Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types.

Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone's intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices.

Electromyography and kinematics data of the hand in activities of daily living with special interest for ergonomics.

This work presents a dataset of human hand kinematics and forearm muscle activation collected during the performance of a wide variety of activities of daily living (ADLs), with tagged characteristics of products and tasks. A total of 26 participants performed 161 ADLs selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices). The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. Data of more than 4100 ADLs is presented in this dataset as MATLAB structures with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour. The dataset is accompanied with a custom data visualization application (ERGOMOVMUS) as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or participants' characteristics.

Impact of hand osteoarthritis in women on maximal forces in six different grasp types.

This work aims to: (1) Provide maximal hand force data on six different grasp types for healthy subjects; (2) detect grasp types with maximal force significantly affected by hand osteoarthritis (HOA) in women; (3) look for predictors to detect HOA from the maximal forces using discriminant analyses. Thirty-three healthy subjects (37 ± 17 years, 17 women, 16 men) and 30 HOA patients (72 ± 9 years, all women) participated in the experiment. Participants were asked to exert their maximal force while performing six different grasp types 3 times. Two MANOVAs were conducted to detect if force depended on gender in healthy participants and if force significantly diminished in women with HOA. Finally, a linear discriminant analysis for detecting HOA was performed using forces of the grasp types that were significantly affected by HOA. Gender-disaggregated statistics are provided for healthy participants. Significant differences are obtained for all grasp types per gender. The women with HOA exerted significantly lower force values (p < 0.001) for all the grasp types than healthy ones. The discriminant analysis revealed that oblique grasp was the most significant one for detecting HOA. A discrimination equation was obtained with a specificity of 88.2% and a sensitivity of 83.3%. This work provides grip force data on six grasp types for healthy participants and for women with HOA. HOA women present reduced strength in all grasps due to pathology. Three of these grasps are a novelty. Oblique grasp strength may suffice to discriminate a patient with HOA, which might help non-invasive HOA detection.

The BE-UJI hand function activity set: a reduced set of activities for the evaluation of the healthy and pathological hand.

BACKGROUND: Hand kinematics during hand function tests based on the performance of activities of daily living (ADLs) can provide objective data to determine patients' functional loss. However, they are rarely used during clinical assessments because of their long duration. Starting with the 20 Sollerman Hand Function Test (SHFT) tasks, we propose identifying a reduced set of ADLs that provides similar kinematic information to the original full set in terms of synergies, ranges of motion and velocities. METHODS: We followed an iterative method with the kinematics of 16 hand joints while performing the 20 ADLs of the SHFT. For each subject, ADLs were ordered according to their influence on the synergies obtained by means of a principal component analysis, the minimum number of ADLs that represented the original kinematic synergies (maximum angle of 30° between synergies), and the maintained ranges of joint movements (85% of the original ones) were selected for each subject. The set of the most frequently selected ADLs was verified to be representative of the SHFT ADLs in terms of motion strategies, ranges of motion and joint velocities when considering healthy subjects and Hand Osteoarthritis patients. RESULTS: A set of 10 tasks, the BE-UJI activity set, was identified by ensuring a certain (minimum) similarity in synergy (maximum mean angle between synergies of 25.5°), functional joint ranges (maximum differences of 10°) and joint velocities (maximum differences of 15°/s). The obtained tasks were: pick up coins from purses, lift wooden cubes, pick up nuts and turn them, write with a pen, cut with a knife, lift a telephone, unscrew jar lids and pour water from a cup, a jar and a Pure-Pak. These activities guarantee using the seven commonest handgrips in ADLs. CONCLUSION: The BE-UJI activity set for the hand function assessment can be used to obtain quantitative data in clinics as an alternative to the SHFT. It reduces the test time and allows clinicians to obtain objective kinematic data of the motor strategies, ranges of motion and joint velocities used by patients.

Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps.

The early and objective detection of hand pathologies is a field that still requires more research. One of the main signs of hand osteoarthritis (HOA) is joint degeneration, which causes loss of strength, among other symptoms. HOA is usually diagnosed with imaging and radiography, but the disease is in an advanced stage when HOA is observable by these methods. Some authors suggest that muscle tissue changes seem to occur before joint degeneration. We propose recording muscular activity to look for indicators of these changes that might help in early diagnosis. Muscular activity is often measured using electromyography (EMG), which consists of recording electrical muscle activity. The aim of this study is to study whether different EMG characteristics (zero crossing, wavelength, mean absolute value, muscle activity) via collection of forearm and hand EMG signals are feasible alternatives to the existing methods of detecting HOA patients' hand function. We used surface EMG to measure the electrical activity of the dominant hand's forearm muscles with 22 healthy subjects and 20 HOA patients performing maximum force during six representative grasp types (the most commonly used in ADLs). The EMG characteristics were used to identify discriminant functions to detect HOA. The results show that forearm muscles are significantly affected by HOA in EMG terms, with very high success rates (between 93.3% and 100%) in the discriminant analyses, which suggest that EMG can be used as a preliminary step towards confirmation with current HOA diagnostic techniques. Digit flexors during cylindrical grasp, thumb muscles during oblique palmar grasp, and wrist extensors and radial deviators during the intermediate power-precision grasp are good candidates to help detect HOA.

