Surface EMG in advanced hand prosthetics

One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible. This work is partially supported by the project NEURObotics, FP6-IST-001917.     

An Anthropomorphic Robot Hand Developed Based on Underactuated Mechanism and Controlled by EMG Signals

When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a highdexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into account to fully accomplishthe actuation tasks.This paper presents a novel humanoid robotic myocontrol hand (AR hand Ⅲ) which adopted an underac-tuated mechanism and a forearm myocontrol EMG method.The AR hand Ⅲ has five fingers and 15 joints,and actuated by threeembedded motors.Underactuation can be found within each finger and between the rest three fingers (the middle finger,the ringfinger and the little finger) when the hand is grasping objects.For the EMG control,two specific methods are proposed:thethree-fingered hand gesture configuration of the AR hand Ⅲ and a pattern classification method of EMG signals based on astatistical learning algorithm-Support Vector Machine (SVM).Eighteen active hand gestures of a testee are recognized ef-fectively,which can be directly mapped into the motions of AR hand Ⅲ.An on-line EMG control scheme is established basedon two different decision functions:one is for the discrimination between the idle and active modes,the other is for the recog-nition of the active modes.As a result,the AR hand Ⅲ can swiftly follow the gesture instructions of the testee with a time delayless than 100 ms.     

Neuron selection by relative importance for neural decoding of dexterous finger prosthesis control application

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain–machine interfaces for direct neural control of hand prostheses.     

Anthropomorphic Control of a Dexterous Artificial Hand via Task Dependent Temporally Synchronized Synergies

Despite the recent influx of increasingly dexterous prostheses, there remains a lack of sufficiently intuitive control methods to fully utilize this dexterity. As a solution to this problem, a control framework is proposed which allows the control of an arbitrary number of Degrees of Freedom （DOF） through a single electromyogram （EMG） control input. Initially, the joint motions of nine test subjects were recorded while grasping and catching a cylinder. Inherent differences emerged depending upon whether the cylinder was grasped or caught. These data were used to form a distinct synergy for each task, described as the families of parametric functions of time that share a mutual time vector. These two Temporally Synchronized Synergies （TSS） were derived to reflect the task dependent control strategies adopted by the initial participants. These synergies were then mapped to a dexterous artificial hand that was subsequently controlled by two subjects with transradial amputations. The EMG signals from these subjects were used to replace the time vector shared by the synergies, enabling the subjects to perform both tasks with a dexterous artificial hand using only a single EMG input. After a ten minute training period, the subjects learned to use the dexterous artificial hand to grasp and catch the cylinder with 100.0% and 65.0% average success rates, respectively.     

Development of a Prototype Over-Actuated Biomimetic Prosthetic Hand

The loss of a hand can greatly affect quality of life. A prosthetic device that can mimic normal hand function is very important to physical and mental recuperation after hand amputation, but the currently available prosthetics do not fully meet the needs of the amputee community. Most prosthetic hands are not dexterous enough to grasp a variety of shaped objects, and those that are tend to be heavy, leading to discomfort while wearing the device. In order to attempt to better simulate human hand function, a dexterous hand was developed that uses an over-actuated mechanism to form grasp shape using intrinsic joint mounted motors in addition to a finger tendon to produce large flexion force for a tight grip. This novel actuation method allows the hand to use small actuators for grip shape formation, and the tendon to produce high grip strength. The hand was capable of producing fingertip flexion force suitable for most activities of daily living. In addition, it was able to produce a range of grasp shapes with natural, independent finger motion, and appearance similar to that of a human hand. The hand also had a mass distribution more similar to a natural forearm and hand compared to contemporary prosthetics due to the more proximal location of the heavier components of the system. This paper describes the design of the hand and controller, as well as the test results.     

Sleeping posture recognition using fuzzy c-means algorithm

Background

Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet.

Method

We designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures.

Results

The experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy.

Conclusions

The proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.

