Posts Tagged grip force

[ARTICLE] Effects of Hand Configuration on the Grasping, Holding, and Placement of an Instrumented Object in Patients With Hemiparesis – Full Text


Objective: Limitations with manual dexterity are an important problem for patients suffering from hemiparesis post stroke. Sensorimotor deficits, compensatory strategies and the use of alternative grasping configurations may influence the efficiency of prehensile motor behavior. The aim of the present study is to examine how different grasp configurations affect patient ability to regulate both grip forces and object orientation when lifting, holding and placing an object.

Methods: Twelve stroke patients with mild to moderate hemiparesis were recruited. Each was required to lift, hold and replace an instrumented object. Four different grasp configurations were tested on both the hemiparetic and less affected arms. Load cells from each of the 6 faces of the instrumented object and an integrated inertial measurement unit were used to extract data regarding the timing of unloading/loading phases, regulation of grip forces, and object orientation throughout the task.

Results: Grip forces were greatest when using a palmar-digital grasp and lowest when using a top grasp. The time delay between peak acceleration and maximum grip force was also greatest for palmar-digital grasp and lowest for the top grasp. Use of the hemiparetic arm was associated with increased duration of the unloading phase and greater difficulty with maintaining the vertical orientation of the object at the transitions to object lifting and object placement. The occurrence of touch and push errors at the onset of grasp varied according to both grasp configuration and use of the hemiparetic arm.

Conclusion: Stroke patients exhibit impairments in the scale and temporal precision of grip force adjustments and reduced ability to maintain object orientation with various grasp configurations using the hemiparetic arm. Nonetheless, the timing and magnitude of grip force adjustments may be facilitated using a top grasp configuration. Conversely, whole hand prehension strategies compound difficulties with grip force scaling and inhibit the synchrony of grasp onset and object release.



Cerebrovascular accidents (stroke) are a frequent cause of disability (1) and the recovery of upper-limb function in particular, is a key determinant of independence in activities of daily living (2). Broadly speaking, the physical impairment experienced by patients is characterized by loss of strength, abnormal movement patterns (pathological synergies), and changes in muscle tone to the side of the body contralateral to the stroke (34). This presentation is commonly referred to as hemiparesis and its severity tends to reflect the extent of the lesion to the corticospinal tract (5). Subtle changes in movement kinematics and hand function on the ipsilesional upper-limb have also been documented and may be the consequence of direct impairment of ipsilateral motor pathways (67), as well as reorganization of the non-lesioned hemisphere to support recovery of motor-function in the hemiparetic limb (810). Above all though, patients living with stroke find that limitations with manual dexterity of the hemiparetic arm have the most significant effect upon their ability to carry out activities involving hand use in daily life (11).

These impairments in patient hand function manifest in multiple different aspects of motor performance. This may include reduced strength (3), loss of individuated finger control (12), and abnormal force control at the level of the fingers (13). Increased muscle tone and spasticity though the flexors of the wrist and hand may further compound these difficulties and inhibit the ability to open the hand in preparation for grasping (14). Atypical reaching and grasping patterns are often seen to emerge both as a consequence of and as a response to the motor dysfunction (1516).

Unfortunately, rehabilitation of upper limb impairments proves to be challenging. Whilst numerous therapeutic modalities (e.g., bilateral training, constraint-induced therapy, electrical stimulation, task-oriented, high intensity programs) have been evaluated in clinical trials, none have demonstrated consistent effects upon hand function (1719). Indeed, previous research papers have described therapy outcomes in upper limb rehabilitation post stroke as “unacceptably poor” (20). Ideally, the design of neurorehabilitation programs should be grounded upon an understanding of basic mechanisms involved in neural plasticity and motor learning (2122). Part of this process implies coming to terms with the factors which characterize the disorganization in voluntary motor output (21). However, the majority of clinical tools currently used for evaluating hand function distinguish motor performance according to ordinal rating scales or task completion time (e.g., Frenchay Arm Test, Jebson-Taylor Hand Function Test) (2324). These kinds of assessments lack sensitivity and may prove insufficient for detecting the presence of mild motor deficits or subtle, yet clinically important changes in hand coordination (2526). Evidence based frameworks for hand rehabilitation have specifically called for the integration of new technology to support patient assessment and treatment planning (27). Despite this, the transposition of technology for upper limb rehabilitation from the research domain into clinical practice has been limited (2829). In the assessment of manual dexterity, the underlying challenge involves analyzing sensorimotor function of the hand with respect to its interaction with objects in the environment (30).

