Monte Carlo simulation of the effect of melanin concentration on light-tissue interactions in transmittance and reflectance finger photoplethysmography.

Photoplethysmography (PPG) uses light to detect volumetric changes in blood, and is integrated into many healthcare devices to monitor various physiological measurements. However, an unresolved limitation of PPG is the effect of skin pigmentation on the signal and its impact on PPG based applications such as pulse oximetry. Hence, an in-silico model of the human finger was developed using the Monte Carlo (MC) technique to simulate light interactions with different melanin concentrations in a human finger, as it is the primary determinant of skin pigmentation. The AC/DC ratio in reflectance PPG mode was evaluated at source-detector separations of 1 mm and 3 mm as the convergence rate (Q), a parameter that quantifies the accuracy of the simulation, exceeded a threshold of 0.001. At a source-detector separation of 3 mm, the AC/DC ratio of light skin was 0.472 times more than moderate skin and 6.39 than dark skin at 660 nm, and 0.114 and 0.141 respectively at 940 nm. These findings are significant for the development of PPG-based sensors given the ongoing concerns regarding the impact of skin pigmentation on healthcare devices.

Cortical activations associated with spatial remapping of finger touch using EEG.

The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100-125 ms in the parietal cortex.

Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Deficits in force production during multifinger tasks demarcate cognitive dysfunction.

BACKGROUND: The multifinger force deficit (MFFD) is the decline in force generated by each finger as the number of fingers contributing to an action is increased. It has been shown to associate with cognitive status. AIMS: The aim was to establish whether a particularly challenging form of multifinger grip dynamometry, that provides minimal tactile feedback via cutaneous receptors and requires active compensation for reaction forces, will yield an MFFD that is more sensitive to cognitive status. METHODS: Associations between measures of motor function, and cognitive status (Montreal Cognitive Assessment [MoCA]) and latent components of cognitive function (derived from 11 tests using principal component analysis), were estimated cross-sectionally using generalized partial rank correlations. The participants (n = 62) were community dwelling, aged 65-87. RESULTS: Approximately half the participants were unable to complete the dynamometry task successfully. Cognitive status demarcated individuals who could perform the task from those who could not. Among those who complied with the task requirements, the MFFD was negatively correlated with MoCA scores-those with the highest MoCA scores tended to exhibit the smallest deficits, and vice versa. There were corresponding associations with latent components of cognitive function. DISCUSSION: The results support the view that neurodegenerative processes that are a feature of normal and pathological aging exert corresponding effects on expressions of motor coordination-in multifinger tasks, and cognitive sufficiency, due to their dependence on shared neural systems. CONCLUSIONS: The outcomes add weight to the assertion that deficits in force production during multifinger tasks are sensitive to cognitive dysfunction.

Effects of finger pinch motor imagery on short-latency afferent inhibition and corticospinal excitability.

Motor imagery is a cognitive process involving the simulation of motor actions without actual movements. Despite the reported positive effects of motor imagery training on motor function, the underlying neurophysiological mechanisms have yet to be fully elucidated. Therefore, the purpose of the present study was to investigate how sustained tonic finger-pinching motor imagery modulates sensorimotor integration and corticospinal excitability using short-latency afferent inhibition (SAI) and single-pulse transcranial magnetic stimulation (TMS) assessments, respectively. Able-bodied individuals participated in the study and assessments were conducted under two experimental conditions in a randomized order between participants: (1) participants performed motor imagery of a pinch task while observing a visual image displayed on a monitor (Motor Imagery), and (2) participants remained at rest with their eyes fixed on the monitor displaying a cross mark (Control). For each condition, sensorimotor integration and corticospinal excitability were evaluated during sustained tonic motor imagery in separate sessions. Sensorimotor integration was assessed by SAI responses, representing inhibition of motor-evoked potentials (MEPs) in the first dorsal interosseous muscle elicited by TMS following median nerve stimulation. Corticospinal excitability was assessed by MEP responses elicited by single-pulse TMS. There was no significant difference in the magnitude of SAI responses between motor imagery and Control conditions, while MEP responses were significantly facilitated during the Motor Imagery condition compared to the Control condition. These findings suggest that motor imagery facilitates corticospinal excitability, without altering sensorimotor integration, possibly due to insufficient activation of the somatosensory circuits or lack of afferent feedback during sustained tonic motor imagery.

