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Exoskeletons have gained importance recently. In the case of musculoskeletal diseases, they can restore or support the mobility of injured body parts; in everyday life, they can relieve the musculoskeletal system and thus contributes to the preservation of health into old age. However, active systems face challenges due to the efficient and reliable actuators. In the following, the example of the hand will be used to investigate what influence the individuality of the hand under consideration has on the selection of a suitable actuator about force and stroke. The literature outlines that tendon-driven mechanism with a biomimetic design are a promising approach for hand exoskeletons: The finger is actively moved by an actuator using artificial tendons positioned along the finger to match anatomical structures. For this purpose, actuators based on electromagnetic or pneumatic principles, and smart materials are described. The requirements for force and stroke of the drive can be derived from the analysis of finger kinematics. In this study the finger kinematics of 13 subjects were analyzed. The results show a scatter in stroke and force depending on the finger considered. To select a suitable actuator for the biomimetic hand exoskeleton, these results are compared with the force and stroke characteristics of various actuating principles. The comparison reveals that the choice of the actuator depends on the intended primary support for the hand (movement, endurance, grip strength). Hence, it can be deduced that step drivers meet the force and stroke requirements and may be a suitable alternative. [ABSTRACT FROM AUTHOR]

An ordinary differential equation (ODE) which models human muscular movement is considered. A functional form of the model parameter is specified through the Lie symmetry approach, yielding a different expression from the one derived in the previous study (Kosugi et al., 2019). The Lie point symmetries corresponding to the model parameter are employed for derivation of exact solution.

Introduction: The oblique retinacular ligament (ORL) has fascinated researchers since its first detailed description by Landsmeer in 1949. Integral to the sophisticated mechanics of finger movements, its anatomical presence and function have sparked extensive debate. Despite initial doubts about its existence, it is now recognized as a crucial structure within the hand's biomechanical system. Materials and methods: A review was conducted to collate primary studies on the ORL, focusing on cadaveric anatomy and biomechanical analyses. Modern dissection techniques and equipment have facilitated the identification of the ORL in virtually all examined samples, moving past earlier challenges of isolating such minute structures. Results: Research elucidates the ORL's dynamic role in coordinating movements between the proximal and distal interphalangeal joints, contradicting earlier theories that deemed it a mere static stabilizer. Recent findings, especially those by Balakrishnan et al., highlight a complex cross structure that allows the ORL to maintain its functional relevance across various joint positions. This challenges previous notions and underscores its significance in finger extension mechanisms. Conclusion: The journey to understand the ORL underscores its pivotal role in hand biomechanics, transforming from a structure of ambiguous existence to a key element in finger movement coordination. This shift in understanding opens new possibilities for therapeutic approaches to hand deformities, emphasizing the ORL's critical function in both anatomical and clinical contexts. As research progresses, it continues to reveal the ORL's complex contributions to hand dynamics, offering insights that promise to enhance outcomes in hand surgery and rehabilitation.

Rehabilitation is a therapy process that helps people who have lost their motor abilities partially or completely to restore their lost functions. Accuracy and continuity of the repetitive movements in the therapy process are important for the success of the rehabilitation process. Therefore, in order to ensure the continuity of the rehabilitation process, it is necessary to develop systems where exercises can be continued without going to rehabilitation centers. In this study, a robot hand prototype application that imitates both hand and finger movements has been developed for hand rehabilitation applications. An Inertial Measurement Unit (IMU) sensor is used to detect hand movements and a flexible sensor is used to detect finger movements. The hand movements are used to control the two motors on the robot hand prototype, while the flexibility sensor controls the gripper on the robot, allowing the robot hand prototype to grip. The task completion times were recorded by giving the participants the task of moving a cylindrical object to the target point using the robot hand prototype. [ABSTRACT FROM AUTHOR]

The deployment of electromyography (EMG) signals can be used in decoding finger movements for exoskeleton robotics, prosthetic hands, and powered wheelchairs and thus has attracted the attention of many researchers. However, decoding any movement is a challenging task. The success of using EMG signals depends on the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study conducted an eightfeature extraction evaluation on various machine learning methods, i.e., Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). Datasets from four intact subjects were used to classify twelve finger movements. Through five cross-validations, the result showed that almost all feature extractions combined with SVM outperformed the other combinations of features and classifiers. Mean absolute value (MAV) as a feature and SVM as a classifier were the best combination, with an accuracy of 94.01%. [ABSTRACT FROM AUTHOR]

