Posts Tagged robots

[Conference Paper] HandMATE: Wearable Robotic Hand Exoskeleton and Integrated Android App for At Home Stroke Rehabilitation – Full Text

Abstract

We have developed HandMATE (Hand Movement Assisting Therapy Exoskeleton); a wearable motorized hand exoskeleton for home-based movement therapy following stroke. Each finger and the thumb is powered by a linear actuator which provides flexion and extension assistance. Force sensitive resistors integrated into the design measure grasp and extension initiation force. An assistive therapy mode is based on an admittance control strategy. We evaluated our control system via subject and bench testing. Errors during a grip force tracking task while using the HandMATE were minimal (<1%) and comparable to unassisted healthy hand performance. We also outline a dedicated app we have developed for optimal use of HandMATE at home. The exoskeleton communicates wirelessly with an Android tablet which features guided exercises, therapeutic games and performance feedback. We surveyed 5 chronic stroke patients who used the HandMATE device to further evaluate our system, receiving positive feedback on the exoskeleton and integrated app.

SECTION I.

Introduction

Stroke is the leading cause of severe long-term disability in the US [1]. The probability of regaining functional use of the impaired upper extremity is low [2]. At 6 months post stroke, 62% of survivors failed to achieve some dexterity [3]. Such impairments can inhibit the individual’s ability to perform activities of daily living (ADL). Subsequently, upper limb rehabilitation recovery to improve ADL is one of the main self-reported goals of stroke survivors [4].

Outpatient rehabilitation is recommended for survivors that have been discharged from inpatient rehabilitative services [5]. However, outpatient rehabilitation in general is largely underutilized, with only 35.5% of stroke survivors using services [6]. Factors inhibiting outpatient therapy include cost, lack of resources and transportation. Wearable robotics that enable home-based therapy have the potential to overcome these barriers. They provide assistive movement forces which enable task-specific training in real-life situations that patients are often unable to practice without a clinician. See [7] for wearable hand robots for rehabilitation review.

At home therapy is not without its limitations. The inability to motivate oneself and fatigue are the most common reported factors resulting in failure to adhere to home based exercise programs for stroke recovery [8]. While wearable robotics can reduce fatigue during exercise, it does not directly address lack of motivation. Research has shown incorporating games into home therapy can encourage compliance [9]. Zondervan et al. showed that use of an instrumented sensor glove, named the MusicGlove, improved self-reported use and quality of movement, greater than convention at home exercises [9]. Other studies showed increased motivation to complete the therapeutic exercises and optimized movement when the user is given feedback of their performance via the Microsoft Kinect [10]. Wearable robotic systems that offer feedback and gaming capability may optimize at home stroke therapy.

Such a system was presented by Nijenhuis et al. in which stroke survivors showed motor improvements after completing a 6 week self-administered training program comprised of a dynamic hand orthosis and gaming environment [11]. However, the hand device was passive, assisting only with extension, which limits the range of stroke survivors who could utilize such a system. Research groups have proposed combining their powered take-home wearable hand devices with custom integrated gaming systems [12], or guided exercises [13]; however, they have yet to conduct clinical trials. Notably, Ghassemi et al., have developed an integrated multi-user VR system to use with their X-Glove actuated orthosis, which will allow for client-therapist sessions without the patient having to travel [12].

Tablets are relatively inexpensive, portable, and straight forward to use, with 47% of internet users globally already owning one [14]. Furthermore, a recent study demonstrated the success of a tablet based at home exercise program in improving the recovery of stroke survivors [15]. Notably, the study evaluated the accessibility of tablets, concluding every participant used the tablet successfully. Therefore a wearable powered hand robot with a dedicated tablet app which will provide functional games, task-specific guided exercises and feedback of movement, could optimize at home stroke therapy.

SECTION II.

Aims

The goal of this project was to create a wearable robotic exoskeleton that enables repetitive practice of task-specific and goal orientated movements, which translates into improvements in ADL. Furthermore, for maximum use and successful integration into home-based rehabilitation, we aimed to create an Android application compatible with the robotic exoskeleton.