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction.

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Effects of sensory afferent input on motor cortex excitability of agonist and antagonist muscles.

In this study, we aimed to analyze control mechanisms of short-latency afferent inhibition (SAI) during motor output exertion from an agonist or antagonist muscle. The motor task involved index finger abduction (agonist) and adduction (antagonist). In Experiment 1, motor-evoked potentials (MEPs) were recorded from the first dorsal interosseous (FDI) muscle with and without SAI at three output force levels. In Experiment 2, MEPs were recorded with and without SAI at various time points immediately before the muscle output. Experiment 1 showed that inhibition decreased with an increase in muscle output in the agonist muscle but increased in the antagonist muscle. Experiment 2 showed a decreasing trend of inhibition in the agonist muscle immediately before contraction but showed no significant change in the antagonist muscle. MEPs without electrical stimulation during the reaction time increased in both directions of movement as compared to those in the resting state. These results suggest that SAI modulation strongly influences smooth motor output. Analyzing the inhibitory or enhanced mechanisms during the performance of motor output by SAI in patients with motor impairment and comparing them with the mechanisms seen in healthy participants will improve our understanding of the neurophysiological mechanisms relevant to various situations (e.g., rehabilitation and sports).

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

Irregularity of instantaneous gamma frequency in the motor control network characterize visuomotor and proprioceptive information processing.

Objective.The study aims to characterize movements with different sensory goals, by contrasting the neural activity involved in processing proprioceptive and visuo-motor information. To accomplish this, we have developed a new methodology that utilizes the irregularity of the instantaneous gamma frequency parameter for characterization.Approach.In this study, eight essential tremor patients undergoing an awake deep brain stimulation implantation surgery repetitively touched the clinician's finger (forward visually-guided/FV movement) and then one's own chin (backward proprioceptively-guided/BP movement). Neural electrocorticographic recordings from the motor (M1), somatosensory (S1), and posterior parietal cortex (PPC) were obtained and band-pass filtered in the gamma range (30-80 Hz). The irregularity of the inter-event intervals (IEI; inverse of instantaneous gamma frequency) were examined as: (1) auto-information of the IEI time series and (2) correlation between the amplitude and its proceeding IEI. We further explored the network connectivity after segmenting the FV and BP movements by periods of accelerating and decelerating forces, and applying the IEI parameter to transfer entropy methods.Main results.Conceptualizing that the irregularity in IEI reflects active new information processing, we found the highest irregularity in M1 during BP movement, highest in PPC during FV movement, and the lowest during rest at all sites. Also, connectivity was the strongest from S1 to M1 and from S1 to PPC during FV movement with accelerating force and weakest during rest.Significance. We introduce a novel methodology that utilize the instantaneous gamma frequency (i.e. IEI) parameter in characterizing goal-oriented movements with different sensory goals, and demonstrate its use to inform the directional connectivity within the motor cortical network. This method successfully characterizes different movement types, while providing interpretations to the sensory-motor integration processes.

Evolution, biomechanics, and neurobiology converge to explain selective finger motor control.

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.

Three Sliding Probes Placed on Forelimb Skin for Proprioceptive Feedback Differentially yet Complementarily Contribute to Hand Gesture Detection and Object-Size Discrimination.