We hypothesized that fine finger motor functions are controlled by the ipsilesional hemisphere, and that gross motor functions are compensated for by the contralesional hemisphere after brain injury in humans. The purpose of this study was to compare finger movements before and after hemispherotomy that defunctionated the ipsilesional hemisphere for patients with hemispherical lesions. We statistically compared Brunnstrom stage of the fingers, arm (upper extremity), and leg (lower extremity) before and after hemispherotomy. Inclusion criteria for this study were: 1) hemispherotomy for hemispherical epilepsy; 2) a ≥ 6-month history of hemiparesis; 3) post-operative follow-up ≥ 6 months; 4) complete freedom from seizures without aura; and 5) application of our protocol for hemispherotomy. Among 36 patients who underwent multi-lobe disconnection surgeries, 8 patients (2 girls, 6 boys) met the study criteria. Mean age at surgery was 6.38 years (range, 2–12 years; median, 6 years; standard deviation, 3.5 years). Paresis of the fingers was significantly exacerbated (p = 0.011) compared to pre-operatively, whereas that of the upper limbs (p = 0.07) and lower limbs (p = 0.103) was not. Finger movement functions tend to remain in the ipsilesional hemisphere after brain injury, whereas gross motor movement functions such as those of the arms and legs are compensated for by the contralesional hemisphere in humans. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction. This study introduces a 3D convolutional neural network (3D-CNN) model to decode finger movements using ECoG data. The model employs adaptive, explainable AI (xAI) techniques to interpret the physiological relevance of brain signals. ECoG signals from epilepsy patients during awake craniotomy were processed to extract power spectral density across multiple frequency bands. These data formed a 3D matrix used to train the 3D-CNN to predict finger trajectories. The 3D-CNN model showed significant accuracy in predicting finger movements, with root-mean-square error (RMSE) values of 0.26–0.38 for single finger movements and 0.20–0.24 for combined movements. Explainable AI techniques, Grad-CAM and SHAP, identified the high gamma (HG) band as crucial for movement prediction, showing specific cortical regions involved in different finger movements. These findings highlighted the physiological significance of the HG band in motor control. The 3D-CNN model outperformed traditional machine learning approaches by effectively capturing spatial and temporal patterns in ECoG data. The use of xAI techniques provided clearer insights into the model's decision-making process, unlike the "black box" nature of standard deep learning models. The proposed 3D-CNN model, combined with xAI methods, enhances the decoding accuracy of finger movements from ECoG data. This approach offers a more efficient and interpretable solution for brain-computer interface (BCI) applications, emphasizing the HG band's role in motor control. • Micro-ECoG signals from epilepsy patients were recorded during awake craniotomy. • Signals were processed using detrending, denoising, and frequency band analysis. • A 3D-CNN model was trained to predict finger movements from ECoG data. • Grad-CAM and SHAP values were used to explain the 3D-CNN model's predictions. • High gamma band (HG) was significant in predicting thumb and index movements. [ABSTRACT FROM AUTHOR]

Using friction modulation to simulate fabrics with a tactile stimulator (i.e. virtual surface) is not sufficient to render fabric touch and even more so for hairy fabrics. We hypothesized that seeing the pile of the velvet darken or lighten depending on changes in the finger movement direction on the virtual surface should improve the velvet fabric rendering. Participants actively rubbed a tactile device or a velvet fabric looking at a screen that showed a synthesized image of a velvet that either remained static (V-static) or darkening/lightening with the direction of touch (V-moving). We showed that in V-moving condition, the touched surface was always perceived rougher, which is a descriptor of a real velvet (Experiment 1). Using electroencephalography and sources localization analyses, we found increased activity in the occipital and inferior parietal lobes (Experiment 2) when seeing dark and shining traces during back-and-forth finger movements over the virtual surface. This suggests that these two posterior cortical regions work together to evaluate visuo-tactile congruence between the seen and the felt (tactile). The visuo-tactile binding, evidenced by neural synchronization (specifically, theta band (5-7 Hz) oscillation) in the left inferior posterior parietal lobule, is consistent with enhanced integration of information and probably contributed to the emergence of a more realistic velvet representation.