Successfully managing grasping and object handling tasks requires skilled control of prehensile finger forces. In healthy adults, grip forces are regulated to be marginally greater than the minimum required to prevent the object from slipping (31). This safety margin is calibrated according to the shape, surface friction and weight distribution of the object (3233). As the hand moves through space (lifting, transporting, object placement), grip force is continually modulated, proportional to the load forces associated with the mass and acceleration of that object (34). This temporal coupling between grip and load forces is considered a hallmark of anticipatory sensorimotor control (35). Disruption to motor planning, volitional motor control or somatosensory feedback may lead to a breakdown in the timing and magnitude of grip force adjustments.

Numerous studies have examined grip force regulation in neurological pathologies including cerebellar dysfunction (36), peripheral sensory neuropathy (3738), Parkinson’s disease (36373940) as well as congenital and acquired brain lesions (13364145). For patients suffering from hemiparesis post stroke, difficulty with coordinating the grasping and lifting action are frequently associated with temporal discrepancies between grip forces and load forces (46). The cerebral hemisphere implicated in the CVA (1347) and the extent of the resulting sensory deficits (4849) have also been observed to influence anticipatory grip force scaling. This body of work highlights the potential interest of using instrumented objects for the diagnosis and evaluation of the impairments associated with hemiparesis (4546485053).

As it stands, these objective studies of hand function post stroke have focused primarily upon either the lifting or the vertical movement components in object handling. To a certain extent, this limitation has been related to technical restrictions. Other than a handful of studies by Hermsdorfer et al. (849), research in this field has predominantly used manipulanda designed for the study of precision grip, where strain gauge force transducers are attached to a separate base unit [e.g., (232529333537)]. These devices cannot be freely handled by subjects, much less a person with an upper-limb movement disorder. Indeed, patients with hemiparesis often experience specific impairments with precision grip (53) and regularly use alternative grasping strategies such as whole hand grasping (151654). Previous researchers have hypothesized that these alternative grasp strategies may impact grip force scaling (55) and compromise patient ability to manage hand-object-environment relationships during object manipulation (56).

In a recent study with healthy adult subjects, (57) we demonstrated how an instrumented object with multiple load cells and an integrated inertial measurement unit (58) may be used to examine relationships between different grasp configurations, grip force regulation and object orientation. The purpose of the present investigation was to extend this work to the study of patients with hemiparesis post stroke. The first objective was to compare how four alternative grasp configurations commonly used in daily tasks affect grip force regulation in this population. The second objective was to explore the timing and coordination of the whole task sequence (grasping, lifting, holding, placement and object release). The third and final objective was to evaluate the stability of the hand-held object’s orientation across the different phases of the task.[…]


Continue —> Frontiers | Effects of Hand Configuration on the Grasping, Holding, and Placement of an Instrumented Object in Patients With Hemiparesis | Neurology

Figure 1. Illustration of the iBox device and the experimental setup. (A) The iBox instrumented object. (B)Setup for the experimental procedure. Initial positions of the iBox and hand start area are indicated by the dotted lines. The gray shaded rectangle indicates the deposit area for the top grasp task.


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[Abstract] Fine motor skills predict performance in the Jebsen Taylor Hand Function Test after stroke


Three characteristic factors differentiate fine motor control in patients and controls.

