Fine detection of grasp force and posture by amputees via surface electromyography

The state-of-the-art feed-forward control of active hand prostheses is rather poor. Even dexterous, multi-fingered commercial prostheses are controlled via surface electromyography (EMG) in a way that enforces a few fixed grasping postures, or a very basic estimate of force. Control is not natural, meaning that the amputee must learn to associate, e.g., wrist flexion and hand closing. Nevertheless, recent literature indicates that much more information can be gathered from plain, old surface EMG. To check this issue, we have performed an experiment in which three amputees train a Support Vector Machine (SVM) using five commercially available EMG electrodes while asked to perform various grasping postures and forces with their phantom limbs. In agreement with recent neurological studies on cortical plasticity, we show that amputees operated decades ago can still produce distinct and stable signals for each posture and force. The SVM classifies the posture up to a precision of 95% and approximates the force with an error of as little as 7% of the signal range, sample-by-sample at 25 Hz. These values are in line with results previously obtained by healthy subjects while feed-forward controlling a dexterous mechanical hand. We then conclude that our subjects could finely feed-forward control a dexterous prosthesis in both force and position, using standard EMG in a natural way, that is, using the phantom limb.     

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.     

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.     

Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map

For controlling dexterous prosthetic hand with a high number of active Degrees of Freedom (DOF),it is necessary to reliably extract control volitions of finger motions from the human body.In this study,a large variety of finger motions are discriminated based on the diversities of the pressure distribution produced by the mechanical actions of muscles on the forearm.The pressure distribution patterns corresponding to the motions were measured by sensor array which is composed of 32 Force Sensitive Resistor (FSR) sensors.In order to map the pressure patterns with different finger motions,a multiclass classifier was designed based on the Support Vector Machine (SVM) algorithm.The multi-subject experiments show that it is possible to identify as many as seventeen different finger motions,including individual finger motions and multi-finger grasping motions,with the accuracy above 99％ in the in-session validation.Further,the cross-session validation demonstrates that the performance of the proposed method is robust for use if the FSR array is not reset.The results suggest that the proposed method has great application prospects for the control of multi-DOF dexterous hand prosthesis.     

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.     

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.     

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.     

Development of an Anthropomorphic Robotic Arm and Hand for Interactive Humanoids

Humanoid robots are designed and built to mimic human form and movement.Ultimately,they are meant to resemble the size and physical abilities of a human in order to function in human-oriented environments and to work autonomously but to pose no physical threat to humans.Here,a humanoid robot that resembles a human in appearance and movement is built using powerful actuators paired with gear trains,joint mechanisms,and motor drivers that are all encased in a package no larger than that of the human physique.In this paper,we propose the construction of a humanoid-applicable anthropomorphic 7-DoF arm complete with an 8-DoF hand.The novel mechanical design of this humanoid arm makes it sufficiently compact to be compatible with currently available narrating-model humanoids,and to be sufficiently powerful and flexible to be functional; the number of degrees of freedom endowed in this robotic arm is sufficient for executing a wide range of tasks,including dexterous hand movements.The developed humanoid arm and hand are capable of sensing and interpreting incoming external force using the motor in each joint current without conventional torque sensors.The humanoid arm adopts an algorithm to avoid obstacles and the dexterous hand is capable of grasping objects.The developed robotic arm is suitable for use in an interactive humanoid robot.     

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.     

A conductive polymer sensor for measuring external finger forces.

This paper describes the construction and use of a durable and thin force sensor that can be attached to the palmar surface of the fingers and hands for studying the biomechanics of grasp and for use in hand injury rehabilitation. These force sensors were constructed using a modified commercially available conductive polymer pressure sensing element and installing an epoxy dome for directing applied forces through a 12 mm diameter active sensing area. The installation of an epoxy dome was effective for making the sensors insensitive to contact surfaces varying from 25 to 1100 mm2 and a 16 mm radius surface curved convex towards the finger. The completed sensors were only 1.8 mm thick and capable of being taped to the distal phalangeal finger pads. They were calibrated on the hand by pinching a strain gage dynamometer. The useful range was between 0 and 30 N with an accuracy of 1 N for both static loading and normal dynamic grasp activities. The sensor time constant was 0.54 ms for a step force input. Because of varying offset voltages every time the sensors were attached, these sensors should be calibrated on the hand before each use. The sensors were used for measuring finger forces during controlled pinching and lifting tasks, and during ordinary grasping activities, such as picking up a book or a box, where the useful force range and response for these sensors were adequate.     

