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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.

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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.

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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.

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PURPOSE: Propose further indicators for helping to detect hand osteoarthritis (HOA) using either active or functional ranges of motion (AROM or FROM). METHOD: Hand kinematics data of 16 hand joint angles from previous studies were used, taken from healthy participants and HOA patients with differently affected joints and distinct levels of compromise. Data consisted of: (i) AROM (extreme values and ranges); (ii) FROM while performing the Sollerman Hand Function Test (mean, extreme percentiles and ranges). Two linear discriminant analyses (stepwise method) were conducted, one per dataset (AROM and FROM), with condition (healthy/patient) as the grouping variable. The potential predictors were the data from the joints with significant differences between samples for each analysis (A-predictors and F-predictors). RESULTS: Good sensitivity-specificity values of 85.2-90.9% and 93.8-93.9% for F-predictors and A-predictors, respectively. Sets of predictors corresponded to the joints more commonly affected by HOA. F-predictors: lower maximal flexion of carpometacarpal and interphalangeal thumb joints, higher maximal flexion of thumb metacarpal joint, lower flexion/extension range of ring proximal interphalangeal joint and higher maximal little finger adduction. A-predictors: narrower flexion/extension range of the thumb carpometacarpal joint, lesser extension of the ring metacarpophalangeal joint; lower flexion of the middle finger proximal interphalangeal joint along with a narrower palmar arch range. CONCLUSION: Both sets of predictors provide discrimination capacity of HOA with good sensitivity-specificity, slightly better for A-predictors. The AROM measurement is technically less demanding and can be clinically applied even with manual goniometry.

Range of motion is affected by hand osteoarthritis even before joint deformity is noticeable.Active (AROM) or functional (FROM) ranges of motion can help to identify potential hand osteoarthritis and opening up the possibility of applying rehabilitation treatments to improve quality of life.The AROM measurement is technically less demanding and can be clinically applied even with manual goniometry.

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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.

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PURPOSE: To identify the impact of kinematic limitations on hand osteoarthritis patients' ability to perform daily living activities. METHODS: An experiment was performed on 33 patients and 32 healthy subjects. Active ranges of motion (AROM) of 16 hand joint angles were measured, together with scores of different hand tests of dexterity (Box and Block, Nine Hole Peg, Kapandji) and function (Sollerman Hand Function Test, SHFT). Functional ranges of motion (FROM) were recorded during SHFT tasks. Results by task are also reported. RESULTS: Patients' AROM is limited in flexion of thumb carpometacarpal and interphalangeal, and finger metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints, and in palmar arch. Patients scored worse in gross dexterity and opposition, but only Kapandji score was correlated with AROM limitations. Pain is mostly reported in patients with limited extension of finger MCP and PIP joints. Patients used significantly different FROM in almost all the joints, and needed more time to accomplish the SHFT tasks. CONCLUSIONS: AROM measurements can be used as indicators for early diagnosis. Patients use specific strategies to accomplish each task, arising from AROM limitations; some tasks with very extreme postures. The tasks where precision or force are required for thumb are the most affected ones.Implications for rehabilitationActive range of motion is an indicator for early hand osteoarthritis diagnosis.Patients' functional ranges are reduced, and thumb opposition and gross dexterity are hindered.Rehabilitation should focus especially on tasks requiring precision and thumb strength.Rehabilitation should favor the improvement of task completion times.

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Sensorized gloves allow the measurement of all hand kinematics that are essential for daily functionality. However, they are scarcely used by clinicians, mainly because of the difficulty of analyzing all joint angles simultaneously. This study aims to render this analysis easier in order to enable the applicability of the early detection of hand osteoarthritis (HOA) and the identification of indicators of dysfunction. Dimensional reduction was used to compare kinematics (16 angles) of HOA patients and healthy subjects while performing the tasks of the Sollerman hand function test (SHFT). Five synergies were identified by using principal component (PC) analyses, patients using less fingers arch, higher palm arching, and a more independent thumb abduction. The healthy PCs, explaining 70% of patients' data variance, were used to transform the set of angles of both samples into five reduced variables (RVs): fingers arch, hand closure, thumb-index pinch, forced thumb opposition, and palmar arching. Significant differences between samples were identified in the ranges of movement of most of the RVs and in the median values of hand closure and thumb opposition. A discriminant function for the detection of HOA, based in RVs, is provided, with a success rate of detection higher than that of the SHFT. The temporal profiles of the RVs in two tasks were also compared, showing their potentiality as dysfunction indicators. Finally, reducing the number of sensors to only one sensor per synergy was explored through a linear regression, resulting in a mean error of 7.0°.

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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.

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

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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.

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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.

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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.

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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.

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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.

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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.

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The increasing development of anthropomorphic artificial hands makes necessary quick metrics that analyze their anthropomorphism. In this study, a human grasp experiment on the most important grasp types was undertaken in order to obtain an Anthropomorphism Index of Mobility (AIM) for artificial hands. The AIM evaluates the topology of the whole hand, joints and degrees of freedom (DoFs), and the possibility to control these DoFs independently. It uses a set of weighting factors, obtained from analysis of human grasping, depending on the relevance of the different groups of DoFs of the hand. The computation of the index is straightforward, making it a useful tool for analyzing new artificial hands in early stages of the design process and for grading human-likeness of existing artificial hands. Thirteen artificial hands, both prosthetic and robotic, were evaluated and compared using the AIM, highlighting the reasons behind their differences. The AIM was also compared with other indexes in the literature with more cumbersome computation, ranking equally different artificial hands. As the index was primarily proposed for prosthetic hands, normally used as nondominant hands in unilateral amputees, the grasp types selected for the human grasp experiment were the most relevant for the human nondominant hand to reinforce bimanual grasping in activities of daily living. However, it was shown that the effect of using the grasping information from the dominant hand is small, indicating that the index is also valid for evaluating the artificial hand as dominant and so being valid for bilateral amputees or robotic hands.

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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.

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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.

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STUDY DESIGN: Cross-sectional research design. INTRODUCTION: Current assessment of hand function is not focused on evaluating the real abilities required for autonomy. PURPOSE OF THE STUDY: To quantify the relevance of grasp types for autonomy to guide hand recovery and its assessment. METHODS: Representative tasks of the International Classification of Functioning, Disability and Health activities in which the hands are directly involved were recorded. The videos were analyzed to identify the grasps used with each hand, and their relevance for autonomy was determined by weighting time with the frequency of appearance of each activity in disability and dependency scales. Relevance is provided globally and distinguished by hand (right-left) and bimanual function. Significant differences in relevance are also checked. RESULTS: The most relevant grasps are pad-to-pad pinch (31.9%), lumbrical (15.4%), cylindrical (12%), and special pinch (7.3%) together with the nonprehensile (18.6%) use of the hand. Lumbrical grasp has higher relevance for the left hand (19.9% vs 12%) while cylindrical grasp for the right hand (15.3% vs 7.7%). Relevancies are also different depending on bimanual function. DISCUSSION: Different relative importance was obtained when considering dependency vs disability scales. Pad-to-pad pinch and nonprehensile grasp are the most relevant grasps for both hands, whereas lumbrical grasp is more relevant for the left hand and cylindrical grasp for the right one. The most significant difference in bimanual function refers to pad-to-pad pinch (more relevant for unimanual actions of the left hand and bimanual actions of the right). CONCLUSIONS: The relative importance of each grasp type for autonomy and the differences observed between hand and bimanual action should be used in medical and physical decision-making. LEVEL OF EVIDENCE: N/A.

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