The purpose was to assess the effectiveness of three sliding tactile probes placed on the forelimb skin to provide proprioceptive feedback for the detection of hand gestures and discrimination of object size. Tactile contactors representing the first three fingers were driven along the proximodistal axis by linear servo motors. Twenty healthy subjects were involved in the gesture detection test, with 10 of them also participating in the object-size discrimination task. Motors were controlled by computer in the first four sessions of the gesture detection experiment, while the fifth session utilized a sensorized glove. Both the volar and dorsal sides of the forearm were examined. In the object-size discrimination experiment, the method was exclusively assessed on the volar surface under four distinct feedback conditions, including all fingers and each finger separately. The psychophysical data were further analyzed using a structural equation model (SEM) to evaluate the specific contributions of each individual contactor. Subjects consistently outperformed the chance level in detecting gestures. Performance improved up to the third session, with better results obtained on the volar side. The performances were similar in the fourth and fifth sessions. The just noticeable difference for achieving a 75% discrimination accuracy was found to be 2.90 mm of movement on the skin. SEM analysis indicated that the contactor for the index finger had the lowest importance in gesture detection, while it played a more significant role in object-size discrimination. However, all fingers were found to be significant predictors of subjects' responses in both experiments, except for the thumb, which was deemed insignificant in object-size discrimination. The study highlights the importance of considering the partial contribution of each degree of freedom in a sensory feedback system, especially concerning the task, when designing such systems.

Fast grip force adaptation to friction relies on localized fingerpad strains.

During object manipulation, humans adjust the grip force to friction, such that slippery objects are squeezed more firmly than sticky ones. This essential mechanism to keep a stable grasp relies on feedback from tactile afferents innervating the fingertips, that are sensitive to local skin strains. To test if this feedback originates from the skin-object interface, we asked participants to perform a grip-lift task with an instrumented object able to monitor skin strains at the contact through transparent plates of different frictions. We observed that, following an unbeknown change in plate across trials, participants adapted their grip force to friction. After switching from high to low friction, we found a significant increase in strain inside the contact arising ~100 ms before the modulation of grip force, suggesting that differences in strain patterns before lift-off are used by the nervous system to quickly adjust the force to the frictional properties of manipulated objects.

The superior colliculus motor region does not respond to finger tapping movements in humans.

Electrophysiological studies in macaques and functional neuroimaging in humans revealed a motor region in the superior colliculus (SC) for upper limb reaching movements. Connectivity studies in macaques reported direct connections between this SC motor region and cortical premotor arm, hand, and finger regions. These findings motivated us to investigate if the human SC is also involved in sequential finger tapping movements. We analyzed fMRI task data of 130 subjects executing finger tapping from the Human Connectome Project. While we found strong signals in the SC for visual cues, we found no signals related to simple finger tapping. In subsequent experimental measurements, we searched for responses in the SC corresponding to complex above simple finger tapping sequences. We observed expected signal increases in cortical motor and premotor regions for complex compared to simple finger tapping, but no signal increases in the motor region of the SC. Despite evidence for direct anatomical connections of the SC motor region and cortical premotor hand and finger areas in macaques, our results suggest that the SC is not involved in simple or complex finger tapping in humans.

Open-Source Instrumented Object to Study Dexterous Object Manipulation.

Humans use tactile feedback to perform skillful manipulation. When tactile sensory feedback is unavailable, for instance, if the fingers are anesthetized, dexterity is severely impaired. Imaging the deformation of the finger pad skin when in contact with a transparent plate provides information about the tactile feedback received by the central nervous system. Indeed, skin deformations are transduced into neural signals by the mechanoreceptors of the finger pad skin. Understanding how this feedback is used for active object manipulation would improve our understanding of human dexterity. In this paper, we present a new device for imaging the skin of the finger pad of one finger during manipulation performed with a precision grip. The device's mass (300 g) makes it easy to use during unconstrained dexterous manipulation. Using this device, we reproduced the experiment performed in Delhaye et al. (2021) We extracted the strains aligned with the object's movement, i.e., the vertical strains in the ulnar and radial parts of the fingerpad, to see how correlated they were with the grip force (GF) adaptation. Interestingly, parts of our results differed from those in Delhaye et al. (2021) due to weight and inertia differences between the devices, with average GF across participants differing significantly. Our results highlight a large variability in the behavior of the skin across participants, with generally low correlations between strain and GF adjustments, suggesting that skin deformations are not the primary driver of GF adaptation in this manipulation scenario.