An integrative approach to the biomechanical function and neuromuscular control of the fingers

The exquisite mechanical functionality and versatility of the human hand emerges from complex neuro-musculo-skeletal interactions that are not completely understood. I have found it useful to work within a theoretical/experimental paradigm that outlines the fundamental neuro-musculo-skeletal components and their interactions. In this integrative paradigm, the laws of mechanics, the specifications of the manipulation task, and the sensorimotor signals define the interactions among hand anatomy, the nervous system, and manipulation function. Thus, our collaborative research activities emphasize a firm grounding in the mechanics of finger function, insistence on anatomical detail, and meticulous characterization of muscle activity. This overview of our work on precision pinch (i.e., the ability to produce and control fingertip forces) presents some of our findings around three Research Themes: Mechanics-based quantification of manipulation ability; Anatomically realistic musculoskeletal finger models; and Neural control of finger muscles. I conclude that (i) driving the fingers to some limit of sensorimotor performance is instrumental to elucidating motor control strategies; (ii) that the cross-over of tendons from flexors to extensors in the extensor mechanism is needed to produce force in every direction, and (iii) the anatomical routing of multiarticular muscles makes co-contraction unavoidable for many tasks. Moreover, creating realistic and clinically useful finger models still requires developing new computational means to simulate the viscoelastic tendinous networks of the extensor mechanism, and the muscle-bone-ligament interactions in complex articulations. Building upon this neuromuscular biomechanics paradigm is of immense clinical relevance: it will be instrumental to the development of clinical treatments to preserve and restore manual ability in people suffering from neurological and orthopedic conditions. This understanding will also advance the design and control of robotic hands whose performance lags far behind that of their biological counterparts.     

A physiological basis for control of a prosthetic hand

Recent surveys from upper limb amputees indicate the sentiment that prosthetic hands do not function in a life-like manner and are not intuitively controlled. Thus, two methods of control for a prosthetic hand are presented. A proportional derivative (PD) force controller is compared to a novel biomimetic application of sliding mode control. The biomimetic sliding mode (BSM) controller was designed to map human muscle signals into prosthesis motor command signals in a physiologically expected manner.The BSM and PD controllers were evaluated analytically and subjectively by one amputee and nine nonamputee test subjects. The posture of the hands of the nonamputee test subjects were measured with a CyberGlove and used to determine if the position of the prosthesis (when driven by both controllers) was highly correlated to the posture of the human hands. Force tracking experiments were also performed by all test subjects with both controllers to evaluate the ability to control the applied force. Finally, a dual object lifting task was performed by all test subjects to determine if the mapping of electromyogram (EMG) signals with the BSM controller resulted in physiologically expected motions. A nonparametric Mann–Whitney U-test was performed on the subjective evaluations to determine the statistical significance of the evaluations.The BSM controller was shown to replicate the posture of the human hand much more accurately than the PD force controller. The BSM controller also enabled better average force tracking results and higher success rates with the dual object lifting experiment while the same task was nearly impossible to perform with the PD controller. Finally, the BSM controller was subjectively rated to be more similar to control in comparison to the human hand with respect to position and force.     

Evidence of muscle synergies during human grasping

Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.     

Reductions in finger blood flow in men and women induced by 125-Hz vibration: association with vibration perception thresholds

Vibration of one hand reduces blood flow in the exposed hand and in the contralateral hand not exposed to vibration, but the mechanisms involved are not understood. This study investigated whether vibration-induced reductions in finger blood flow are associated with vibrotactile perception thresholds mediated by the Pacinian channel and considered sex differences in both vibration thresholds and vibration-induced changes in digital circulation. With force and vibration applied to the thenar eminence of the right hand, finger blood flow and finger skin temperature were measured in the middle fingers of both hands at 30-s intervals during seven successive 4-min periods: 1) pre-exposure with no force or vibration, 2) pre-exposure with force, 3) vibration 1, 4) rest with force, 5) vibration 2, 6) postexposure with force, and 7) recovery with no force or vibration. A 2-N force was applied during periods 2-6 and 125-Hz vibration at 0.5 and 1.5 ms(-2) root mean square (r.m.s.; unweighted) was applied during periods 3 and 5, respectively. Vibrotactile thresholds were measured at the thenar eminence of right hand using the same force, contact conditions, and vibration frequency. When the vibration magnitude was greater than individual vibration thresholds, changes in finger blood flow were correlated with thresholds (with both 0.5 and 1.5 ms(-2) r.m.s. vibration): subjects with lower thresholds showed greater reductions in finger blood flow. Women had lower vibrotactile thresholds and showed greater vibration-induced reductions in finger blood flow. It is concluded that mechanoreceptors responsible for mediating vibration perception are involved in the vascular response to vibration.     

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

Background  

Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.     

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.     