The correlation between the performance of coordination movement and brain activity is still not fully understood. The current study aimed to identify activated brain regions and brain network connectivity changes for several coordinated finger movements with different difficulty levels and to correlate the brain hemodynamics and connectivity with kinematic performance. Methods: Twenty-one right-dominant-handed subjects were recruited and asked to complete circular motions of single and bilateral fingers in the same direction (in-phase, IP) and in opposite directions (anti-phase, AP) on a plane. Kinematic data including radius and angular velocity at each task and synchronized blood oxygen concentration data using functional near-infrared spectroscopy (fNIRS) were recorded covering six brain regions including the prefrontal cortex, motor cortex, and occipital lobes. A general linear model was used to locate activated brain regions, and changes compared with baseline in blood oxygen concentration were used to evaluate the degree of brain region activation. Small-world properties, clustering coefficients, and efficiency were used to measure information interaction in brain activity during the movement. Result: It was found that the radius error of the dominant hand was significantly lower than that of the non-dominant hand (p < 0.001) in both clockwise and counterclockwise movements. The fNIRS results confirmed that the contralateral brain region was activated during single finger movement and the dominant motor area was activated in IP movement, while both motor areas were activated simultaneously in AP movement. The 1hbo were weakly correlated with radius errors (p = 0.002). Brain information interaction in IP movement was significantly larger than that from AP movement in the brain network (p < 0.02) in the right prefrontal cortex. Brain activity in the right motor cortex reduces motor performance (p < 0.001), while the right prefrontal cortex region promotes it (p < 0.05). Conclusion: Our results suggest there was a significant correlation between motion performance and brain activation level, as well as between motion deviation and brain functional connectivity. The findings may provide a basis for further exploration of the operation of complex brain networks. [ABSTRACT FROM AUTHOR]

This study proposes Bstick, the first handheld-type haptic controller that can monitor and control the placement of five fingers in real-time using linear motors attached on the fingers. As a handheld device, it can be used with both hands, and it was designed and produced to allow it to be freely moved and used with other virtual reality (VR) devices via Bluetooth. Bstick also provides stiffness that can maintain the pressing forces of an adult man's fingers, providing a realistic sense of grabbing and controlling a virtual object with rigidity and softness. By changing the location of the finger buttons, the device can render virtual objects of various shapes and sizes. A component that can be implemented in the Unity game engine was developed to provide convenience in the content development, using a haptic controller feature where a user can move five fingers independently, and this was applied for hand rehabilitation contents. Bstick includes five linear motors that can sustain approximately 22 N of force per finger, and the hardware and circuitry are compact, so as to be held in the user's hand. Bstick can be used to create VR services and contents based on five-fingered force feedback using a haptic controller that can independently manage the motions of five fingers, and Unity game engine software that can modify hardware. [ABSTRACT FROM AUTHOR]

The ability to imitate is considered impaired in schizophrenia patients. This assumption, however, is based on heterogeneous studies mostly targeting voluntary imitation, e.g., pantomime. Studies on automatic imitation, however, and on underlying mechanisms of top-down inhibition of automatic imitation and contextual modulation in schizophrenia are highly limited. We employed two sensorimotor paradigms to examine imitation-inhibition and action context mapping in 37 schizophrenia patients and 36 matched controls. In the first experiment, participants performed finger lifts while observing a hand executing compatible or incompatible finger lifts from the third-person perspective. The compatibility or incompatibility of these finger lifts affected participants' reaction times (RTs). The comparison of between-condition RT differences shows a larger movement compatibility effect in schizophrenia than in controls. The second experiment involved finger lifts while watching a still hand, from the first-person perspective, with constrained fingers that either corresponded or did not correspond to the participants' response fingers. Here, schizophrenia patients showed a diminished RT slowing in corresponding constraint trials. While the former results provide evidence for an impaired control of imitation in patients with schizophrenia, the latter results indicate a reduced encoding of action context. In conclusion, this study provides the first evidence for deficits of top-down control of imitation and motor context processing in the same sample of schizophrenia patients. [ABSTRACT FROM AUTHOR]

