseness problem by averaging the opinions of the similar users. However, it does not bring consistent improvement on the performance of collaborative filtering since it produces less-personal prediction. In this paper, we propose a clustering-based KNN method, which combines the iterative clustering algorithm and the KNN to improve the performance of collaborative filtering. Using the iterative clustering approach, the sparseness problem could be solved by fully exploiting the voting information first. Then, as a smoothing method to the KNN method, cluster-based KNN is used to optimize the performance of collaborative filtering. The experimental results show that our proposed cluster-based KNN method can perform consistently better than the traditional KNN method and clustering-based method in large-scale data sets.

COVID-19 is a serious disease that spreads rapidly and affects the world. Alternative methods based on machine learning are recommended to diagnose COVID-19 positive and negative cases cheaper and faster. However, as the data size increases, problems such as space requirement or classification time may arise. KNN (K-nearest neighbor), a simple but effective machine learning method, is widely used in various fields. However, the effectiveness of the KNN algorithm decreases considerably when the sample size is large and the number of features is too large. To solve these problems, it is important to use datasets more effectively and to select meaningful parts of the data. The current study proposes an improved neighborhood-based classification method called CURE-NN and compares its performance with standard NN and KNN algorithms. The proposed CURE-NN method obtains reduced structural information from the data by applying clustering before classification to use the dataset more effectively. The resulting reduced structural information was used as a training set in the classification process. The proposed method was applied to the COVID-19 dataset. With this method, while the classification success is preserved as much as possible compared to the NN and KNN methods, the data used in the test phase is reduced by up to 96%. Experimental results show that the reduced data obtained based on structural information can be used instead of the entire data set. In addition, the method works by using only one neighbor, thus eliminating the need for the K parameter compared to the KNN algorithm.

Over the past several years, electromyography (EMG) signals have been used as a natural interface to interact with computers and machines. Recently, deep learning algorithms such as Convolutional Neural Networks (CNNs) have gained interest for decoding the hand movement intention from EMG signals. However, deep networks require a large dataset to train appropriately. Creating such a database for a single subject could be very time-consuming. In this study, we addressed this issue from two perspectives: (i) we proposed a subject-transfer framework to use the knowledge learned from other subjects to compensate for a target subject's limited data; (ii) we proposed a task-transfer framework in which the knowledge learned from a set of basic hand movements is used to classify more complex movements, which include a combination of mentioned basic movements. We introduced two CNN-based architectures for hand movement intention detection and a subject-transfer learning approach. Classifiers are tested on the Nearlab dataset, a sEMG hand/wrist movement dataset including 8 movements and 11 subjects, along with their combination, and on open-source hand sEMG dataset "NinaPro DataBase 2 (DB2)." For the Nearlab database, the subject-transfer learning approach improved the average classification accuracy of the proposed deep classifier from 92.60 to 93.30% when classifier was utilizing 10 other subjects' data via our proposed framework. For Ninapro DB2 exercise B (17 hand movement classes), this improvement was from 81.43 to 82.87%. Moreover, three stages of analysis in task-transfer approach proved that it is possible to classify combination hand movements using the knowledge learned from a set of basic hand movements with zero, few samples and few seconds of data from the target movement classes. First stage takes advantage of shared muscle synergies to classify combined movements, while second and third stages take advantage of novel algorithms using few-shot learning and fine-tuning to use samples from target domain to further train the classifier trained on the source database. The use of information learned from basic hand movements improved classification accuracy of combined hand movements by 10%.

Objective: Apply the GLCM method to select mango leaf feature extraction and determine the accuracy level obtained from the K-Nearest Neighbor classification results.Design/method/approach: Using GLCM and K-Nearest Neighbor(KNN) methods. System development using the Prototype method.Results: The test results have been carried out using as many as 60 mango leaves compared to training data and 80:20 test data, with different accuracy. The highest accuracy is at K = 3 by 81% using 6 features, K = 6 by 78% using 5 features, and K = 7 by 74% using 4 features.Authenticity/state of the art: The difference between this research and previous research is the pre-processing method, the type of features used, and the classification method. In this method, the mango leaf image is converted to grayscale, and a feature extraction process is carried out. Then the results of feature extraction will be classified using the K-Nearest Neighbor method. The output of this system is the result of the image classification of mango leaves, such as Kweni, Lalijowo, and Madu.