Handgrip strength and hand dimensions in young handball and basketball players

In handball and basketball the longer the finger length the better the accuracy of the shot or throw. All shots and throws are finished with the wrist and fingers. It can be proposed that athletes with longer fingers and greater hand surface parameters also probably have greater grip strength. The aim of this study was to investigate the influence of general body and hand-specific anthropometric dimensions on handgrip strength in boys participating in handball and basketball training. In total, 193 boys aged 10-17 years participated in this study. They were divided into 6 groups: 10-, 11-, 12-, 13-, 14-15-, and 16-17-year-olds. The body height and body mass were measured and body mass index was calculated as general anthropometric parameters. The outlines of the hands of the boys were drawn on paper with a thin marker. Three groups of hand anthropometric parameters were measured: 5 finger spans, 5 finger lengths, and 5 perimeters of the hand. Handgrip strength was measured on the dominant hand with a Lafayette dynamometer. As a rule, general anthropometric parameters determined the maximal handgrip strength more accurately than did specific hand anthropometric parameters. From the specific hand anthropometric parameters, finger lengths and perimeters of the hand significantly correlated with the maximal handgrip strength. In summary, fingers are the smallest, lightest parts of the motor apparatus, and, therefore, they represent the parts most easily deflected by force from the ball, but at the same time, finger control is especially important for the accuracy of different shots, both in handball and basketball. Thus, it is especially necessary to measure finger length and perimeters of the hand for practical reasons.     

Two coupled oscillators as a model for the coordinated finger tapping by both hands

Recently, it was found that rhythmic movements (e.g. locomotion, swimmeret beating) are controlled by mutually coupled endogeneous neural oscillators (Kennedy and Davis, 1977; Pearson and Iles, 1973; Stein, 1974; Shik and Orlovsky, 1976; Grillner and Zangger, 1979). Meanwhile, it has been found out that the phase resetting experiment is useful to investigate the interaction of neural oscillators (Perkel et al., 1963; Stein, 1974). In the preceding paper (Yamanishi et al., 1979), we studied the functional interaction between the neural oscillator which is assumed to control finger tapping and the neural networks which control some tasks. The tasks were imposed on the subject as the perturbation of the phase resetting experiment. In this paper, we investigate the control mechanism of the coordinated finger tapping by both hands. First, the subjects were instructed to coordinate the finger tapping by both hands so as to keep the phase difference between two hands constant. The performance was evaluated by a systematic error and a standard deviation of phase differences. Second, we propose two coupled neural oscillators as a model for the coordinated finger tapping. Dynamical behavior of the model system is analyzed by using phase transition curves which were measured on one hand finger tapping in the previous experiment (Yamanishi et al., 1979). Prediction by the model is in good agreement with the results of the experiments. Therefore, it is suggested that the neural mechanism which controls the coordinated finger tapping may be composed of a coupled system of two neural oscillators each of which controls the right and the left finger tapping respectively.     

Tactile feedback plays a critical role in maximum finger force production

This study investigates the role of cutaneous feedback on maximum voluntary force (MVF), finger force deficit (FD) and finger independence (FI). FD was calculated as the difference between the sum of maximal individual finger forces during single-finger pressing tasks and the maximal force produced by those fingers during an all-finger pressing task. FI was calculated as the average non-task finger forces normalized by the task-finger forces and subtracted from 100 percent. Twenty young healthy right-handed males participated in the study. Cutaneous feedback was removed by administering ring block digital anesthesia on the 2nd, 3rd, 4th and 5th digits of the right hands. Subjects were asked to press force sensors with maximal effort using individual digits as well as all four digits together, with and without cutaneous feedback. Results from the study showed a 25% decrease in MVF for the individual fingers as well as all the four fingers pressing together after the removal of cutaneous feedback. Additionally, more than 100% increase in FD after the removal of cutaneous feedback was observed in the middle and ring fingers. No changes in FI values were observed between the two conditions. Results of this study suggest that the central nervous system utilizes cutaneous feedback and the feedback mechanism plays a critical role in maximal voluntary force production by the hand digits.     

Design of a cybernetic hand for perception and action

Strong motivation for developing new prosthetic hand devices is provided by the fact that low functionality and controllability—in addition to poor cosmetic appearance—are the most important reasons why amputees do not regularly use their prosthetic hands. This paper presents the design of the CyberHand, a cybernetic anthropomorphic hand intended to provide amputees with functional hand replacement. Its design was bio-inspired in terms of its modular architecture, its physical appearance, kinematics, sensorization, and actuation, and its multilevel control system. Its underactuated mechanisms allow separate control of each digit as well as thumb–finger opposition and, accordingly, can generate a multitude of grasps. Its sensory system was designed to provide proprioceptive information as well as to emulate fundamental functional properties of human tactile mechanoreceptors of specific importance for grasp-and-hold tasks. The CyberHand control system presumes just a few efferent and afferent channels and was divided in two main layers: a high-level control that interprets the user’s intention (grasp selection and required force level) and can provide pertinent sensory feedback and a low-level control responsible for actuating specific grasps and applying the desired total force by taking advantage of the intelligent mechanics. The grasps made available by the high-level controller include those fundamental for activities of daily living: cylindrical, spherical, tridigital (tripod), and lateral grasps. The modular and flexible design of the CyberHand makes it suitable for incremental development of sensorization, interfacing, and control strategies and, as such, it will be a useful tool not only for clinical research but also for addressing neuroscientific hypotheses regarding sensorimotor control.