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues.

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

Synergy-Based Sensor Reduction for Recording the Whole Hand Kinematics.

Simultaneous measurement of the kinematics of all hand segments is cumbersome due to sensor placement constraints, occlusions, and environmental disturbances. The aim of this study is to reduce the number of sensors required by using kinematic synergies, which are considered the basic building blocks underlying hand motions. Synergies were identified from the public KIN-MUS UJI database (22 subjects, 26 representative daily activities). Ten synergies per subject were extracted as the principal components explaining at least 95% of the total variance of the angles recorded across all tasks. The 220 resulting synergies were clustered, and candidate angles for estimating the remaining angles were obtained from these groups. Different combinations of candidates were tested and the one providing the lowest error was selected, its goodness being evaluated against kinematic data from another dataset (KINE-ADL BE-UJI). Consequently, the original 16 joint angles were reduced to eight: carpometacarpal flexion and abduction of thumb, metacarpophalangeal and interphalangeal flexion of thumb, proximal interphalangeal flexion of index and ring fingers, metacarpophalangeal flexion of ring finger, and palmar arch. Average estimation errors across joints were below 10% of the range of motion of each joint angle for all the activities. Across activities, errors ranged between 3.1% and 16.8%.

Biomechanical function requirements of the wrist. Circumduction versus flexion/abduction range of motion.

The biomechanical function of the wrist is widely assessed by measuring the range of motion (RoM) in two separate orthogonal planes: flexion-extension (FE) and radioulnar deviation (RUD). However, the two motions are coupled. The aim of this study is to compare wrist circumduction with FE and RUD RoM in terms of representativeness of the kinematic requirements for performing activities of daily living (ADL). To this end, the wrist motion of healthy participants was measured while performing maximum RoM in FE and in RUD, circumduction, and thirty-two representative ADL. Active and functional RoM (ARoM and FRoM) were computed in each plane, the evolving circumduction curves were adjusted to ellipses, and intensity maps representing the frequency of the coupling angles in ADL were plotted, both per ADL and globally for both hands. Ellipses representing different percentages of coupling angles in ADL were also plotted. Wrist circumduction fits the coupling angles measured in ADL better than ARoM or FRoM. As a novelty, quantitative data for both circumduction and the coupling angles required in ADL are provided, shedding light on the real biomechanical function requirements of the wrist. Results might be used to quantify mobility reduction and its impact on the performance of ADL, globally and per ADL, to enhance rehabilitation strategies, as well as in clinical decision-making, robotics, and prostheses.

Publisher Correction: Sharing of hand kinematic synergies across subjects in daily living activities.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Hand Kinematics Characterization While Performing Activities of Daily Living Through Kinematics Reduction.

Improving the understanding of hand kinematics during the performance of activities of daily living may help improve the control of hand prostheses and hand function assessment. This work identifies sparse synergies (each degree of freedom is present mainly in only one synergy), representative of the global population, with emphasis in unveiling the coordination of joints with small range of motion (palmar arching and fingers abduction). The study is the most complete study described in the literature till now, involving 22 healthy subjects and 26 representative day-to-day life activities. Principal component analysis was used to reduce the original 16 angles recorded with an instrumented glove. Five synergies explained 75% of total variance: closeness (coordinated flexion and abduction of metacarpophalangeal finger joints), digit arching (flexion of proximal interphalangeal joints), palmar-thumb coordination (coordination of palmar arching and thumb carpometacarpal flexion), thumb opposition, and thumb arch. The temporal evolution of these synergies is provided during reaching per intended grasp and during manipulation per specific task, which could be used as normative patterns for the global population. Reaching has been observed to require the modulation of closeness, digit arch and thumb opposition synergies, with different control patterns per grasp. All the synergies are very important during manipulation and need to be modulated for all the tasks. Finally, groups of tasks with similar kinematic requirements in terms of synergies have been identified, which could benefit the selection of tasks for rehabilitation and hand function assessments.

Sharing of hand kinematic synergies across subjects in daily living activities.