Brain computer interface (BCI) has been widely studied to allow people to control external devices as an extension of capabilities or a replacement of lost functions. The decoding algorithm of brain signals is a crucial part in BCI, since its performance determines the efficiency of the interface. Decoding performance can be improved by generating optimal feature matrix. The objective of this paper is to propose and implement a decoding algorithm with optimized small dimension feature matrix on identifying motor intention of finger movement using electroencephalogram (EEG) signals. An experiment was designed and conducted, in which EEG was acquired from 10 healthy subjects during the left or the right index finger movement. Event-related desynchronization (ERD) topography was analyzed during motor tasks. A degree feature extraction algorithm was proposed based on the graph theory together with Support Vector Machine (SVM) to classify two kinds of index finger movement, which takes three factors into consideration: frequency bands, amplitude and range of ERD. The results showed that the algorithm can classify the finger movement effectively for 7 subjects based on a three-dimension optimized feature matrix, consisting of the maximum degree, average degree and clustering range. The proposed algorithm is not limited by the size of samples and can indicate the source area of the neural activities. Results also demonstrate that the proposed degree feature extraction algorithm can smooth signal noise and enlarge the feature differences between the contralateral and the ipsilateral hemispheres.

Hu proposed a method that recognizes finger movements by detecting wrist muscles for human-computer interaction (HCI). Considering human habits and aesthetics, the sensor is placed on the back of the wrist. We first designed a polyvinylidene fluoride (PVDF) piezoelectric thin-film sensor unit with a planar elastic substrate. By studying the effects of the hardness and thickness of the substrate, we designed the sensor unit to have a Shore-A hardness of 23 and a thickness of 2 mm. Then we constructed a 4 × 2 sensor matrix with a size of 25 × 15 mm2. To build a finger movement dataset, we collected wrist dorsal movement signals when the fingers moved using the sensor matrix. Then we used a four-layer back-propagation (BP) neural network to recognize the finger movements. We experimentally demonstrated that even on the dorsal wrist side, finger movements could be recognized. The recognition rate of the general model using mixed personal data was 79%. In comparison, the recognition rate of the individual model using personal data was 94%. [ABSTRACT FROM AUTHOR]

Stuttering is a neurodevelopmental speech disorder characterized by the symptoms of speech repetition, prolongation, and blocking. Stuttering-related dysfluency can be transiently alleviated by providing an external timing signal such as a metronome or the voice of another person. Therefore, the existence of a core motor timing deficit in stuttering has been speculated. If this is the case, then motoric behaviors other than speech should be disrupted in stuttering. This study examined motoric performance on four complex bimanual tasks in 37 adults who stutter and 31 fluent controls. Two tasks utilized bimanual rotation to examine motor dexterity, and two tasks used the bimanual mirror and parallel tapping movements to examine timing control ability. Video-based analyses were conducted to determine performance accuracy and speed. The results showed that individuals who stutter performed worse than fluent speakers on tapping tasks but not on bimanual rotation tasks. These results suggest stuttering is associated with timing control for general motor behavior. [ABSTRACT FROM AUTHOR]

Stuttering is a neurodevelopmental speech disorder characterized by the symptoms of speech repetition, prolongation, and blocking. Stuttering-related dysfluency can be transiently alleviated by providing an external timing signal such as a metronome or the voice of another person. Therefore, the existence of a core motor timing deficit in stuttering has been speculated. If this is the case, then motoric behaviors other than speech should be disrupted in stuttering. This study examined motoric performance on four complex bimanual tasks in 37 adults who stutter and 31 fluent controls. Two tasks utilized bimanual rotation to examine motor dexterity, and two tasks used the bimanual mirror and parallel tapping movements to examine timing control ability. Video-based analyses were conducted to determine performance accuracy and speed. The results showed that individuals who stutter performed worse than fluent speakers on tapping tasks but not on bimanual rotation tasks. These results suggest stuttering is associated with timing control for general motor behavior.