Surgical training using 3D virtual reality simulators has become an important routine in medical education. Recent research points to Leap Motion as an exciting interface for the control of virtual surgical instruments due to its simplicity and low-cost characteristics. However, previous studies using Leap Motion only evaluated movements of the whole hand, without considering individual finger movements during the manipulation of surgical instruments. This work investigates the use of Leap Motion as an interface for the capture of basic hand and finger movements during a simulated hysteroscopy using a 3D-printed hysteroscope model. We created a virtual simulated uterine environment containing a hysteroscope controlled by movements of the hand and fingers of a user actuating on a 3D-printed model hysteroscope. The model hysteroscope was positioned in a pivot basis allowing the capture of the following basic movements: leftward/rightward, upward/downward, forward/backward, and extrusion/retraction of the virtual resection loop (which rests on the end of the virtual virtual resectoscope). The findings indicate that the arc-shaped paths of the hysteroscope's alpha plane (rightward/leftward) and beta plane (upward/downward) movements are satisfactorily simulated by the virtual reality system. Using Intraclass Correlation, was noted that the similarity between the calculated (ideal standard) and measured arcs was highly significant on both planes (r = 0.9599 on the alpha plane, and r = 0.9208 on the beta plane). Also, the forward/backward trajectory is a straight line; the pinch gesture decreases its accuracy when increase its distance from the Interaction Box of Leap Motion. The results were satisfactorily since compared with previous works, which used Leap Motion for the capture of hands-free gesturing.

Motor bearings play an important role in industrial production. Regular inspection and fault diagnosis can provide reliable guarantee for the normal operation of the motor. If the potential or early bearing failures are not detected in time, they will bring safety hazards to production operations. In this thesis, distance measure method for feature extraction and KNN (k-nearest neighbor) algorithm for recognition are combined for the purpose of diagnosis of motor bearing. Through experimental verification, the medium KNN algorithm has the most obvious improvement, and the accuracy has increased from 39. 1% to 99. 8% with the help of the proposed feature extraction. Using the KNN algorithm, the effect of time domain feature extraction, frequency domain feature extraction and distance feature extraction are verified. After comparison, it is concluded that the distance feature extraction method can effectively improve the accuracy rate and speed up the training speed of the model.

Decoding finger and hand movements from sEMG electrodes placed on the forearm of transradial amputees has been commonly studied by many research groups. A few recent studies have shown an interesting phenomenon: simple correlations between distal phantom finger, hand and wrist voluntary movements and muscle activity in the residual upper arm in transhumeral amputees, i.e., of muscle groups that, prior to amputation, had no physical effect on the concerned hand and wrist joints. In this study, we are going further into the exploration of this phenomenon by setting up an evaluation study of phantom finger, hand, wrist and elbow (if present) movement classification based on the analysis of surface electromyographic (sEMG) signals measured by multiple electrodes placed on the residual upper arm of five transhumeral amputees with a controllable phantom limb who did not undergo any reinnervation surgery. We showed that with a state-of-the-art classification architecture, it is possible to correctly classify phantom limb activity (up to 14 movements) with a rather important average success (over 80% if considering basic sets of six hand, wrist and elbow movements) and to use this pattern recognition output to give online control of a device (here a graphical interface) to these transhumeral amputees. Beyond changing the way the phantom limb condition is apprehended by both patients and clinicians, such results could pave the road towards a new control approach for transhumeral amputated patients with a voluntary controllable phantom limb. This could ease and extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

Biometric recognition systems or human identification are very important in security access for identification and verification systems. The biometric recognition system can be used as an identification system based on the characteristics possessed by the body part of each individual. The soles of the feet can be used for identification because the soles of the feet have certain and unique characteristics which include major lines, protrusions, small dots, single points, and textures. The introduction of biometrics in babies is still conventional, which is a standard operating procedure such as attaching bracelets on baby's feet and imprinting or inking on the soles of baby's feet which are affixed to paper and are very vulnerable to the risk of damage or loss of data, there is a need for a system that can store data automatically digital and able to do the baby identification process. The Principal Component Analysis method is used for the extraction process of the characteristics of the baby's feet. The classification uses the K-Nearest Neighbor (K-NN) method with the euclidean distance approach. Tests were carried using 120 images of baby feet, there are 20 classes, each class contains 3 images of the right foot and 3 images of the foot of the left foot, and a dataset of 280 training images. The highest accuracy result obtained in system testing is 91% with a computation time of 5.63 seconds using the Principal Component Analysis method with the K-Nearest Neighbor (K-NN) classification.Keywords: Footprint, Feature Extraction, Principal Component Analysis, K-Nearest Neighbor.

