Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot

Tianfang Zhang; Zhao Yao; Fenfen Chen; Jie Wang; Weiyi Shi; Jinjin Zheng; Ziwei Zhang; Zuobing Chen

doi:10.3791/66938

JoVE Journal > Medicine

Medicine

Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot

Published: September 06, 2024

doi:

10.3791/66938

Tianfang Zhang¹, Zhao Yao^1,2, Fenfen Chen^2,3, Jie Wang⁴, Weiyi Shi¹, Jinjin Zheng¹, Ziwei Zhang¹, Zuobing Chen^1,2,3

¹Department of Rehabilitation Medicine, The First Affiliated Hospital, School of Medicine,Zhejiang University, ²The Second Affiliated Hospital and Yuying Children’s Hospital,Wenzhou Medical University, ³Department of Rehabilitation Medicine,Taizhou Hospital Affiliated to Wenzhou Medical University, ⁴Affiliate Hospital of Shandong University of Traditional Chinese Medicine

Summary

This protocol describes an upper limb rehabilitation robot that provides intelligent feedback through four modes. These modes enhance upper limb function and flexibility, thereby improving patients’ quality of life.

Abstract

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leading to significant upper limb disabilities, thereby profoundly affecting individuals’ activities of daily living and diminishing their quality of life. Traditional rehabilitation methods for upper limb recovery post-stroke are often hindered by limitations, including therapist and patient fatigue, reliance on singular training methodologies, and lack of sustained motivation. Addressing these challenges, this study introduces an upper limb rehabilitation robot, which uses intelligent feedback motion control to improve therapeutic outcomes. The system is distinguished by its capability to adjust the direction and magnitude of force feedback dynamically, based on the detection of spastic movements during exercises, thereby offering a tailored therapeutic experience. This system is equipped with four distinct training modes, intelligent assessment of joint range of motion, and the ability to personalize training programs. Moreover, it provides an immersive interactive gaming experience coupled with comprehensive safety measures. This multifaceted approach not only elevates the engagement and interest of participants beyond traditional rehabilitation protocols but also demonstrates significant improvements in upper limb functionality and the activities of daily living among hemiplegic patients. The system exemplifies an advanced tool in upper limb rehabilitation, offering a synergistic blend of precision, personalization, and interactive engagement, thereby broadening the therapeutic options available to stroke survivors.

Introduction

Stroke, identified as an acute neurological event caused by the blockage or rupture of cerebral vessels, interrupts brain circulation¹, ranking as the second leading cause of death and a major contributor to long-term disability worldwide. On the initial day following a stroke, up to 80% of survivors experience upper limb dysfunction, with 30%-66% still facing challenges six months later². After one year, those with upper limb impairments report heightened anxiety, diminished quality of life, and reduced happiness³. Moreover, by 16 months post-stroke, only about 60% of hemiplegic individuals requiring hospital rehabilitation attain functional independence in basic daily activities, with those suffering from sensory, motor, and visual impairments being significantly more reliant on caregiver support⁴. Additionally, upper limb dysfunction impedes hand utility, particularly noted by increased muscle tension among weakened flexors and extensors during physical tasks⁵.

Despite various rehabilitation efforts, effectively addressing upper limb injuries in stroke survivors presents a formidable challenge⁶. High-intensity, repetitive task training has shown optimal outcomes but demands considerable therapist involvement, leading to high costs and logistical burdens⁷. Therefore, low-cost interventions are needed that do not increase therapists’ workload while increasing patient interest in training. The upper limb rehabilitation robot can serve as an alternative treatment to promote high-intensity exercise and reduce dependence on therapists¹. It is a newly developed upper limb intelligent feedback rehabilitation robot system (see Table of Materials). The device can output objective metrics (such as speed, torque, range of motion, position, etc.) to assess and monitor patients’ improvements and customize treatment according to varying degrees of motor impairment. It has high consistency and reproducibility for widespread use. Additionally, strong evidence supports high-intensity, high-repetitiveness, and task-oriented training in facilitating post-stroke motor recovery⁸.

On the other hand, rehabilitation robots are a relatively novel assistive treatment approach with advantages like high safety and durability⁹. The American Stroke Association recently released guidelines reporting that robot-assisted motor training can help patients improve post-stroke motor function and mobility in addition to conventional therapy¹⁰. A 2018 article in the Journal of Rehabilitation Medicine reported that combining robot-assisted training with conventional rehabilitation can significantly improve upper limb motor function in stroke patients, warranting clinical promotion¹¹. The system includes four training modes: constant speed training, power-assisted training, active training, and resistance training, and can conduct assessments of joint range of motion. A review of robot-assisted rehabilitation for subacute stroke patients indicated that robotic interventions significantly improved upper extremity functions, particularly in shoulder, elbow, and forearm performance, as assessed by the Functional Independence Measure and Fugl-Meyer Assessment Scale. These interventions also enhanced daily living activities, improving the quality of life¹⁰.

This study aims to evaluate the effectiveness of an intelligent feedback rehabilitation robot in rehabilitating upper limb motor functions in patients with early post-stroke hemiplegia, providing a scientific basis for rehabilitation strategies for stroke patients with hemiplegia.