To meet these goals, the following design objectives were established: 1) Assistance with finger flex/extension. 2) Assistance with thumb carpometacarpal (CMC) add/abduction and thumb metacarpophalangeal (MCP) flex/extension. 3) Independent assistive control of each finger and thumb. 4) Portable for at home use, meaning the device has to be lightweight and wireless. 5) Relatively affordable. 6) Integrated with android tablet app. Specific design goals for the app included: 1) Easy to use. 2) Allow the user to control the exoskeletons assistance mode through the app. 3) Records the user’s data and prompts the user via notifications to complete the allocated daily or weekly recommended activity time.

In this paper we will evaluate if the proposed device and app goals have been achieved via bench and subject testing.

SECTION III.

Design

The HandMATE device (Fig. 1) builds upon the Hand Spring Operated Movement Enhancer (HandSOME) devices [16][17][18]. The HandSOME devices are non-motorized wearable exoskeletons that assists stroke patients with finger and thumb extension movements. The HandSOME I device assists with gross whole hand opening movements, while the HandSOME II assists isolated extension movement of 15 finger and thumb degrees of freedom (DOF), allowing performance of various grip patterns used in ADL. While both devices have been shown to significantly increase range of motion (ROM) and functional ability in chronic stroke subjects [16],[18], the HandSOME devices only assist with extension movements and require enough flexion activity to overcome the assistance of the extension springs. As many stroke patients also suffer finger and thumb flexion weakness, we decided to build upon the work of the high DOF HandSOME II and additionally utilize power actuation so we can assist with both flexion and extension movements.

Figure 1: - 
HandMATE device. Individually actuated fingers and thumb shown. Electronics box is affixed to back of splint.
Figure 1:
HandMATE device. Individually actuated fingers and thumb shown. Electronics box is affixed to back of splint.

Continue —-> https://ieeexplore.ieee.org/abstract/document/9175332

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[ARTICLE] Systematic review with network meta-analysis of randomized controlled trials of robotic-assisted arm training for improving activities of daily living and upper limb function after stroke – Full Text

Abstract

Background

The aim of the present study was to to assess the relative effectiveness of the various types of electromechanical-assisted arm devices and approaches after stroke.

Method

This is a systematic review of randomized controlled trials with network meta-analysis. Our primary endpoints were activities of daily living (measured e.g. with Barthel-Index) and hand-arm function (measured e.g. with the Fugl-Meyer Scale for the upper limb), our secondary endpoints were hand-arm strength (measured e.g. with the Motricity Index) and safety. We used conventional arm training as our reference category and compared it with different intervention categories of electromechanical-assisted arm training depending on the therapy approach. We did indirect comparisons between the type of robotic device. We considered the heterogeneity of the studies by means of confidence and prediction intervals.

Results

Fifty five randomized controlled trials, including 2654 patients with stroke, met our inclusion criteria.

For the primary endpoints activities of daily living and hand-arm function and the secondary endpoint hand-arm strength, none of the interventions achieved statistically significant improvements, taking into account the heterogeneity of the studies.

Safety did not differ with regard to the individual interventions of arm rehabilitation after stroke.

Conclusion

The outcomes of robotic-assisted arm training were comparable with conventional therapy.

Indirect comparisons suggest that no one type of robotic device is any better or worse than any other device, providing no clear evidence to support the selection of specific types of robotic device to promote hand-arm recovery.

Introduction

Stroke is one of the most common diseases worldwide and leads to permanent disability, reduced quality of life and thus to a high burden of disease [1]. A majority of stroke patients have limited hand and arm function and are therefore restricted in their daily activities [2]. The recovery of hand-arm function is therefore an important goal for rehabilitation after stroke [1]. In recent years, interventions such as electromechanical-assisted arm training have been introduced to improve hand and arm functions [34]. It has been argued that use of electromechanical-assisted arm training can support the provision of evidence-based rehabilitation, by facilitating therapy that is intensive, frequent and repetitive [3]. However, while systematic reviews show some beneficial effects of electromechanical-assisted arm training on upper limb motor function, these effects are not clinically relevant [34]. Furthermore, there is also some evidence of a detrimental effect, with one systematic review concluding that muscle tone of the upper limb might be negatively influenced by robotic-assisted arm-training [4].