     

A spin-label study of the effects of drugs on calcium release from isolated sarcoplasmic reticulum vesicles

The effects of caffeine, thymol, and procaine on calcium release from fragmented sarcoplasmic reticulum (FSR) from rabbit skeletal white muscle were investigated by the spin label method at the organellar level. Two thiol-directed spin labels, 4-maleimide-2,2,6,6-tetramethylpiperidinooxyl and 4-(2-iodoacetamide)-2,2,6,6-tetramethylpiperidinooxyl, were used for the labeling of SR proteins. The ratio (W/S) of the weakly (W) and strongly (S) immobilized ESR signals was measured for the maleimide and iodoacetamide labeled FSR. The two labels gave different W/S values, which means that the two labels report conformational changes at different loci of SR proteins. The dependences of the W/S ratios on the concentration of the drugs showed that conformational changes of SR proteins induced by these drugs are not the same. From measurements of the distribution of 5-doxyldecanoic acid methylester between the lipid and water phases, it was found that the conformational changes of the SR proteins caused by thymol or procaine induced a disorder in local regions of the phospholipid bilayers of FSR, while such disordering was not induced by caffeine. On the other hand, caffeine and thymol showed definite effects on calcium release from FSR, while procaine did not. These results indicate that the effects of the drugs on the protein conformations can be well characterized at the organellar level by means of the spin label technique and that some specific changes in the conformations of SR proteins are necessary for calcium release from FSR.     

Kinematic evaluation of the finger’s interphalangeal joints coupling mechanism—variability,flexion–extension differences,triggers, locking swanneck deformities,anthropometric correlations

The human finger contains tendon/ligament mechanisms essential for proper control. One mechanism couples the movements of the interphalangeal joints when the (unloaded) finger is flexed with active deep flexor. This study’s aim was to accurately determine in a large finger sample the kinematics and variability of the coupled interphalangeal joint motions, for potential clinical and finger model validation applications. The data could also be applied to humanoid robotic hands. Sixty-eight fingers were measured in seventeen hands in nine subjects. Fingers exhibited great joint mobility variability, with passive proximal interphalangeal hyperextension ranging from zero to almost fifty degrees. Increased measurement accuracy was obtained by using marker frames to amplify finger segment motions. Gravitational forces on the marker frames were not found to invalidate measurements. The recorded interphalangeal joint trajectories were highly consistent, demonstrating the underlying coupling mechanism. The increased accuracy and large sample size allowed for evaluation of detailed trajectory variability, systematic differences between flexion and extension trajectories, and three trigger types, distinct from flexor tendon triggers, involving initial flexion deficits in either proximal or distal interphalangeal joint. The experimental methods, data and analysis should advance insight into normal and pathological finger biomechanics (e.g., swanneck deformities), and could help improve clinical differential diagnostics of trigger finger causes. The marker frame measuring method may be useful to quantify interphalangeal joints trajectories in surgical/rehabilitative outcome studies. The data as a whole provide the most comprehensive collection of interphalangeal joint trajectories for clinical reference and model validation known to us to date.     

Human-like reflex control for an artificial hand

In this paper, we illustrate the low level reflex control used to govern an anthropomorphic artificial hand. The paper develops the position and stiffness control strategy based on dynamic artificial neurons able to simulate the neurons acting in the human reflex control. The controller has a hierarchical structure. At the lowest level there are the receptors able to convert the analogical signal into a neural impulsive signal appropriate to govern the reflex control neurons. Immediately upon it, the artificial motoneurons set the actuators inner pressure to control the finger joint position and moment. Other auxiliary neurons in combination with the motoneurons are able to set the finger stiffness and emulate the inverse myotatic reflex control. Stiffness modulation is important both to save energy during task execution, and to manage objects made of different materials. The inverse myotatic reflex is able to protect the hand from possible harmful external actions. The paper also presents the dynamic model of the joints and of the artificial muscles actuating Blackfingers, our artificial hand. This new type of neural control has been simulated on the Blackfingers model; the results indicate that the developed control is very flexible and efficient for all kind of joints present in the humanoid hand.     

Learned Manipulation at Unconstrained Contacts Does Not Transfer across Hands

Recent studies about sensorimotor control of the human hand have focused on how dexterous manipulation is learned and generalized. Here we address this question by testing the extent to which learned manipulation can be transferred when the contralateral hand is used and/or object orientation is reversed. We asked subjects to use a precision grip to lift a grip device with an asymmetrical mass distribution while minimizing object roll during lifting by generating a compensatory torque. Subjects were allowed to grasp anywhere on the object’s vertical surfaces, and were therefore able to modulate both digit positions and forces. After every block of eight trials performed in one manipulation context (i.e., using the right hand and at a given object orientation), subjects had to lift the same object in the second context for one trial (transfer trial). Context changes were made by asking subjects to switch the hand used to lift the object and/or rotate the object 180° about a vertical axis. Therefore, three transfer conditions, hand switch (HS), object rotation (OR), and both hand switch and object rotation (HS+OR), were tested and compared with hand matched control groups who did not experience context changes. We found that subjects in all transfer conditions adapted digit positions across multiple transfer trials similar to the learning of control groups, regardless of different changes of contexts. Moreover, subjects in both HS and HS+OR group also adapted digit forces similar to the control group, suggesting independent learning of the left hand. In contrast, the OR group showed significant negative transfer of the compensatory torque due to an inability to adapt digit forces. Our results indicate that internal representations of dexterous manipulation tasks may be primarily built through the hand used for learning and cannot be transferred across hands.     

