Posts Tagged Force

[Abstract + References] Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons in Slope Scenarios

Abstract

Lower-limb exoskeleton has gained considerable interests in walking assistance applications for paraplegic patients. In walking assistance of paraplegic patients, the exoskeleton should have the ability to help patients to walk over different terrains in the daily life, such as slope terrains. One critical issue is how to plan the stepping locations on slopes with different gradients, and generate stable and human-like gaits for patients. This paper proposed an adaptive gait planning approach which can generate gait trajectories adapt to slopes with different gradients for lower-limb walking assistance exoskeletons. We modeled the human-exoskeleton system as a 2D Linear Inverted Pendulum Model (2D-LIPM) with an external force in the two-dimensional sagittal plane, and proposed a Dynamic Gait Generator (DGG) based on an extension of the conventional Capture Point (CP) theory and Dynamic Movement Primitives (DMPs). The proposed approach can dynamically generate reference foot locations for each step on slopes, and human-like adaptive gait trajectories can be reproduced after the learning from demonstrated trajectories that sampled from level ground walking of normal healthy human. We demonstrated the efficiency of the proposed approach on both the Gazebo simulation platform and an exoskeleton named AIDER. Experimental results indicate that the proposed approach is able to provide the ability for exoskeletons to generate appropriate gaits adapt to slopes with different gradients.

References

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Source: https://ieeexplore.ieee.org/document/8793863/references#references

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[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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[Abstract] RehabFork: An Interactive Game-assisted Upper Limb Stroke Rehabilitation System – IEEE Conference Publication

Abstract

In this paper, we present the design and development of a game-assisted stroke rehabilitation system RehabFork that allows a user to train their upper-limb to perform certain functions related to the task of eating.

The task of eating is divided into several components: (i) grasping the eating utensils such as a fork and knife; (ii) lifting the eating utensils; (iii) using the eating utensils to cut a piece of food; (iv) transferring the food to the mouth; and (v) chewing the food. The RehabFork supports the user through sub-tasks (i)–(iii).

The hardware components of RehabFork consist of an instrumented fork and knife, and a 3D printed pressure pad, that measure and communicate information on user performance to a gaming environment to render an integrated rehabilitation system.

The gaming environment consists of an interactive game that utilizes sensory data as well as user information about the severity of their disability and current level of progress to adjust the difficulty levels of the game to maintain user motivation. Information pertaining to the user, including performance data, is stored and can be shared with care providers for ongoing oversight.

Source: https://ieeexplore.ieee.org/abstract/document/9176168

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[ARTICLE] Mechanical Design of Exoskeleton for Hand Therapeutic Rehabilitation – Full Text PDF

ABSTRACT 

In this study an exoskeleton is designed for hand in therapeutic rehabilitation. The mechanical design is manufactured in consideration of anthropometrical measurements of the hand studied from literature. Kinematic model of the hand exoskeleton was obtained by results of position, velocity and torque-moment analysis.
The exoskeleton has a single degree of freedom (DOF) for the PIP and MCP joints. Basic four-bar linkage mechanisms are used in the exoskeleton. With this design, while movements (flexion and extension) occurs in both joints at the same time, angular displacement come out as in healthy hands. Linkage lengths aroptimized to achieve the targeted angular dynamics. The manipulation of the exoskeleton is actuated by a linear
servo motor.

Continue —>  Mechanical Design of Exoskeleton for Hand Therapeutic Rehabilitation

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Fig 2 Hand Rehabilitation System

 

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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] Design and development of a portable exoskeleton for hand rehabilitation

Abstract:

Improvement in hand function to promote functional recovery is one of the major goals of stroke rehabilitation. This paper introduces a newly developed exoskeleton for hand rehabilitation with a user-centered design concept, which integrates the requirements of practical use, mechanical structure and control system. The paper also evaluated the function with two prototypes in a local hospital. Results of functional evaluation showed that significant improvements were found in ARAT (P=0.014), WMFT (P=0.020) and FMA_WH (P=0.021). Increase in the mean values of FMA_SE was observed but without significant difference (P=0.071). The improvement in ARAT score reflects the motor recovery in hand and finger functions. The increased FMA scores suggest there is motor improvement in the whole upper limb, and especially in the hand after the training. The product met patients’ requirements and has practical significance. It is portable, cost effective, easy to use and supports multiple control modes to adapt to different rehabilitation phases.

 

via Design and development of a portable exoskeleton for hand rehabilitation – IEEE Journals & Magazine

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[Abstract] Desktop upper limb rehabilitation robot using omnidirectional drive gear – IEEE Conference Publication

Abstract

Research and development efforts into small upper limb rehabilitation robots for home-based rehabilitation have been made in order to reduce the patient burden associated with making visits to the hospital. However, currently, there are only a few small upper limb rehabilitation robots capable of providing training that is tailored to account for the differences in individual patients. This is because many robots use omni wheels for their movement mechanism, thus causing problems when measuring patient motor function because it is not possible to accurately estimate the position. To solve this problem, in this study, we propose a new small upper limb rehabilitation robot that switches the driving unit from an omni wheel to an omnidirectional drive gear mechanism, as a mechanism that does not cause slips. Although an omnidirectional drive gear poses problems in terms of machining difficulty and weight, these problems can be solved by using a 3D printer. We show that position errors in small upper limb rehabilitation robots are greatly reduced by introducing a gear mechanism.

via Desktop upper limb rehabilitation robot using omnidirectional drive gear – IEEE Conference Publication

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[Abstract + References] A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study

Abstract

Previously, we reported a novel bilateral upper-limb rehabilitation system, an adaptive admittance controller and a related bilateral recovery strategy. In this study, we want to get a stronger evidence to verify the robustness of the proposed system, controller and recovery strategy as well as to further investigate the possibility of bilateral trainings for clinical applications. To this end, ten healthy subjects took part in a 60-minute experiment. Trajectories of robots and interaction force were recorded under the proposed bilateral recovery strategy which contained four exercise modes. For mode-l and mode-2, results showed that the trajectories of master and slave robots can catch the reference trajectory very well, and be changed with active interaction force applied by participants. For mode-3 and mode-4, participants finished tasks very well by drawing the ‘square-shaped’ trajectories through their own force. In conclusion, the experimental results were good enough to provide a strong and positive evidence for the proposed system and controller. Moreover, according to the feedbacks from participants, the bilateral recovery strategy can be treated as a new and interesting training as compared to the traditional unilateral training, and could be tested in clinical applications further.