The three factors are grip force scaling, motor coordination and speed of movement.

These factors are predictors of 69% of variance for the Jebsen Taylor Hand Function Test.



To determine factors characterizing the differences in fine motor performance between stroke patients and controls. To confirm the relevance of the factors by analyzing their predictive power with regard to the Jebsen Taylor Hand Function Test (JTHFT), a common clinical test of fine motor control.


Twenty-two people with slight paresis in an early chronic phase following stroke and twenty-two healthy controls were examined.. Performance on the JTHFT, Nine-Hole Peg Test and 2-point discrimination was evaluated. To analyze object manipulation skills, grip forces and temporal measures were examined during 1) lifting actions with variations of weight and surface 2) cyclic movements 3) predictive/reactive catching tasks. Three other aspects of force control included 4) visuomotor tracking 5) fast force changes and 6) grip strength.


Based on 9 parameters which significantly distinguished fine motor performance in the two groups, we identified three principal components (factors): grip force scaling, motor coordination and speed of movement. The three factors are shown to predict JTHFT scores via linear regression (R2=.687, p<.001).


We revealed a factor structure behind fine motor impairments following stroke and showed that it explains JTHFT results to a large extend.

Significance: This result can serve as a basis for improving diagnostics and enabling more targeted therapy.

Source: Fine motor skills predict performance in the Jebsen Taylor Hand Function Test after stroke – Clinical Neurophysiology

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[ARTICLE] Elasticity improves handgrip performance and user experience during visuomotor control – Full Text


Passive rehabilitation devices, providing motivation and feedback, potentially offer an automated and low-cost therapy method, and can be used as simple human–machine interfaces. Here, we ask whether there is any advantage for a hand-training device to be elastic, as opposed to rigid, in terms of performance and preference. To address this question, we have developed a highly sensitive and portable digital handgrip, promoting independent and repetitive rehabilitation of grasp function based around a novel elastic force and position sensing structure. A usability study was performed on 66 healthy subjects to assess the effect of elastic versus rigid handgrip control during various visuomotor tracking tasks. The results indicate that, for tasks relying either on feedforward or on feedback control, novice users perform significantly better with the elastic handgrip, compared with the rigid equivalent (11% relative improvement, 9–14% mean range; p < 0.01). Furthermore, there was a threefold increase in the number of subjects who preferred elastic compared with rigid handgrip interaction. Our results suggest that device compliance is an important design consideration for grip training devices.

1. Introduction

Interaction with the environment involves the exchange of forces while manipulation requires skillful force control and is a sensitive measure of motor condition [1,2]. For hand and finger training, this motivates isometric training based on force control without the need to support overt movements, for example using a force-sensing handle such as Tyromotion’s Pablo device ( Grip force control can also be used for human–machine interfaces and teleoperation applications, e.g. control of surgical robotics [3], and as a tool to study ergonomics and handgrip design [4]. Furthermore, grip strength is a pervasive clinical outcome supported by dynamometry-based isometric measurements (using the Jamar handgrip) [5,6]. Isometric training has been shown to enable the learning of force fields applied on virtual movements associated with the exerted isometric force and that this learning transferred to real (isotonic) movements [7,8]. However, such systems for isometric control or strength do not support the kinematic aspect of training which is an intrinsic part of manipulation and activities of daily living (ADLs) [9].

Grasping of objects involves grip aperture modulation and shaping of the hand, and often involves interaction with soft objects or manipulation [10]. This suggests that grip training should involve learning to shape one’s hand across a range of joint angles similar to natural grasping tasks. Moreover, allowing the stretching of muscles can reduce collagen build-up in the joints and prevent further biomechanical issues such as contractures [11]. The MusicGlove system promotes finger individuation through finger tapping [12], while Neofect’s Smartglove can measure overt movements of the digits using bend sensors [13], with both interfacing to virtual environments for training. A recent study in 12 chronic stroke patients with moderate hemiparesis comparing two weeks of movement-based training using the MusicGlove system to both isometric grip training and conventional therapy showed superior functional outcomes [12].

