In this study, the EMG signals are processed using 16 time-domain features extraction to classify the finger movement such as thumb, index, middle, ring, and little. The pattern recognition of 16 extracted features are classified using artificial neural network (ANN) with two layer feed forward network. The network utilizes a log-sigmoid transfer function in hidden layer and a hyperbolic tangent sigmoid transfer function in the output layer. The ANN uses 10 neurons in hidden layer and 5 neurons in output layer. The training of ANN pattern recognition uses Levenberg-Marquardt training algorithm and the performance utilizes mean square error (MSE). At about 22 epochs the MSE of training, test, and validation get stabilized and MSE is very low.There is no miss classification during training process. Based on the resulted overall confusion matrix, the accuracy of thumb, middle, ring, and little is 100%. The confusion of index is 16.7%. The overall confusion matrix shows that the error is 3.3% and overall performance is 96.7%.
A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurological or muscular disorder by having nine hand movements. This research had several sequential processes (i.e., feature extraction, feature reduction, and feature classification). Sixteen time-domain features were employed for feature extraction. The features were then reduced using principal component analysis (PCA) into two and three-dimensional feature space. The artificial neural network (ANN) classifier was tested on two different feature sets: (1) using all principal components obtained from PCA (PC1–PC3) and (2) using selected principal components (PC2 and PC3). The third best principal components were then used for classification using ANN. The average accuracy using all subject signals was 86.7% to discriminate the nine finger movements.
This study proposes a data-driven control method of extra robotic fingers to assist a user in bimanual object manipulation that requires two hands. The robotic system comprises two main parts, i.e., robotic thumb (RT) and robotic fingers (RF). The RT is attached next to the user’s thumb, while the RF is located next to the user’s little finger. The grasp postures of the RT and RF are driven by bending angle inputs of flex sensors, attached to the thumb and other fingers of the user. A modified glove sensor is developed by attaching three flex sensors to the thumb, index, and middle fingers of a wearer. Various hand gestures are then mapped using a neural network. The input data of the robotic system are the bending angles of thumb and index, read by flex sensors, and the outputs are commanded servo angles for the RF and RT. The third flex sensor is attached to the middle finger to hold the extra robotic finger’s posture. Two force-sensitive resistors (FSRs) are attached to the RF and RT for the haptic feedback when the robot is worn to take and grasp a fragile object, such as an egg. The trained neural network is embedded into the wearable extra robotic fingers to control the robotic motion and assist the human fingers in bimanual object manipulation tasks. The developed extra fingers are tested for their capacity to assist the human fingers and perform 10 different bimanual tasks, such as holding a large object, lifting and operate an eight-inch tablet, and lifting a bottle, and opening a bottle cap at the same time.
[Purpose] To investigate the effect of heel height on the distribution of plantar foot force and heel pain in patients with a heel spur. [Subjects and Methods] Plantar force was measured using 8 force sensors in 16 patients (3 men, 13 women), with symptomatic heel spur for 4 heel heights (0–4 cm). Sensors were located at the hallux (T1); medial to lateral metatarsals (M1 through M3), mid-foot (MF); and at the central, lateral, and medial heel (CH, LH, and MH). Pain was evaluated using the minimum compression force that caused pain and was measured using an algometer. [Results] Load bearing shifted from the heel (CH) to the mid-foot (MF) and hallux (T1) with increasing heel height. Raising the heel from 2 to 3 cm reduced the magnitude of load bearing, relative to the minimum compression force for pain, by 3.70% at the LH and 2.35% at the MH. Excellent clinical outcomes, defined by a 70–100% decrease in pain, were achieved in 10/16 participants with the use of a 2-cm and 3-cm heel height in men and women, respectively. [Conclusion] Increasing heel height effectively decreases the plantar force on the heel during weight-bearing activities.
This study aims to invent a new, low-cost, and faster method of prosthetic socket fabrication, especially in Indonesia. In this paper, the photogrammetry with the 3D printing method is introduced as the new applicative way for transradial prosthetic making. Photogrammetry is used to retrieve a 3D model of the amputated hand stump using a digital camera. A digital camera is used for photogrammetry technique and the resulting 3D model is printed using a circular 3D printer with Polylactic acid (PLA) material. The conventional casting socket fabrication method was also conducted in this study as a comparison. Both prosthetic sockets were analyzed for usability, and sectional area conformities to determine the size deviation using the image processing method. This study concludes that the manufacturing of transradial prosthetic sockets incorporating the photogrammetry technique reduces the total man-hour production. Based on the results, it can be implied that the photogrammetry technique is a more efficient and economical method compared to the conventional casting method. The 3D printed socket resulting from the photogrammetry method has a 5–19% area deviation to the casting socket but it is still preferable and adjustable for the transradial amputee when applied to the stump of the remaining hand.
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