Clustering analysis of human finger grasping based on SOM neural network model

SOM (Self-organizing Maps) model was introduced to cluster and analyse on the human grasping activities of GloveMAP based on data reduction of the initial grasping data.By acquiring the data reduction of the initial hand grasping data of the several objects, it will be going to be functioned as the inputs to the SOM model.After the iterative learning of net-trained, all data of the trained network will be simulated and finally self-organized.The output results of models’ are farthest approached to the reality in 3-dimensional grasping features.The experimental result of the simulation signal will generate the simulate result of the grasping features from the selected object.The whole experiment of grasping features is derived into three features/groups and the results are satisfactory

PCA-based finger movement and grasping classification using data glove “Glove MAP”

nowadays, fingers movement and hand gestures can be used as main activities in translating by naturally and convenient way to the human computer interaction.The purpose of this paper is to analyze in depth the thumb, index and middle fingers on the hand grasping movement against an object.The classification of the fingers activities is analyzed using the statistical analysis method. Principal Component Analysis (PCA) is one of the methods that able to reduce the dimensional dataset of hand motion as well as measure the capacity of the fingers movement.The fingers movement is estimated from the bending representative of proximal and intermediate phalanges of thumb, index and middle fingers. The effectiveness of the propose assessment analysis were shown through the experiments of three fingers motions.Preliminary results of this experiment showed that the use of the first and second principal components can allow distinguishing between three fingers grasping movements

Experimental and analysis study on GloveMAP grasping force signal using Gaussian filtering method and principal component analysis (PCA)

This research paper presents the analysis study of human grasping forces for several objects by using a DataGlove called GloveMAP.The grasping force is generated from the bending of proximal and intermediate phalanges of the fingers when touching with a surface.A flexiforce sensor is installed at the finger’s position of the GloveMAP.The acquired grasping force signals are filtered by using a Gaussian filtering for the purpose of removing noises.A Principal Component Analysis technique (PCA) is employed to reduce the dimension of the grasping force signal, and follows by the extraction of its features.In the experiment, five subjects are selected to perform the grasping activities.The experimental results show that the Gaussian filter could be used to smoothen the grasping force signals. Moreover, the first and the second principal components of PCA could be used to extract features of grasping force signals

Ergonomic risk assessment of manual handling tools by oil palm collectors and loaders

Oil palm workers are exposed to ergonomics problems in their routine works. Although many technological advances have been developed, a large number of workers are still using manual handling tools in their daily work. A study was done to identify and solve the problems or issues of material handling effect on oil palm collectors and loaders during their daily work activities. A cross sectional study was done in an oil palm plantation in Negeri Sembilan, Malaysia. Twenty five workers were selected randomly to participate in this study. Musculoskeletal symptoms were recorded using Modified Nordic Questionnaires and awkward postures of the workers were assessed using Rapid Entire Body Assessment (REBA). Result showed that 61% of workers were exposed to high risk level and 39% to very high risk level of working posture problems. In conclusion, majority of oil palm collectors and loaders need to correct their working posture as soon as possible. The manual handling activities need to be improved with respect to correct procedure for health and safety concerns

Learning and manipulating human's fingertip bending data for sign language translation using PCA-BMU classifier

Nowadays the classification of fingers movement could be used to classify or categorize many kinds of human finger motions including the classification of sign language for verbal communication.Principal Component Analysis (PCA) is one of classical method that capable to be verity the finger motions for various alphabets by reducing the dimensional dataset of finger movements.The objective of this paper is to analyze the human finger motions / movements between thumbs,index and middle fingers while bending the fingers using PCA-BMU based techniques. The used of low cost DataGlove “GloveMAP” which is based on fingers adapted postural movement (or EigenFingers) of the principal component was applied in order to translate the finger bending to the sign language alphabets. Preliminary experimental results have shown that the “GloveMAP” DataGlove capable to measure several human Degree of Freedom (DoF), by “translating” them into a virtual commands for the interaction in the virtual world

Performance Assessment of the Optimum Feature Extraction for Upper-limb Stroke Rehabilitation using Angular Separation Method

Most of the human everyday activities will require the use of their upper-limb muscles. The pattern of upper-limb muscle movement can be used to estimate upper-limb motions. Fundamental arm movement which is part of upper-limb muscle rehabilitation activity has been studied in order to investigate the time domain features, frequency domain, and time-frequency domain from the surface electromyogram (sEMG) signal of the upper-limb muscle. The relationship of electromyogram (EMG) signal and the rehabilitation exercise of related upper limb muscles movements are analyzed in this study. Then the features from the three domains were compared using Angular Separation Method to determine optimal feature. The result shows that MinWT has the best value of similarity which is 0.98, followed by a MeanWT feature which resulted in 0.91 of similarity. These results of EMG signal feature extraction can be used later in the study of human upper-limb muscle especially for analyzing EMG signal from patient undergone a rehabilitation treatment

An Experimental Framework for Assessing Emotions of Stroke Patients using Electroencephalogram (EEG)

Abstract: This research aims to assess the emotional experiences of stroke patients using Electroencephalogram (EEG) signals. Since emotion and health are interrelated, thus it is important to analyse the emotional states of stroke patients for neurofeedback treatment. Moreover, the conventional methods for emotional assessment in stroke patients are based on observational approaches where the results can be fraud easily. The observational-based approaches are conducted by filling up the international standard questionnaires or face to face interview for symptom recognition from psychological reactions of patients and do not involve experimental study. This paper introduces an experimental framework for assessing emotions of the stroke patient. The experimental protocol is designed to induce six emotional states of the stroke patient in the form of video-audio clips. In the experiments, EEG data are collected from 3 groups of subjects, namely the stroke patients with left brain damage (LBD), the stroke patients with right brain damage (RBD), and the normal control (NC). The EEG signals exhibit nonlinear properties, hence the non-linear methods such as the Higher Order Spectra (HOS) could give more information on EEG in the signal’s analysis. Furthermore, the EEG classification works with a large amount of complex data, a simple mathematical concept is almost impossible to classify the EEG signal. From the investigation, the proposed experimental framework able to induce the emotions of stroke patient and could be acquired through EEG

Extracting features of fingertips bending by using self-organizing map

In this paper the method of Self-Organizing Maps (SOM) is introduced to analyze the human grasping activities of human fingertips bending using the low cost DataGlove called as GloveMAP.The research shows that the proposed approaches capable to utilize the effectiveness of the SOM for creating the grasping features of the bottle object.After the iterative learning of net-trained, all data of the trained network will be simulated and finally self-organized.The final result of the research study shows the fingertips features extraction were generated from the several grasping activities and verify the validity of the analy sis through simulation with human grasp data captured by a GloveMAP

Probability distribution of arm trajectory for motion estimation and gesture recognition

In the human motion measurement, motion capture system is used to record the movement of the human body by using different types of sensors such as a magnetic position sensor, a mechanical motion detector and a vision sensor. The most challenging task in human motion measurement is to achieve the ability and reliability of a motion capture system for tracking and recognizing dynamic gestures, because human body structure has many degrees of freedom. This paper introduces a 3D motion measurement of the human upper body by using an optical motion capture system for the purpose of the estimation of human upper body motions, which is based on the probability distribution of arm trajectories. In this study, by examining the characteristic of the arm trajectory, motion features are selected and classified by using the fuzzy technique. The posture of the occluded body part is probabilistically estimated by using the aggregation of the fuzzy information of arm trajectories and the constructed human upper body model. Experimental results show that the use of the system effectively works for classifying various motion patterns and estimating the occluded posture in the motion