The devices used in electromechanically-assisted arm therapy target the motor function of either the shoulder/elbow, elbow/wrist, wrist/hand, hand/finger or the entire upper extremity [35]. There are two broad types of electromechanical devices which have been used to enable or assist arm and/or hand movement in a patient with a paretic limb following stroke:

  1. (a)An external robotic arm, known as an exoskeleton, which is designed to control one or more joints of the paretic arm. The exoskeleton uses torque actuators in order to apply rotational forces to move, or assist the movement, at a joint. For example, a robotic arm could support the weight of a patient’s arm in the horizontal plane, and assist combined movement at the shoulder and elbow [5].
  2. (b)A robotic device, known as an end-effector, which assists movement of only the distal part of the paretic arm [34]. These devices generally only have contact with the patient’s hand/fingers; and move – or assist the movement – of the distal part of the arm, which may result in movement at more proximal parts of the arm. End effectors may act to move just the paretic limb, or may act to support bilateral arm movement. For example, an end effector may comprise two handles, which are held by the patient’s hands. Movement of the handles facilitates bilateral pronation/supination of the forearm and flexion/extension of the wrist. Movement of the patient’s affected arm may be passive, either driven entirely by the robot or by active movement of the unaffected arm, or may be active-assisted, supported by the robot or unaffected arm [5].

In addition to generating either passive or assisted movement of a paretic arm, electromechanically assisted arm therapy can give patients feedback about the joint position and the arm power used.

Electromechanical-assisted arm therapy may, alternatively, be classified based on whether the robot acts: more proximally or distally, with a one-sided / unilateral or double-sided / bilateral exercise approach, or to give support to specific joint sections. End effector-based therapy robots generally initiate movement via contact with the patient’s hand, generating movement of more proximal joints from this distal contact; while exoskeletal devices can directly guide and control movement of both proximal and distal joints via series of drive elements.

Furthermore, the torque actuators which can be used within robotic devices may have different mechanisms of action, and there is ongoing debate regarding these different approaches to control of force. For example, it remains unclear whether a compliant actuator (e.g. series elastic actuators, an elastic element attached) is any more beneficial than an assist-as-needed control mechanism (e.g. which encourages patients’ active participation), or an impedance control mechanism (e.g. an end effector that takes into account the kinematics and dynamics of the object being manipulated).

With a rapid growth in new technologies and devices over recent decades, there are now a large number of different electromechanical-assisted arm training devices designed to move, or assist movement of, the arm. The types of therapy provided by different devices differ significantly both in terms of the technologies employed and the therapy provided. There is a growing body of evidence, synthesized within systematic reviews, that demonstrates that electromechanical-assisted arm training may be beneficial for recovery of arm function after stroke, with quality of the evidence judged to be ‘high’ (using the GRADE approach) [34]. However, although the evidence on robotic-assisted arm training after stroke seems robust, there remains a lack of information about the relative effects of different types of devices. The existing systematic reviews are arguably limited by their narrow focus, for example on the effectiveness of robotic-assisted arm training or electromechanical-assisted arm rehabilitation compared to control interventions [346]. Thus, while in practice it is crucial to know which type of robotic device performs most effectively in a given situation, the current evidence base lacks direct comparisons of two or more different types of device. Furthermore, it remains unclear which of the different devices or approaches may be most effective for certain subgroups of patients with stroke, meaning that a treating clinician will encounter difficulties in deciding which specific form of treatment to select and/or apply for a specific patient after stroke. Thus, while systematic reviews have explored the effectiveness of electromechanical-assisted arm rehabilitation [34], these have not directly compared the effects of the different types of devices or therapy provided by devices, in order to determine the optimal type of electromechanical-assisted arm training for individual patients.