While skilful force control is critical to efficient manipulation, it may be helped by using additional joint position sensing. Indeed, proprioception can be divided into both static and dynamic components, and relies on various types of mechanoreceptors and skin afferents, including muscle spindles, Golgi tendon organs and skin stretch senses [14]. The different afferents respond in a variety of ways to different stimuli, for example muscle spindle receptors signal both the length and rate of change of muscles hence contributing to both the static and dynamic components [15]. The static component senses the stationary limb while the dynamic component involves the estimation of limb position and velocity during either volitionally generated active movements or passively induced motions. In fact, active movement itself as opposed to endpoint postures is thought to provide the greatest acuity for localization [16]. Therefore, elastic as opposed to isometric interaction will provide additional coordinated kinaesthetic information facilitating control and learning by playing a vital role during the planning and execution of voluntary movements [17,18]. A recent study comparing virtual learning based on isometric force information demonstrated the beneficial effect of additional elastic deformation on control and learning [19]. Damage to the neural circuits mediating proprioceptive function, e.g. due to an infarction in thalamic or parietal brain areas, can impair a patient’s ability during goal-directed movement, prehension, accurate aiming, reaching and tracking movements [20,21]. This can occur in up to half of stroke patients and therefore technology that can stimulate proprioceptive feedback during active training are essential.

The vast majority of ADLs require a functioning hand. This explains why individuals with complete loss of movement capabilities select recovering arm and hand function as their number one priority for improving their quality of life [22]. Unfortunately, 77% of stroke survivors are affected by arm–hand weakness and poor control [23], while impaired hand function is also common in other neurological diseases such as cerebral palsy and multiple sclerosis. Hand function is also commonly impaired as a consequence of rheumatological and orthopaedic conditions such as symptomatic hand arthritis which is estimated to affect over 300 million worldwide [24]. The only intervention shown to improve arm function is repetitive, task-specific exercise, but this is limited by the cost and availability of physiotherapists [25,26]. To address this issue, we are developing affordable devices to promote independent training of hand function from the ward to the home. These simple devices provide accessible functional rehabilitation by working on improving hand function through the use of engaging virtual therapy games controlled via sensors. With such devices, it is possible to train hand functions through individuated finger movements or whole hand grip force control [27].

So how can one train using both force control and hand kinaesthesia with a passive device using no actuators? To manipulate objects such as a soft ball, one has to control the force which is coupled to motion through the object’s elasticity. Similarly, we have created an elastic handle with a spring mechanism in series with a force transducer yielding force-sensing coupled with movement deformation. In a recent study, we showed that this sensitive mechanism enables even severely impaired patients to interact with a mobile tablet PC who would otherwise be unable to use such technology by conventional means, i.e. swiping, tapping and tilting [28].

This device has enabled us to study the effect of elasticity and resulting proprioceptive information on grip control. We have carried out a usability study with 66 healthy individuals, contrasting the elastic behaviour that this handgrip affords to isometric-equivalent interaction during visuomotor tracking tasks. We used two types of tasks, namely, one relying predominantly on feedforward information while the other relies on continuous sensory feedback. The digital handgrip and mobile-based virtual therapy platform used for this experiment are described in the next section, followed by the description of the visuomotor tasks and experimental protocols. The results presented in the following section reveal advantages of the elastic interaction over pure isometric information for grip control, alongside the influence of different factors on performance and preferences during the different interaction modalities.

Continue —> Elasticity improves handgrip performance and user experience during visuomotor control | Open Science

Figure 1.

Figure 1. Overview of the interactive handgrip and mobile virtual training package including the motor behaviours the handgrip affords, alongside a photo of a patient using the digital handgrip and screenshots of some of the training games that have been developed.

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