Virtual power based algorithm for decoupling large motions from infinitesimal strains: application to shoulder joint biomechanics

New trends of numerical models of human joints require more and more computation of both large amplitude joint motions and fine bone stress distribution. Together, these problems are difficult to solve and very CPU time consuming. The goal of this study is to develop a new method to diminish the calculation time for this kind of problems which include calculation of large amplitude motions and infinitesimal strains. Based on the Principle of Virtual Power, the present method decouples the problem into two parts. First, rigid body motion is calculated. The bone micro-deformations are then calculated in a second part by using the results of rigid body motions as boundary conditions. A finite element model of the shoulder was used to test this decoupling technique. The model was designed to determine the influence of humeral head shape on stress distribution in the scapula for different physiological motions of the joint. Two versions of the model were developed: a first version completely deformable and a second version based on the developed decoupling method. It was shown that biomechanical variables, as mean pressure and von Mises stress, calculated with the two versions were sensibly the same. On the other hand, CPU time needed for calculating with the new decoupled technique was more than 6 times less than with the completely deformable model.     

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported.     

Directional Coordination of Thumb and Finger Forces during Precision Pinch

The human opposable thumb enables the hand to perform dexterous manipulation of objects, which requires well-coordinated digit force vectors. This study investigated the directional coordination of force vectors generated by the thumb and index finger during precision pinch. Fourteen right-handed, healthy subjects were instructed to exert pinch force on an externally stabilized apparatus with the pulps of the thumb and index finger. Subjects applied forces to follow a force-ramp profile that linearly increased from 0 to 12 N and then decreased to 0 N, at a rate of ±3 N/s. Directional relationships between the thumb and index finger force vectors were quantified using the coordination angle (CA) between the force vectors. Individual force vectors were further analyzed according to their projection angles (PAs) with respect to the pinch surface planes and the shear angles (SAs) within those planes. Results demonstrated that fingertip force directions were dependent on pinch force magnitude, especially at forces below 2 N. Hysteresis was observed in the force-CA relationship for increasing and decreasing forces and fitted with exponential models. The fitted asymptotic values were 156.0±6.6° and 150.8±9.3° for increasing and decreasing force ramps, respectively. The PA of the thumb force vector deviated further from the direction perpendicular to the pinching surface planes than that of the index finger. The SA showed that the index finger force vector deviated in the ulnar-proximal direction, whereas the thumb switched its force between the ulnar-proximal and radial-proximal directions. The findings shed light on the effects of anatomical composition, biomechanical function, and neuromuscular control in coordinating digit forces during precision pinch, and provided insight into the magnitude-dependent force directional control which potentially affects a range of dexterous manipulations.     

Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production

Determining tendon tensions of the finger muscles is crucial for the understanding and the rehabilitation of hand pathologies. Since no direct measurement is possible for a large number of finger muscle tendons, biomechanical modelling presents an alternative solution to indirectly evaluate these forces. However, the main problem is that the number of muscles spanning a joint exceeds the number of degrees of freedom of the joint resulting in mathematical under-determinate problems. In the current study, a method using both numerical optimization and the intra-muscular electromyography (EMG) data was developed to estimate the middle finger tendon tensions during static fingertip force production. The method used a numerical optimization procedure with the muscle stress squared criterion to determine a solution while the EMG data of three extrinsic hand muscles serve to enforce additional inequality constraints. The results were compared with those obtained with a classical numerical optimization and a method based on EMG only. The proposed method provides satisfactory results since the tendon tension estimations respected the mechanical equilibrium of the musculoskeletal system and were concordant with the EMG distribution pattern of the subjects. These results were not observed neither with the classical numerical optimization nor with the EMG-based method. This study demonstrates that including the EMG data of the three extrinsic muscles of the middle finger as inequality constraints in an optimization process can yield relevant tendon tensions with regard to individual muscle activation patterns, particularly concerning the antagonist muscles.     

