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

Pembinaan data korpus Bahasa Arab untuk tujuan pengajaran di peringkat STAM / Zainur Rijal B. Abdul Razak...[et al.]

Beberapa kajian dalam bidang linguistik korpus telah membuktikan bahawa data korpus memainkan peranan besar dalam membantu pelajar memahami subjek yang dipelajari. Ini kerana ia dapat memberi maklumat tentang kosa kata dan kata kunci penting dalam subjek tersebut. Tujuan kertas kerja ini disediakan, pertama, adalah untuk menghuraikan langkah yang telah dilalui dalam membina data korpus bagi dua buku teks di peringkat STAM bagi subjek Fiqh dan Hadith. Kedua, mengenal pasti kosa kata penting dan kata kunci dalam dua buku teks tersebut. Bagi mencapai objektif di atas, data yang mengandungi sekitar 124,500 perkataan dipindahkan dalam bentuk softcopy dan dilakukan proses pengkodan. Kemudian data tersebut dianalisis menggunakan perisian Wordsmith 6.0 untuk mendapatkan frekuensi penggunaan perkataan dan kata kunci bagi setiap subjek. Dapatan menunjukkan bahawa satu korpus lebih besar yang merangkumi semua subjek di peringkat STAM mampu dibina. Analisis ke atas korpus mendapati, terdapat beberapa kosa kata penting yang umum digunakan dan kata kunci bagi setiap subjek

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

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

Hemodynamic study on upper extremity: simulation on straight reverse saphenous vein graft

Artery reconstruction in upper extremities is rare performed compare to the incidence of reconstruction in lower extremities. In many cases, primary vascular repair was performed, whenever, otherwise, the interposition vein graft or venous bypass grafting were used in order to alleviate vascular occlusion. However, after grafting technique are applied, one or more of the digital arteries are blocked or severely narrowed because of mismatch of end-to-side or end-to-end reverse saphenous vein graft. The objective of this study was to understand the end-to-end blood flow influence on reverse saphenous vein graft with small diameter. The finite volume method was employed to model the 3-D blood flow pattern to determine the velocity, pressure gradient, flow, wall shear stress, flow resistance and longitudinal impedance (ZL). We expected that reverse saphenous vein graft behave hydraulically like provide straight graft. Furthermore longitudinal impedance modulus (ZL) is expected to be inverse proportional to small diameter

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

Analysis of EMG based Arm Movement Sequence using Mean and Median Frequency

This paper present the studies of analysis arm movement sequence which dedicated for upper limb rehabilitation after stroke. The recovery of the arm could be optimized if the rehabilitation therapy is in a right manner. Upper limb weakness after stroke is prevalent in post-stroke rehabilitation, many factors that can deficit muscle strength there are neural, muscle structure and function change after stroke. Rehabilitation process needs to start as soon as after a stroke attack, repetitive and conceptualized. On the other hand monitoring of muscle activity also need in the rehabilitation process to evaluate muscle strength, motor function and progress in the rehabilitation process. The objective of this research is to analysis arm movement sequence using the feature frequency domain. In this study deltoid, biceps and flexor carpum ulnaris (FCU) muscles will be monitored by surface electromyography (sEMG). Five healthy subjects male and female become participants in data recording. Mean frequency (MNF) and median frequency (MDF) domain are two signals processing technique used for arm movement sequence analyzing. The analysis result showed that MNF is better than MDF where MNF produced higher frequency than MDF from each segment. From the data analysis, this movement sequence design more focuses on deltoid and FCU muscles treatment. This movement sequence has five condition movements. First undemanding, second difficult, third moderate, fourth moderate and the last cool-down movements. The best movement sequence minimum has four condition movements warming up - moderate - difficult - cool-down

Computational fluid dynamic analysis on microvascular vein grafting: effect of mismatched conduit diameters

An artery disease of upper extremity is uncommon happened. The most common cause of artery disease in upper extremity is atherosclerosis. In few patients with artery disease, surgical vein bypassing or vein interposition is frequently performed. However, one or more the internal diameters of applied vein graft are blocked or severely norrowed due to the mismatched diameter between existing artery and vein graft. The objective of this study is to investigate the blood flow influence on vein graft with mismatched diameter failure. The 3-D computational fluid dynamic method was employed to determine pulsatile flow velocity, pulsatile pressure gradient, and wall shear stress impact on the mismatched diameter of artery-vein graft model. We expect that pulsatile flow velocity, pulsatile pressure gradient, and wall shear stress impact on mismatched diameter of artery-vein graft model to behave non-hydraulically compared to an ideal matched model

Risk factors of musculoskeletal disorders among oil palm fruit harvesters during early harvesting stage

This cross-sectional study intends to investigate the associations of musculoskeletal disorders (MSDs) among foreign labourers on a socio-economic background, occupational exposure, social lifestyle, and postures adopted during harvesting tasks. A total of 446 male respondents (263 FFB cutters; 183 FFB collectors) were studied using an interview-assisted questionnaire. OWAS was used to determine the severity of awkward posture based on videos of harvesting tasks recorded for each respondent. Analysis found that increasingly educated respondents had higher risk of developing MSDs. Shorter daily work duration and longer resting duration appear to increase the risk of neck and shoulder disorders among harvesters, which may be attributable to organizational work design. Awkward posture was a particularly significant risk factor of MSDs among FFB collectors. Among the results of the study, occupational exposure, postures and certain socio-demographic backgrounds explained some, but not all, the risk factor of MSDs among harvesters. An in-depth investigation, preferably a longitudinal study investigating the dynamic of work activities and other risk factors, such as psychosocial risk factors, are recommended