I. Introduction

Compared to the traditional manual therapy, the robot involved therapy can alleviate labor-intensive aspects of conventional rehabilitation trainings, and provide precise passive/active repetitive trainings in a sufficiently long timeframe [1], [2]. In terms of upper-limb rehabilitation trainings, some robotic systems have been developed for bilateral exercises, and figured out a problem that performing most activities of daily living tasks with one-hand is awkward, difficult and time-consuming [2].

 

1. M. Cortese, M. Cempini, P. R. de Almeida Ribeiro, S. R. Soekadar, M. C. Carrozza, N. Vitiello, “A mechatronic system for robot-mediated hand telerehabilitation”, IEEE/ASME Transactions on Mechatronics, vol. 20, pp. 1753-1764, September 2015.

2. P. S. Lum, C. G. Burgar, P. C. Shor, “Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke”, Archives of physical medicine and rehabilitation, vol. 83, pp. 952-959, July 2002.

3. B. Sheng, Y. Zhang, W. Meng, C. Deng, S. Xie, “Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects”, Medical engineering & physics, vol. 38, pp. 587-606, July 2016.

4. P. R. Culmer, A. E. Jackson, S. Makower, R. Richardson, J. A. Cozens, M. C. Levesley et al., “A control strategy for upper limb robotic rehabilitation with a dual robot system”, IEEE/ASME Transactions on Mechatronics, vol. 15, pp. 575-585, September 2010.

5. Z. Song, S. Guo, M. Pang, S. Zhang, N. Xiao, B. Gao et al., “Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint”, J. Med. Biol. Eng, vol. 34, pp. 188-196, 2014.

6. R. C. Loureiro, W. S. Harwin, K. Nagai, M. Johnson, “Advances in upper limb stroke rehabilitation: a technology push”, Medical & biological engineering & computing, vol. 49, pp. 1103, July 2011.

7. S. Hesse, C. Werner, M. Pohl, S. Rueckriem, J. Mehrholz, M. Lingnau, “Computerized arm training improves the motor control of the severely affected arm after stroke”, Stroke, vol. 36, pp. 1960-1966, August 2005.

8. C.-L. Yang, K.-C. Lin, H.-C. Chen, C.-Y. Wu, C.-L. Chen, “Pilot comparative study of unilateral and bilateral robot-assisted training on upper-extremity performance in patients with stroke”, American Journal of Occupational Therapy, vol. 66, pp. 198-206, March 2012.

9. E. Taub, G. Uswatte, R. Pidikiti, “Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation-a clinical review”, Journal of rehabilitation research and development, vol. 36, pp. 237, July 1999.

10. S. B. Brotzman, R. C. Manske, “Clinical Orthopaedic Rehabilitation E-Book: An Evidence-Based Approach-Expert Consult” in Elsevier Health Sciences, 2011.

11. K. C. Lin, Y. F. Chang, C. Y. Wu, Y. A. Chen, “Effects of constraint-induced therapy versus bilateral arm training on motor performance daily functions and quality of life in stroke survivors”, Neurorehabilitation and Neural Repair, vol. 23, pp. 441-448, December 2009.

12. J. Chen, N. Y. Yu, D. G. Huang, B. T. Ann, G. C. Chang, “Applying fuzzy logic to control cycling movement induced by functional electrical stimulation”, IEEE transactions on rehabilitation engineering, vol. 5, pp. 158-169, Jun 1997.

13. D. A. Winter, “Biomechanics and motor control of human movement” in John Wiley & Sons, 2009.

 

via A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study – IEEE Conference Publication

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[Abstract] A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training

Abstract:

Objective: Loss of arm function is common in individuals with neurological damage, such as stroke or cerebral palsy. Robotic devices that address muscle strength deficits in a task-specific manner can assist in the recovery of arm function; however, current devices are typically large, bulky, and expensive to be routinely used in the clinic or at home. This study sought to address this issue by developing a portable planar passive rehabilitation robot, PaRRo. Methods: We designed PaRRo with a mechanical layout that incorporated kinematic redundancies to generate forces that directly oppose the user’s movement. Cost-efficient eddy current brakes were used to provide scalable resistances. The lengths of the robot’s linkages were optimized to have a reasonably large workspace for human planar reaching. We then performed theoretical analysis of the robot’s resistive force generating capacity and steerable workspace using MATLAB simulations. We also validated the device by having a subject move the end-effector along different paths at a set velocity using a metronome while simultaneously collecting surface electromyography (EMG) and end-effector forces felt by the user. Results: Results from simulation experiments indicated that the robot was capable of producing sufficient end-effector forces for functional resistance training. We also found the endpoint forces from the user were similar to the theoretical forces expected at any direction of motion. EMG results indicated that the device was capable of providing adjustable resistances based on subjects’ ability levels, as the muscle activation levels scaled with increasing magnet exposures. Conclusion: These results indicate that PaRRo is a feasible approach to provide functional resistance training to the muscles along the upper extremity. Significance: The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy.

via A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training – IEEE Journals & Magazine

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