An approach to solving this problem is offered by network meta-analyses. These enable quantitative synopsis of an “evidence network” by combining direct and indirect effects of three or more interventions, compared to the same comparative intervention (often a control or a no-treatment intervention), within a randomized controlled trial [7]. This is also called a multiple treatment comparison [8].

In this way, network meta-analyses allow the quantitative synthesis of evidence of effectiveness of interventions directly compared within the same randomised controlled trial (direct comparisons) and interventions from different randomised controlled trials which have a common comparator (indirect comparisons) [7]. Network meta-analyses could therefore provide an efficient method for determining the relative effects of different electromechanical-assisted arm training devices and therapy approaches, without the need for new randomised controlled trials.

The aim of the present study was therefore to provide a systematic overview of current randomised controlled trials of electromechanical-assisted arm training, and to use network meta-analysis to assess the relative effectiveness of the various types of electromechanical-assisted arm devices and approaches. We aimed to evaluate the relative effect of different types of electromechanical-assisted arm training on activities of daily living, hand/arm function and hand/arm strength in patients with stroke, and to explore the safety of these devices.[…]

Continue —-> https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-020-00715-0

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[Abstract] A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation

Abstract

Exoskeleton robots have been demonstrated to effectively assist the rehabilitation of patients with upper or lower limb disabilities. To make exoskeletons more accessible to patients, they need to be lightweight and compact without major performance tradeoffs. Existing upper-limb exoskeletons focus on assistance with coarse-motion of the upper arm while forearm fine-motion rehabilitation is often ignored. This paper presents an elbow-wrist exoskeleton with five degrees-of-freedom (DoFs). Using geared bearings, slider crank mechanisms, and a spherical mechanism for the wrist and elbow modules, this exoskeleton can provide 5-DoF rotary motion forearm assistance. The optimized exoskeleton dimensions allow sufficient rotation output while the motors are placed parallel to the forearm and elbow joint. Thus compactness and less inertia loading can be achieved. Linear and rotary series elastic actuators (SEAs) with high torque-to-weight ratios are proposed to accurately measure and control interaction force and impedance between exoskeleton and forearm. The resulting 3-kg exoskeleton can be used alone or easily in combination with other exoskeleton robots to provide various robot-aided upper limb rehabilitation.

via A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation – IEEE Journals & Magazine

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[Abstract] Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings

Abstract

Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously moves the wrist and metacarpophalangeal joints.
The device was designed for the ease of manufacturing and maintenance, with specific considerations for countries with limited resources. Active participation of the user is ensured by the implementation of electromyographic control and visual feedback of performance. Muscle activity requirements, movement parameters, range of motion, and speed of the device can all be customized to meet the needs of the user.
Twelve stroke survivors, ranging from the subacute to chronic phases of recovery (mean 10.6 months post-stroke) participated in a pilot study with the device. Participants completed 20 sessions, each lasting 45 minutes. Overall, subjects exhibited statistically significant changes (p < 0.05) in clinical outcome measures following the treatment, with the Fugl-Meyer Stroke Assessment score for the upper extremity increasing from 36 to 50 and the Barthel Index increasing from 74 to 89. Active range of wrist motion increased by 190 while spasticity decreased from 1.75 to 1.29 on the Modified Ashworth Scale.
Thus, this device shows promise for improving rehabilitation outcomes, especially for patients in countries with limited resources.

via Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings – IEEE Journals & Magazine

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[Abstract] An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential

Abstract

Movement impairments resulting from neurologic injuries, such as stroke, can be treated with robotic exoskeletons that assist with movement retraining. Exoskeleton designs benefit from low impedance and accurate torque control. We designed a two-degrees-of-freedom tethered exoskeleton that can provide independent torque control on elbow flexion/extension and forearm supination/pronation. Two identical series elastic actuators (SEAs) are used to actuate the exoskeleton. The two SEAs are coupled through a novel cable-driven differential. The exoskeleton is compact and lightweight, with a mass of 0.9 kg. Applied rms torque errors were less than 0.19 Nm. Benchtop tests demonstrated a torque rise time of approximately 0.1 s, a torque control bandwidth of 3.7 Hz, and an impedance of less than 0.03 Nm/° at 1 Hz. The controller can simulate a stable maximum wall stiffness of 0.45 Nm/°. The overall performance is adequate for robotic therapy applications and the novelty of the design is discussed.