Prosthetic plays an important role for the amputees to improve the ability and mobility of their regular activities. Electromyography(EMG) has been used for decades in the control of the motorized upper-limb prosthesis. Processed EMG can imitate human movements. Mayo armband is a wireless sensor of low power, Bluetooth, and small interference which provides a good quality EMG signal. The Myo armband measures the EMG from the upper-limb. In this paper, the statistical time-domain features have been considered to classify different hand movements. The classification and comparison have been performed by 4 different Machine Learning based algorithms i.e. Support Vector Machine(SVM), Naïve Bayes(NB), Random Forest(RF), and K-Nearest Neighbor(KNN). The data has been collected from subjects (males and females) of different ages. The classifier model has used 80% data as a training set and the remaining 20% of data as the test set. The result shows that Random Forest and SVM outperform the other two algorithms with an accuracy of 98 %. Referring to the accuracy here, this classification model serves as a promising candidate for the input of prosthetic hand control systems.

Abstract. This system was developed to determine the accuracy of sentiment analysis on Twitter regarding the COVID-19 issue using the Nazief-Adriani and Porter stemmers with TF-IDF weighting, along with a classification process using K-Nearest Neighbor (KNN) that resulted in a comparison of 48.24% for Nazief-Adriani and 48.24% for Porter. Purpose: This research aims to determine the accuracy of the Nazief-Adriani and Porter stemmer algorithms in performing text preprocessing using a dataset from Indonesian-language Twitter. This research involves word weighting using TF-IDF and classification using the K-Nearest Neighbor (KNN) algorithm. Methods/Study design/approach: The experimentation was conducted by applying the Nazief-Adriani and Porter stemmer algorithm methods, utilizing data sourced from Twitter related to COVID-19. Subsequently, the data underwent text preprocessing, stemming, TF-IDF weighting, accuracy testing of training and testing data using K-Nearest Neighbor (KNN) algorithm, and the accuracy of both stemmers was calculated employing a confusion matrix table. Result/Findings: This study obtained reasonably accurate results in testing the Nazief-Adriani stemmer with an accuracy of 50.98%, applied to sentiment analysis of COVID-19-related Twitter data using the Indonesian language. As for the accuracy of the Porter stemmer, it achieved an accuracy rate of 48.24%. Novelty/Originality/Value: Feature selection is crucial in stemmer accuracy testing. Therefore, in this study, feature selection is carried out using the Nazief-Adriani and Porter stemmers for testing purposes, and the accuracy data classification is conducted using the K-Nearest Neighbor (KNN) algorithm

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

In this study, it is aimed to filter spam e-mails by using machine learning and text mining techniques. K-Nearest Neighbor (KNN) algorithm which is one of the techniques of machine learning is used. KNN algorithm is an easy to use and high performance classification algorithm. But the main problem of this algorithm is what will be the k value at the beginning. The performance of the algorithm changes according to the selected k value. In this study, three different data sets are discussed. These are Enron, Ling-Spam and SMSSpam-Collection data sets. Firstly, basic text mining techniques and term frequency-inverse document frequency (TF-IDF) term weighting method are applied to all data sets. By, according to the Chi-Square feature selection method, the best 500 attributes are selected and given to KNN algorithm. Finally, extensive experiments are carried out by giving the values of 1, 3, 5, 7 and 9 to the k value of the algorithm. In all three data sets, the most successful result is obtained when k is 1. The most successful results obtained from Ling-Spam, Enron and SMSSpam-Collection data sets according to F-measure are 0:9324, 0:9215 and 0:9196 respectively.

It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.

Patients with mild degenerative cervical myelopathy (DCM) represent a heterogeneous population, and indications for surgical decompression remain controversial. To dissociate patient phenotypes within the broader population of mild DCM associated with degree of impairment in baseline quality of life (QOL) and surgical outcomes. This was a post hoc analysis of patients with mild DCM (modified Japanese Orthopedic Association [mJOA] 15-17) enrolled in the AOSpine CSM-NA/CSM-I studies. A k-means clustering algorithm was applied to baseline QOL (Short Form-36 [SF-36]) scores to separate patients into 2 clusters. Baseline variables and surgical outcomes (change in SF-36 scores at 1 yr) were compared between clusters. A k-nearest neighbors (kNN) algorithm was used to evaluate the ability to classify patients into the 2 clusters by significant baseline clinical variables. One hundred eighty-five patients were eligible. Two groups were generated by k-means clustering. Cluster 1 had a greater proportion of females (44% vs 28%, P=.029) and symptoms of neck pain (32% vs 11%, P=.001), gait difficulty (57% vs 40%, P=.025), or weakness (75% vs 59%, P=.041). Although baseline mJOA correlated with neither baseline QOL nor outcomes, cluster 1 was associated with significantly greater improvement in disability (P=.003) and QOL (P<.001) scores following surgery. A kNN algorithm could predict cluster classification with 71% accuracy by neck pain, motor symptoms, and gender alone. We have dissociated a distinct patient phenotype of mild DCM, characterized by neck pain, motor symptoms, and female gender associated with greater impairment in QOL and greater response to surgery.