This paper presents 18 fundamental movements for the rehabilitation of the stroke patient. The objective of this research is to develop the movement sequences which are suitable for the rehabilitation process and is focused on hemiparesis sufferers which are the most common among stroke patients. The muscle activities are analyzed using electromyography (EMG). 12 electrodes are attached to the right arm of the subject includes deltoid, bicep, tricep, flexor and extensor. The experimental results proof that it is likely to produce movement sequence for stroke rehabilitation based on each muscle activity.
In this research paper, the study for grip force on the maximum level of the various materials handling griper can be evaluate at an effective maximum isometric strength especially for intermediate and proximal phalanges of index finger. This analysis method using the piezoresistive force sensor, whereas the devices will be automatically increases the accuracy and repeatability of the force sensitivity. Force sensor is a component of flexible and easily applied to enable measurement of the non-intrusive value. The sensor can be attached to or placed on a variety of surface conditions. The physical structure of product is to be combined with plastic film or metal for increased stiffness or for added protection from abrasion. In order to determine forces acting upon an articular joint during fingers rehabilitation for maximum grip force on low cost DataGlove. The estimation show that all the action force is starting at their fingertips functioning as the total volume of gripper force, dimensions / orientation of the handle, and grip made. By measuring the gripper forces acting on the fingertips of several subjects, the different handle and level of gripper force are resulting from movement of fingers will be gathered and will be analyzed so that a realistic mathematical model structure could be produced.
The rapid development of technologies that are emerging during this era produces the evolution of humancomputer interaction (HCI). Dataglove is one of sensor technologies resultant from HCI advancement. Dataglove provides vital information of finger grasping activities for HCI and Human-Machine Interface (HMI) by providing physical data of finger bending. Nevertheless, data acquisitions from Dataglove need to be processed and analyzed in order to effectively train the computer to recognize the finger grasping activities. The purpose of this research is to recognize the grasping objects by using feature extraction and clustering techniques. The conclusion will determine grasping features of subject to grasp the experimental object with the thumb, index and middle fingers of GloveMAP. Based on fingers movement adapted, this study gives the grasping features in order to justify the best grasping for each subject grasp behavior.
Abstract-This research study presents the recognition of fingers grasps for various grasping styles of daily living. In general, the posture of the human hand determines the fingers that are used to create contact between an object at the same time while developing the touching contact. Human grasping can detect by studying the movement of fingers while bending during object holding. Ten right-handed subjects are participated in the experiment; each subject was fitted with a right-handed GloveMAP, which recorded all movement of the thumb, index, and middle of human fingers while grasping selected objects. GloveMAP is constructed using flexible bend sensors placed back of a glove. Based on the grasp human taxonomy by Cutkosky, the object grasping is distinguished by two dominant prehensile postures; that is, the power grip and the precision grip. The dataset signal is extracted using GloveMAP, and all the signals are filtered using Gaussian filtering method. The method is capable to improving the amplitude transmission characteristic with the minimal combination of time and amplitude response. The result was no overshoot in order to smoothen the grasping signal from unneeded signal (noise) that occurs on the input / original grasping data. Principal Component Analysis -Best Matching Unit (PCA-BMU) is a process of justifying the human grasping data involves several grasping groups and forming a component identified as nodes or neuron.
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