Simple recognition of hand gestures using single-channel EMG signals.

Electromyography (EMG) signals are used for many different purposes, such as recording and measuring the electrical activity generated by varying the body's skeletal muscles. Biosignals are different types of biomedical signals, like EMG signals, which can be used for the neural linkage with computers and are obtained from a particular part of the body such as tissue, muscle, organ, or cell system like the nervous system. Surface electromyography (SEMG) is a non-invasive method that can be used as an effective system for controlling upper arm prostheses. This study focused on classifying the five types of distinct finger movements investigated in four unique channels.We have used a classification technique, the k-nearest neighbors (KNN), to categorize the collected samples. Two time-domain features, (a) maximum (Max) and (b) minimum (Min), were used with one of these three features separately: mean absolute value (MAV), root mean square (RMS), and simple square integral (SSI) to classify gestures. We chose classification accuracy as a criterion for evaluating the effectiveness of every classification. We figured out that the first grouping, that is, (MAV, Max, Min), was the best choice for classification. The accuracy percentage in the four channels for the first group was 91.0%, 89.9%, 89.8%, and 96.0%, respectively.

Observing finger movement influences the stimulus-response process of the subsequent non-aiming finger movement.

AIM: The present study investigated whether observing the finger movement influences the stimulus-response process of the subsequent non-aiming finger movement. METHODS: Participants directed their eyes to the finger. Three auditory cues with 3 s intervals were provided in each trial. The participants abducted and adducted the index finger in response to the second and third cues; the first response was considered to be the previous response and the second response was considered to be the subsequent response. The time taken for the stimulus-response process was measured via reaction time. Vision was allowed from 0 to 1 s after the start cue of the previous response, after the cue of the subsequent response, or after the cues of the previous and subsequent responses. RESULTS: Online visual information of the stationary finger accelerated the stimulus-response process of the non-aiming finger movement. The acceleration of the stimulus-response process induced by online visual information of the stationary finger was cancelled out by the previous response information, but this cancellation is itself then eliminated by the visual information from the previous response. The visual information from the previous response decelerated the stimulus-response process of the subsequent non-aiming movement, but this deceleration was then itself cancelled out by visual information of the stationary finger immediately before the subsequent non-aiming movement. CONCLUSION: Taken together, information regarding the previous response functions as noise interfering with the processes contributing to the subsequent non-aiming movement.

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients.

Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints were obtained using the SURF and RANSAC algorithms. Furthermore, the trajectory of finger joint end and the correlation equation of finger joint motion were fitted, and a comprehensive finger kinematic model was established. Based on this finger kinematic model, a novel under-actuated exoskeleton mechanism was designed using a human-machine integration approach. The novel robotic glove fully aligns with the equivalent rotation centers and natural motion trajectories of the fingers, exerting minimal and evenly distributed dynamic pressure on the fingers, with a theoretical static pressure value of zero. Experiments involving gripping everyday objects demonstrated that the novel robotic glove significantly reduces the overall pressure on the fingers during grasping compared to the pneumatic glove and the traditional exoskeleton robotic glove. It is suitable for long-term use by stroke patients for rehabilitation training.

Hello from the other side: Robust contralateral interference in tactile detection.