Numerical analysis of dynamic temperature in response to different levels of reactive hyperaemia in a three-dimensional image-based hand model

Vascular reactivity (VR) is considered as an effective index to predict the risk of cardiovascular events. A cost-effective alternative technique used to evaluate VR called digital thermal monitoring (DTM) is based on the response of finger temperature to vessel occlusion and reperfusion. In this work, a simulation has been developed to investigate hand temperature in response to vessel occlusion and perfusion. The simulation consists of image-based mesh generation and finite element analysis of blood flow and heat transfer in tissues. In order to reconstruct a real geometric model of human hand, a computer programme including automatic image processing for sequential MR data and mesh generation based on the transfinite interpolation method is developed. In the finite element analysis part, blood flow perfused in solid tissues is considered as fluid phase through porous media. Heat transfer in tissues is described by Pennes bioheat equation and blood perfusion rate is obtained from Darcy velocities. Capillary pressure, blood perfusion and temperature distribution of hand are obtained. The results reveal that fingertip temperature is strongly dependent on larger arterial pressure. This simulation is of potential to quantify the indices used for evaluating the VR in DTM test if it is integrated with the haemodynamic model of blood circulation in upper limb.     

Virtual Power Based Algorithm for Decoupling Large Motions from Infinitesimal Strains: Application to Shoulder Joint Biomechanics

New trends of numerical models of human joints require more and more computation of both large amplitude joint motions and fine bone stress distribution. Together, these problems are difficult to solve and very CPU time consuming. The goal of this study is to develop a new method to diminish the calculation time for this kind of problems which include calculation of large amplitude motions and infinitesimal strains. Based on the Principle of Virtual Power, the present method decouples the problem into two parts. First, rigid body motion is calculated. The bone micro-deformations are then calculated in a second part by using the results of rigid body motions as boundary conditions. A finite element model of the shoulder was used to test this decoupling technique. The model was designed to determine the influence of humeral head shape on stress distribution in the scapula for different physiological motions of the joint. Two versions of the model were developed: a first version completely deformable and a second version based on the developed decoupling method. It was shown that biomechanical variables, as mean pressure and von Mises stress, calculated with the two versions were sensibly the same. On the other hand, CPU time needed for calculating with the new decoupled technique was more than 6 times less than with the completely deformable model.     

Hand function: peripheral and central constraints on performance.

The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.     

Development of a finger biomechanical model and its considerations

The development of a biomechanical model for a human finger is faced with many challenges, such as extensor mechanism complexity, statistical indeterminacy and suitability of computational processes. Motivation for this work was to develop a computer model that is able to predict the internal loading patterns of tendons and joint surfaces experienced by the human finger, while mitigating these challenges. Proposed methodology was based on a non-linear optimising mathematical technique with a criterion of boundary conditions and equality equations, maximised against unknown parameters to reduce statistical indeterminacy. Initial validation was performed via the simulation of one dynamic and two static postures case studies. Past models and experiments were used, based on published literature, to verify the proposed model's methodology and results. The feasibility of the proposed methodology was deemed satisfactory as the simulated results were concordant with in-vivo results for the extrinsic flexors.     

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.     

Kinematic performance of a six degree-of-freedom hand model (6DHand) for use in occupational biomechanics

Upper extremity musculoskeletal disorders represent an important health issue across all industry sectors; as such, the need exists to develop models of the hand that provide comprehensive biomechanics during occupational tasks. Previous optical motion capture studies used a single marker on the dorsal aspect of finger joints, allowing calculation of one and two degree-of-freedom (DOF) joint angles; additional algorithms were needed to define joint centers and the palmar surface of fingers. We developed a 6DOF model (6DHand) to obtain unconstrained kinematics of finger segments, modeled as frusta of right circular cones that approximate the palmar surface. To evaluate kinematic performance, twenty subjects gripped a cylindrical handle as a surrogate for a powered hand tool. We hypothesized that accessory motions (metacarpophalangeal pronation/supination; proximal and distal interphalangeal radial/ulnar deviation and pronation/supination; all joint translations) would be small (less than 5° rotations, less than 2mm translations) if segment anatomical reference frames were aligned correctly, and skin movement artifacts were negligible. For the gripping task, 93 of 112 accessory motions were small by our definition, suggesting this 6DOF approach appropriately models joints of the fingers. Metacarpophalangeal supination was larger than expected (approximately 10°), and may be adjusted through local reference frame optimization procedures previously developed for knee kinematics in gait analysis. Proximal translations at the metacarpophalangeal joints (approximately 10mm) were explained by skin movement across the metacarpals, but would not corrupt inverse dynamics calculated for the phalanges. We assessed performance in this study; a more rigorous validation would likely require medical imaging.