via An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential – IEEE Journals & Magazine

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[Abstract] Development of a Compatible Exoskeleton (Co-Exos II) for Upper-Limb Rehabilitation

Abstract

A key approach for reducing motor impairment and regaining independence after spinal cord injuries or strokes is frequent and repetitive functional training. A compatible exoskeleton (Co-Exos II) is proposed for the upper-limb rehabilitation. A compatible configuration was selected according to optimum configuration principles. Four passive translational joints were introduced into the connecting interfaces to adapt the glenohumeral joint (GH) movements and improve the compatibility of the exoskeleton. This configuration of the passive joints could reduce the influence of gravity of the exoskeleton device and the upper extremities. A Co-Exos II prototype was developed and still owned a compact volume. A new approach was presented to compensate the vertical GH movements. The shoulder closed-loop was simplified as a guide-bar mechanism. The compatible models of this loop were established based on the kinematic model of GH. The compatible experiments were completed to verify the kinematic models and analyze the human-machine compatibility of Co-Exos II. The theoretical displacements of the translational joints were calculated by the kinematic model of the shoulder loop. The passive joints exhibited good compensations for the GH movements through comparing the theoretical and measured results, especially vertical GH movements. Co-Exos II showed good human-machine compatibility for upper limbs.

via Development of a Compatible Exoskeleton (Co-Exos II) for Upper-Limb Rehabilitation* – IEEE Conference Publication

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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation

Abstract

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available: https://www.frontiersin.org/article/10.3389/fninf.2018.00029

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

via Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation – IEEE Conference Publication

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[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation

Abstract

In this study, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient’s finger is demonstrated in human experiments. Specifically, the net force required to move a subject’s finger joints can be accounted for by the Lagrange model.

via https://ieeexplore.ieee.org/abstract/document/8701573

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[Abstract] The wearable hand robot: supporting impaired hand function in activities of daily living and rehabilitation

Abstract

Our hands are very important in our daily life. They are used for non-verbal communication and sensory feedback, but are also important to perform both fine (e.g. picking up paperclips) and gross (e.g. lifting heavy boxes) motor tasks. Decline of hand function in older adults as a result of age-related loss of muscle mass (i.e. sarcopenia) and/or age-related diseases such as stroke, rheumatoid arthritis or osteoarthritis, is a common problem worldwide. The decline in hand function, in particular grip strength, often results in increased difficulties in performing activities of daily living (ADL), such as carrying heavy objects, doing housework, (un)dressing, preparing food and eating.
New developments, based on the concept of wearable soft-robotic devices, make it possible to support impaired hand function during the performance of daily activities and intensive task-specific training. The ironHand and HandinMind systems are examples of such novel wearable soft-robotic systems that have been developed in the ironHand and HandinMind projects. Both systems are developed to provide grip support during a wide range of daily activities. The ironHand system consists of a 3-finger wearable soft-robotic glove, tailored to older adults with a variety of physical age-related hand function limitations. The HandinMind system consists of a 5-finger wearable soft-robotic glove, dedicated towards application in stroke. In both cases, the wearable soft-robotic system could be connected to a computer with custom software to train specific aspects of hand function in a motivating game-like environment with multiple levels of difficulty. By adding the game environment, an assistive device is transformed into a dedicated training device.
The aim of the current thesis is to define user requirements, to investigate feasibility and to evaluate the direct and clinical effects of a wearable soft-robotic system that is developed to support impaired hand function of older adults and stroke patients in a wide range of daily activities and in exercise training at home.

via The wearable hand robot: supporting impaired hand function in activities of daily living and rehabilitation — University of Twente Research Information

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