Touch is unique among the sensory modalities in that our tactile receptors are spread across the body surface and continuously receive different inputs at the same time. These inputs vary in type, properties, relevance according to current goals, and, of course, location on the body. Sometimes, they must be integrated, and other times set apart and distinguished. Here, we investigate how simultaneous stimulation to different body sites affects tactile cognition. Specifically, we characterized the impact of irrelevant tactile sensations on tactile change detection. To this end, we embedded detection targets amidst ongoing performance, akin to the conditions encountered in everyday life, where we are constantly confronted with new events within ongoing stimuli. In the set of experiments presented here, participants detected a brief intensity change (.04 s) within an ongoing vibrotactile stimulus (1.6 s) that was always presented in a constantly attended location. The intensity change (i.e., the detection target) varied parametrically, from hardly detectable to easily detectable. In half of the trials, irrelevant ongoing stimulation was simultaneously presented to a site across the body midline, but participants were instructed to ignore it. In line with previous bimanual studies employing brief onset targets, we document robust interference on performance due to the irrelevant stimulation at each of the measured body sites (homologous and nonhomologous fingers, and the contralateral ankle). After describing this basic phenomenon, we further examine the conditions under which such interference occurs in three additional tasks. In each task, we honed in on a different aspect of the stimulation protocol (e.g., hand distance, the strength of the irrelevant stimulation, the detection target itself) in order to better understand the principles governing the observed interference effects. Our findings suggest a minimal role for exogenous attentional capture in producing the observed interference effects (Exp. 2), and a principled distribution of attentional resources or sensory integration between body sides (Exps. 3, 4). In our last study (Exp. 4), we presented bilateral tactile targets of varying intensities to both the relevant and irrelevant stimulation sites. We then characterized the degree to which the irrelevant stimulation is also processed. Our results-that participants' perception of target intensity is always proportional to the combined bilateral signal-suggest that both body sites are equally weighed and processed despite clear instructions to attend only the target site. In light of this observation and participants' inability to use selection processes to guide their perception, we propose that bilateral tactile inputs are automatically combined, quite possibly early in the hierarchy of somatosensory processing.

Cold-induced vasodilation during sequential immersions of the hand.

A common practice for those operating in cold environments includes repetitive glove doffing and donning to perform specific tasks, which creates a repetitive cycle of hand cooling and rewarming. This study aimed to determine the influence of intraday repeated hand cooling on cold-induced vasodilation (CIVD), sympathetic activation, and finger/hand temperature recovery. Eight males and two females (mean ± SD age: 28 ± 5 year; height: 181 ± 9 cm; weight: 79.9 ± 10.4 kg) performed two 30-min hand immersions in cold (4.3 ± 0.92 °C) water in an indoor environment (18 °C). Both immersions (Imm1; Imm2) were performed on the same day and both allowed for a 10-min recovery. CIVD components were calculated for each finger (index, middle, ring) during each immersion. CIVD onset time (index, p = 0.546; middle, p = 0.727; ring, p = 0.873), minimum finger temperature (index, p = 0.634; middle, p = 0.493; ring, p = 0.575), and mean finger temperature (index, p = 0.986; middle, p = 0.953; ring, p = 0.637) were all similar between immersions. Recovery rates generally demonstrated similar responses as well. Findings suggest that two sequential CIVD tests analyzing the effect of prior cold exposure of the hand does not impair the CIVD response or recovery. Such findings appear promising for those venturing into cold environments where hands are likely to be repeatedly exposed to cold temperatures.

Learning a covert sequence of effector movements: limits to its acquisition.

Sequence learning in serial reaction time (SRT) tasks is an established, lab-based experimental paradigm to study acquisition and transfer of skills based on the detection of predictable regularities in stimulus and motor response sequences. Participants learn a sequence of targets and responses to these targets by associating the responses with subsequently presented targets. In the traditional paradigm, however, actions and response targets are directly related. In contrast, the present study asked whether participants would demonstrate acquisition of a sequence of effector movements of the left vs. right hand (e.g., hand sequence learning), whilst the actual targets and associated finger responses are unpredictable. Twenty-seven young adults performed a SRT task to visually presented characters with the index or middle fingers of both hands. While the specific fingers to respond with were randomly selected for each target presentation, both hands followed a covert sequence. We asked whether participants would learn the underlying hand sequence as demonstrated by shortened response latencies and increased accuracy compared to a fully randomized hand sequence. The results show sequence-specific learning effects. However, categorization of hand responses depending on the previous response suggested that learning occurred predominantly for subsequent finger responses of the same hand, which added to general hand-based priming. Nevertheless, a marginally significant effect was observed even for predictable shifts between hands when homologous fingers were involved. Our results thus suggest that humans are able to benefit from predictable within-hand finger shifts but less so for predicted shifts between hands.