In this paper, a flexible dual-modal sensing system is proposed for monitoring the pressure and inertial information of the finger movement. The sensing system is composed of a pressure sensor array and two inertial measurement units (IMUs) for sensing the fingertip pressure and the finger gesture, respectively. The graphene aerogel (GA) with excellent sensitivity is utilized as the sensing element of the pressure sensor array, and five GA sensing elements are designed to detect the pressure distribution of different fingertip areas. With serpentine circuitry structure sealed by polydimethylsiloxane (PDMS), the pressure sensor array can demonstrate flexibility, stretchability, and biocompatibility when mounted on the finger. The two IMUs are attached to the back of the finger. The signal and power supply electrodes of the pressure sensor array are connected with the IMUs, which can obtain the pressure and inertial signals simultaneously. A prototype was fabricated and experiments were carried out to test the basic performance of the sensing system. It has been verified that the pressure sensor array shows high resolution of 108.8 kPa and well stability after a 10-hour repeatability experiment. The IMUs are also reliable for sensing inertial information after calibration. Measurement of four daily hand movements and recognition of ten typical grasping gestures were conducted to verify the feasibility of the proposed system. Experimental results showed that the sensing systems could classify the ten gestures with a high accuracy of 97%, which verified the approach can provide a promising path for hand movement measurement and gesture recognition. [ABSTRACT FROM AUTHOR]

Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions—independent component analysis and non-negative matrix factorization—were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.

This study proposes Bstick, the first handheld-type haptic controller that can monitor and control the placement of five fingers in real-time using linear motors attached on the fingers. As a handheld device, it can be used with both hands, and it was designed and produced to allow it to be freely moved and used with other virtual reality (VR) devices via Bluetooth. Bstick also provides stiffness that can maintain the pressing forces of an adult man’s fingers, providing a realistic sense of grabbing and controlling a virtual object with rigidity and softness. By changing the location of the finger buttons, the device can render virtual objects of various shapes and sizes. A component that can be implemented in the Unity game engine was developed to provide convenience in the content development, using a haptic controller feature where a user can move five fingers independently, and this was applied for hand rehabilitation contents. Bstick includes five linear motors that can sustain approximately 22 N of force per finger, and the hardware and circuitry are compact, so as to be held in the user’s hand. Bstick can be used to create VR services and contents based on five-fingered force feedback using a haptic controller that can independently manage the motions of five fingers, and Unity game engine software that can modify hardware.

Myoelectric control system (MCS) had been applied to hand exoskeleton to improve the human-machine interaction. The current MCS enables the exoskeleton to move all fingers concurrently for opening and closing hand and does not consider robustness issue caused by the condition not considered in the training stage. This study addressed a new MCS employing novel myoelectric pattern recognition (M-PR) to handle more movements. Furthermore, a rejection-based radial-basis function extreme learning machine (RBF-ELM) was proposed to tackle the movements that are not included in the training stage. The results of the offline experiments showed the RBF-ELM with rejection mechanism (RBF-ELM-R) outperformed RBF-ELM without rejection mechanism and other well-known classifiers. In the online experiments, using 10-trained classes, the M-PR achieved an accuracy of 89.73% and 89.22% using RBF-ELM-R and RBF-ELM, respectively. In the experiment with 5-trained classes and 5-untrained classes, the M-PR accuracy was 80.22% and 59.64% using RBF-ELM-R and RBF-ELM, respectively [ABSTRACT FROM AUTHOR]

Volkmann ischemic contracture (VIC) is a devastating condition that results from neglected compartment syndrome, which leads to prolonged ischemia, irreversible tissue necrosis, and various degrees of muscle and nerve damage, causing serious motor and sensory functional implications for the limb and a spectrum of diseases associated with worsening deformities. A thorough understanding of the anatomy and VIC pathophysiology is needed to plan an appropriate strategy. Functioning free muscle transplantation (FFMT) can restore finger movement in a paralyzed limb but requires a three-staged approach to maximize the benefits of FFMT, leading to meaningful finger extrinsic function., Competing Interests: Disclosure The authors have no financial interest to declare in relation to the content of this article. No funding was received for this article., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions—independent component analysis and non-negative matrix factorization—were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals. [ABSTRACT FROM AUTHOR]

Featured Application: This study presents the design of a low-cost hand prosthesis, produced by additive manufacturing, which can be used as a reference for manufacturing prostheses for various types of hand amputation. This work aims to develop a low-cost human hand prosthesis manufactured through additive manufacturing. The methodology used for the development of the prosthesis used affordable and low-cost materials in the market. Tensile testing was performed to estimate the mechanical properties in order to verify the resistance of the printing material used. Afterwards, the mechanical feasibility study executed on the device was performed using finite element method. In conclusion, we can observe fundamental factors that influence the 3D printing process, especially in relation to its printing parameters and mechanical properties. Maximum stress, yield stress, modulus of elasticity, elongation, and hardness are the prominent properties that should be considered when choosing the polymeric material. The numerical simulation showed that the structure of the prosthesis did not present plastic deformations to the applied loads, proving its mechanical viability. [ABSTRACT FROM AUTHOR]