The motor system is hypothesised to use kinematic synergies to simplify hand control. Recent studies suggest that there is a large set of synergies, sparse in degrees of freedom, shared across subjects, so that each subject performs each action with a sparse combination of synergies. Identifying how synergies are shared across subjects can help in prostheses design, in clinical decision-making or in rehabilitation. Subject-specific synergies of healthy subjects performing a wide number of representative daily living activities were obtained through principal component analysis. To make synergies comparable between subjects and tasks, the hand kinematics data were scaled using normative range of motion data. To obtain synergies sparse in degrees of freedom a rotation method that maximizes the sum of the variances of the squared loadings was applied. Resulting synergies were clustered and each cluster was characterized by a core synergy and different indexes (prevalence, relevance for function and within-cluster synergy similarity), substantiating the sparsity of synergies. The first two core synergies represent finger flexion and were present in all subjects. The remaining core synergies represent coordination of the thumb joints, thumb-index joints, palmar arching or fingers adduction, and were employed by subjects in different combinations, thus revealing different subject-specific strategies.

A large calibrated database of hand movements and grasps kinematics.

Modelling hand kinematics is a challenging problem, crucial for several domains including robotics, 3D modelling, rehabilitation medicine and neuroscience. Currently available datasets are few and limited in the number of subjects and movements. The objective of this work is to advance the modelling of hand kinematics by releasing and validating a large publicly available kinematic dataset of hand movements and grasp kinematics. The dataset is based on the harmonization and calibration of the kinematics data of three multimodal datasets previously released (Ninapro DB1, DB2 and DB5, that include electromyography, inertial and dynamic data). The novelty of the dataset is related to the high number of subjects (77) and movements (40 movements, each repeated several times) for which we release for the first time calibrated kinematic data, resulting in the largest available kinematic dataset. Differently from the previous datasets, the data are also calibrated to avoid sensor nonlinearities. The validation confirms that the data are not affected by experimental procedures and that they are similar to data acquired in real-life conditions.

Effect on manual skills of wearing instrumented gloves during manipulation.

Instrumented gloves are motion capture systems that are widely used due to the simplicity of the setup required and the absence of occlusion problems when manipulating objects. Nevertheless, the effect of their use on manipulation capabilities has not been studied to date. Therefore, the aim of this work is to quantify the effect of wearing CyberGlove instrumented gloves on these capabilities when different levels of precision are required. Thirty healthy subjects were asked to perform three standardised dexterity tests twice: bare-handed and wearing instrumented gloves. The tests were the Sollerman Hand Function Test (to evaluate capability of performing activities of daily living), the Box and Block Test (to evaluate gross motor skills) and the Purdue Pegboard Test (to evaluate fine motor skills). Scores obtained in the test evaluating fine motor skills decreased by an average of 29% when wearing gloves, while scores obtained on those evaluating gross motor skills and capability to perform activities of daily living were reduced by an average of 8% and 3%, respectively. The use of instrumented gloves to record hand kinematics is only recommended when performing tasks requiring medium and gross motor skills.

A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living.

Linking hand kinematics and forearm muscle activity is a challenging and crucial problem for several domains, such as prosthetics, 3D modelling or rehabilitation. To advance in this relationship between hand kinematics and muscle activity, synchronised and well-defined data are needed. However, currently available datasets are scarce, and the presented tasks and data are often limited. This paper presents the KIN-MUS UJI Dataset that contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.

Effect on hand kinematics when using assistive devices during activities of daily living.

Assistive devices (ADs) are products intended to overcome the difficulties produced by the reduction in mobility and grip strength entailed by ageing and different pathologies. Nevertheless, there is little information about the effect that the use of these devices produces on hand kinematics. Thus, the aim of this work is to quantify this effect through the comparison of kinematic parameters (mean posture, ROM, median velocity and peak velocity) while performing activities of daily living (ADL) using normal products and ADs. Twelve healthy right-handed subjects performed 11 ADL with normal products and with 17 ADs wearing an instrumented glove on their right hand, 16 joint angles being recorded. ADs significantly affected hand kinematics, although the joints affected differed according to the AD. Furthermore, some pattern effects were identified depending on the characteristics of the handle of the ADs, namely, handle thickening, addition of a handle to products that initially did not have one, extension of existing handles or addition of handles to apply higher torques. An overview of the effects of these design characteristics on hand kinematics is presented as a basis for the selection of the most suitable AD depending on the patient's impairments.

Human hand kinematic data during feeding and cooking tasks.

This work presents a database of human hand kinematics containing data collected during the performance of a wide variety of activities of daily living involving feeding and cooking. The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each. A total of 20 subjects participated in each part of the experiment, and the objects and their arrangement were the same across subjects, although they performed the tasks in a natural non-directed way. This dataset contains a total of 1160 continuous calibrated recordings taken at 100 Hz during the performance of the tasks, with filtered signal. Statistical descriptive analyses from these data are presented. This database can be useful for machine learning purposes and prostheses control, as well as for the characterization of healthy human hand kinematics.

Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset.