Estimation of joint kinematics plays an important role in intuitive human–machine interactions. However, continuous and reliable estimation of small (e.g., the finger) joint angles is still a challenge. The objective of this study was to continuously estimate finger joint angles using populational motoneuron firing activities. Multi-channel surface electromyogram (sEMG) signals were obtained from the extensor digitorum communis muscles, while the subjects performed individual finger oscillatory extension movements at two different speeds. The individual finger movement was first classified based on the EMG signals. The discharge timings of individual motor units were extracted through high-density EMG decomposition, and were then pooled as a composite discharge train. The firing frequency of the populational motor unit firing events was used to represent the descending neural drive to the motor unit pool. A second-order polynomial regression was then performed to predict the measured metacarpophalangeal extension angle using the derived neural drive based on the neuronal firings. Our results showed that individual finger extension movement can be classified with >96% accuracy based on multi-channel EMG. The extension angles of individual fingers can be predicted continuously by the derived neural drive with R2 values >0.8. The performance of the neural-drive-based approach was superior to the conventional EMG-amplitude-based approach, especially during fast movements. These findings indicated that the neural-drive-based interface was a promising approach to reliably predict individual finger kinematics. [ABSTRACT FROM AUTHOR]

In this study, we proposed and developed a novel ultra-multijointed assistive mechanism for gripping, which can assist flexion and extension from the back of the hand. In addition, we confirmed the applicability of our mechanism to gripping of daily objects with assistance of flexion and extension by our mechanism. In the mechanism, many links are combined at the hinged joints, and a wire is inserted into the mechanism to transmit a bending force. To generate bending torque for the finger, the wire is pulled by the tip of the ultra-multijointed structure. When the wire is not pulled, no bending torque is generated for the finger, and the ultra-multijointed structure becomes flexible in the bending direction of the fingerʼs flexion and extension. By extruding this ultra-multijointed structure according to the finger surface length, which changes with flexion and extension, it is possible to adapt it to flex and extend for a wide range of finger postures for gripping daily objects. To confirm the applicability of the gripping assistant, we conducted experiments using a hand mockup and in a healthy volunteer. We have demonstrated the applicability of the assistive mechanism for providing assistive force to support fingers in gripping daily objects by assisting the flexion and extension of the fingersIn this study, we proposed and developed a novel ultra-multijointed assistive mechanism for gripping, which can assist flexion and extension from the back of the hand. In addition, we confirmed the applicability of our mechanism to gripping of daily objects with assistance of flexion and extension by our mechanism. In the mechanism, many links are combined at the hinged joints, and a wire is inserted into the mechanism to transmit a bending force. To generate bending torque for the finger, the wire is pulled by the tip of the ultra-multijointed structure. When the wire is not pulled, no bending torque is generated for the finger, and the ultra-multijointed structure becomes flexible in the bending direction of the fingerʼs flexion and extension. By extruding this ultra-multijointed structure according to the finger surface length, which changes with flexion and extension, it is possible to adapt it to flex and extend for a wide range of finger postures for gripping daily objects. To confirm the applicability of the gripping assistant, we conducted experiments using a hand mockup and in a healthy volunteer. We have demonstrated the applicability of the assistive mechanism for providing assistive force to support fingers in gripping daily objects by assisting the flexion and extension of the fingers.. [ABSTRACT FROM AUTHOR]

Investigating human movements is the most important issue in rehabilitation science. Movements of fingers as one of the major movements of human has been considered by many scientists. Therefore, decoding of finger movements by analysis of related biosignal is very important to consider. In this research, we do the complexity analysis on the Electromyography (EMG) signal that was recorded due to basic movements of fingers. In fact, the EMG signal was classified in case of different movements of fingers by fractal analysis. The result of analysis showed that the EMG signal has the greatest and lowest fractal dimension (complexity) in case of thumb finger flexion and little finger extension. In further attempts, the fractal theory can be applied to investigate the influence of other types of stimulation on variations of the complexity of muscles' reactions. [ABSTRACT FROM AUTHOR]