BACKGROUND: Hand grasp patterns require complex coordination. The reduction of the kinematic dimensionality is a key process to study the patterns underlying hand usage and grasping. It allows to define metrics for motor assessment and rehabilitation, to develop assistive devices and prosthesis control methods. Several studies were presented in this field but most of them targeted a limited number of subjects, they focused on postures rather than entire grasping movements and they did not perform separate analysis for the tasks and subjects, which can limit the impact on rehabilitation and assistive applications. This paper provides a comprehensive mapping of synergies from hand grasps targeting activities of daily living. It clarifies several current limits of the field and fosters the development of applications in rehabilitation and assistive robotics. METHODS: In this work, hand kinematic data of 77 subjects, performing up to 20 hand grasps, were acquired with a data glove (a 22-sensor CyberGlove II data glove) and analyzed. Principal Component Analysis (PCA) and hierarchical cluster analysis were used to extract and group kinematic synergies that summarize the coordination patterns available for hand grasps. RESULTS: Twelve synergies were found to account for > 80% of the overall variation. The first three synergies accounted for more than 50% of the total amount of variance and consisted of: the flexion and adduction of the Metacarpophalangeal joint (MCP) of fingers 3 to 5 (synergy #1), palmar arching and flexion of the wrist (synergy #2) and opposition of the thumb (synergy #3). Further synergies refine movements and have higher variability among subjects. CONCLUSION: Kinematic synergies are extracted from a large number of subjects (77) and grasps related to activities of daily living (20). The number of motor modules required to perform the motor tasks is higher than what previously described. Twelve synergies are responsible for most of the variation in hand grasping. The first three are used as primary synergies, while the remaining ones target finer movements (e.g. independence of thumb and index finger). The results generalize the description of hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics.

Effect of assistive devices on hand and arm posture during activities of daily living.

Assistive devices (ADs) are products designed to overcome the grip strength and mobility difficulties produced by ageing and different pathologies. Nevertheless, little is known about the postural effect of such devices. This work aims to quantify this effect on the entire upper limb. Ten healthy right-handed subjects performed 13 activities of daily living (ADL) with normal products and 22 ADs and both arm (shoulder, elbow and wrist) and hand (grasp types and contacts) postures were analysed. ADs were found to affect upper limb postures in ADL, reducing the use of precision grasps in the right hand by 31.9% and increasing palm contact by 26% and 29.1% in right and left hands, respectively. Nevertheless, they were also found to increase shoulder flexion, elbow pronation and wrist deviation, which may be a drawback in some pathologies. Results may help in the selection of a suitable AD for enhancing ADL performance depending on the patient's limitations due to a particular pathology.

Identification of forearm skin zones with similar muscle activation patterns during activities of daily living.

BACKGROUND: A deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients' ability to perform activities of daily living. METHODS: In this work, we recorded sEMG activity from 30 spots distributed over the skin of the whole forearm of six subjects during the performance of 21 representative simulated ADL from the Sollerman Hand Function Test. Functional principal component analysis and hierarchical cluster analysis (HCA) were used to identify forearm spots with similar muscle activation patterns. RESULTS: The best classification of spots with similar activity in simulated ADL consisted in seven muscular-anatomically coherent groups: (1) wrist flexion and ulnar deviation; (2) wrist flexion and radial deviation; (3) digit flexion; (4) thumb extension and abduction/adduction; (5) finger extension; (6) wrist extension and ulnar deviation; and (7) wrist extension and radial deviation. CONCLUSION: The number of sEMG sensors could be reduced from 30 to 7 without losing any relevant information, using them as representative spots of the muscular activity of the forearm in simulated ADL. This may help to assess muscle function in rehabilitation while also simplifying the complexity of prosthesis control.

Validity of a simple videogrammetric method to measure the movement of all hand segments for clinical purposes.

Hand movement measurement is important in clinical, ergonomics and biomechanical fields. Videogrammetric techniques allow the measurement of hand movement without interfering with the natural hand behaviour. However, an accurate measurement of the hand movement requires the use of a high number of markers, which limits its applicability for the clinical practice (60 markers would be needed for hand and wrist). In this work, a simple method that uses a reduced number of markers (29), based on a simplified kinematic model of the hand, is proposed and evaluated. A set of experiments have been performed to evaluate the errors associated with the kinematic simplification, together with the evaluation of its accuracy, repeatability and reproducibility. The global error attributed to the kinematic simplification was 6.68°. The method has small errors in repeatability and reproducibility (3.43° and 4.23°, respectively) and shows no statistically significant difference with the use of electronic goniometers. The relevance of the work lies in the ability of measuring all degrees of freedom of the hand with a reduced number of markers without interfering with the natural hand behaviour, which makes it suitable for its use in clinical applications, as well as for ergonomic and biomechanical purposes.