This work aims to develop a low-cost human hand prosthesis manufactured through additive manufacturing. The methodology used for the development of the prosthesis used affordable and low-cost materials in the market. Tensile testing was performed to estimate the mechanical properties in order to verify the resistance of the printing material used. Afterwards, the mechanical feasibility study executed on the device was performed using finite element method. In conclusion, we can observe fundamental factors that influence the 3D printing process, especially in relation to its printing parameters and mechanical properties. Maximum stress, yield stress, modulus of elasticity, elongation, and hardness are the prominent properties that should be considered when choosing the polymeric material. The numerical simulation showed that the structure of the prosthesis did not present plastic deformations to the applied loads, proving its mechanical viability.

Humans represent symbolic numbers as oriented from left to right: the mental number line (MNL). Up to now, scientific studies have mainly investigated the MNL by means of response times. However, the existing knowledge on the MNL can be advantaged by studies on motor patterns while responding to a number. Cognitive representations, in fact, cannot be fully understood without considering their impact on actions. Here we investigated whether a motor response can be influenced by number processing. Participants seated in front of a little soccer goal. On each trial they were visually presented with a numerical (2, 5, 8) or a non-numerical ($) stimulus. They were instructed to kick a small ball with their right index toward a frontal soccer goal as soon as a stimulus appeared on a screen. However, they had to refrain from kicking when number five was presented (no-go signal). Our main finding is that performing a kicking action after observation of the larger digit proved to be more efficient: the trajectory path was shorter and lower on the surface, velocity peak was anticipated. The smaller number, instead, specifically altered the temporal and spatial aspects of trajectories, leading to more prolonged left deviations. This is the first experimental demonstration that the reaching component of a movement is influenced by number magnitude. Since this paradigm does not require any verbal skill and non-symbolic stimuli (array of dots) can be used, it could be fruitfully adopted to evaluate number abilities in children and even preschoolers. Notably, this is a self-motivating and engaging task, which might help children to get involved and to reduce potential arousal connected to institutional paper-and-pencil examinations.

The implementation of signal filters in a real-time form requires a tradeoff between computation resources and the system performance. Therefore, taking advantage of low lag response and the reduced consumption of resources, in this article, the Recursive Least Square (RLS) algorithm is used to filter a signal acquired from a fiber-optics-based sensor. In particular, a Long-Period Fiber Grating (LPFG) sensor is used to measure the bending movement of a finger. After that, the Gaussian Mixture Model (GMM) technique allows us to classify the corresponding finger position along the motion range. For these measures to help in the development of an autonomous robotic hand, the proposed technique can be straightforwardly implemented on real time platforms such as Field Programmable Gate Array (FPGA) or Digital Signal Processors (DSP). Different angle measurements of the finger’s motion are carried out by the prototype and a detailed analysis of the system performance is presented.

Humans represent symbolic numbers as oriented from left to right: the mental number line (MNL). Up to now, scientific studies have mainly investigated the MNL by means of response times. However, the existing knowledge on the MNL can be advantaged by studies on motor patterns while responding to a number. Cognitive representations, in fact, cannot be fully understood without considering their impact on actions. Here we investigated whether a motor response can be influenced by number processing. Participants seated in front of a little soccer goal. On each trial they were visually presented with a numerical (2, 5, 8) or a non-numerical ($) stimulus. They were instructed to kick a small ball with their right index toward a frontal soccer goal as soon as a stimulus appeared on a screen. However, they had to refrain from kicking when number five was presented (no-go signal). Our main finding is that performing a kicking action after observation of the larger digit proved to be more efficient: the trajectory path was shorter and lower on the surface, velocity peak was anticipated. The smaller number, instead, specifically altered the temporal and spatial aspects of trajectories, leading to more prolonged left deviations. This is the first experimental demonstration that the reaching component of a movement is influenced by number magnitude. Since this paradigm does not require any verbal skill and non-symbolic stimuli (array of dots) can be used, it could be fruitfully adopted to evaluate number abilities in children and even preschoolers. Notably, this is a self-motivating and engaging task, which might help children to get involved and to reduce potential arousal connected to institutional paper-and-pencil examinations. [ABSTRACT FROM AUTHOR]