Protocol

This study was approved by the ethics committee of the First Affiliated Hospital of Zhejiang University, China, and all research protocols were formulated in compliance with the principles of the Declaration of Helsinki. All patients provided written informed consent to participate in this study. The study recruited 24 patients with upper limb hemiplegia who were admitted to the rehabilitation ward of the First Affiliated Hospital of Zhejiang University from January 2023 to June 2023. Inclusion criteria were: first ischemic or hemorrhagic stroke confirmed by neuroimaging (CT or MRI), aged 45 to 75 years, within 6 months of onset, upper limb motor function impairment and unilateral hemiplegia (Fugl-Meyer Assessment for Upper Extremity, FMA-UE ≤40)¹²^,¹³, modified Ashworth Scale ≤2¹⁴, Mini-Mental State Examination (MMSE) >20 (indicating adequate cognitive function)¹⁵, and a clinically stable condition with underlying diseases well controlled, and signed informed consent. Exclusion criteria were: unstable intracranial condition, cognitive and language impairment, shoulder subluxation, shoulder/elbow/wrist mobility impairment, severe spasticity (Ashworth 3-4), and visual impairment. The details of the robot and the software used in this study are listed in the Table of Materials.

1. Study design

Generate a random number using SAS software to split all patients into two groups: experimental and control, each containing 12 patients.
Conduct initial assessments of upper limb motor function and self-care ability using FMA-UE¹², Brunnstrom score (BRS)¹⁶, and modified Barthel index (MBI)¹⁷ by a blinded rehabilitation therapist.
Administer basic drug therapy to all patients throughout the trial, focusing on blood pressure control, blood glucose management, blood lipid regulation, seizure prevention, etc.
Provide the control group with 30 min of routine upper limb rehabilitation training daily, including active and passive joint training, muscle strengthening, and finger movement exercises¹⁸.
1. Additionally, include 30 min of sanding board training daily¹⁹. Offer specialized therapy for lower limb dysfunction, aphasia, dysphagia, and other functional disorders as needed by professional therapists, administered five times a week for eight weeks.
Offer the experimental group the same 30 min routine upper limb rehabilitation therapy as the control group daily, supplemented with 30 min of upper limb rehabilitation robot training daily. Provide equivalent therapy for other functional disorders as provided to the control group.

2. Specific operation steps for the upper limb rehabilitation robot

Evaluation of joint range of motion and motor control ability
1. Ask the patient to sit in front of the robot, keeping the chest one punch away from the platform (Figure 1).
2. Place the affected hand on the end processor of the robot, and use gloves and binders to secure the wrist and hand to prevent slip-off during exercise.
3. Ask the patient to move the upper arm to the maximum and extend it as far as possible.
  NOTE: The instrument will automatically record the patient's hand movement trajectory to determine the patient's active joint movement range.
4. Put the healthy hand on the affected hand and maximally move the affected upper arm with the assistance of the healthy side.
  NOTE: The instrument recorded the patient's hand movement trajectory and obtained the passive joint range of motion. Passive range of motion measurements may be assisted by the therapist if the patient has bilateral mobility impairment.
5. Set the motor control evaluation parameters, including target repetition times, single exercise time, and single relaxation time.
  NOTE: The motor control evaluation parameters were set by the therapist according to the patient's FMA-UE score¹²and weekly evaluations using the robot's built-in assessment system, such as increasing the difficulty for participants with better upper limb strength, increasing the number of repetitions and reducing the rest time, to more accurately assess the patient's motor control.
6. Control the target point to move in different directions according to the motion path and direction displayed on the screen.
  NOTE: The instrument will evaluate the patient's motor control ability according to the patient's motor performance.
Selection of training mode
1. Select the isokinetic passive training mode if the upper limb muscles of the patient cannot contract at all or only have a small amount of contraction (BRS 1-2).
  NOTE: The robot provides complete help to drive the affected upper limb for the passive movement training exercise task.
2. Select the auxiliary movement training mode if the patient's upper limb can perform partial joint movement, but the movement is very slight, and the voluntary movement ability is poor (BRS 3).
  NOTE: The system can provide the corresponding auxiliary force in real-time according to the actual force degree of the patient and induce the active participation of the patient's upper limb to the greatest extent during the whole training process to form the correct movement mode.
3. Select the active training mode if the patient's upper limb muscle strength can produce large force or partial resistance (BRS 4).
  NOTE: The robot can further strengthen the main movement ability of the patient's upper limb.
4. Select the resistance training mode to further improve the accuracy and aiming control of the patient's upper limb if the patient's upper limb strength is already strong and can resist greater resistance (BRS 5-6).
Selection of training procedures
1. Choose the training procedure, noting that the system provides more than 10 interesting game programs so that patients can experience different VR scenes and interactive experiences, which greatly improves the training enthusiasm of patients (Figure 2).
Setting game parameters
1. Set the training time according to the physical condition of the patient, which can be about 10-20 min in general.
  NOTE: If the patient's upper limb strength is good, increase the single training time to improve the patient's exercise tolerance. If the patient's upper limb strength is poor, select a shorter single training time and allow the patient to complete the training program in multiple sessions.
2. Set the range of motion according to the evaluated range of motion of the joint, selecting from full range, medium range, or small range of motion.
3. Set the activity trajectory according to the characteristics of the patient's upper arm muscle strength, choosing the appropriate activity path to target and strengthen weak muscles.
4. Set the power assist or resistance value according to the muscle strength of the patient.
  NOTE: During the training process, the instrument can also automatically adjust the power assist and friction force according to the actual force feedback of the patient.
5. Set the protection threshold using mechanical feedback technology to detect when the patient's strength reaches the threshold, indicating spasms (manifested as discomfort, a sudden increase in muscle tone, or abnormal joint stiffness and locking). The device will issue an alarm and stop immediately to ensure the safety of the patient's training.
Specific training process
NOTE: Patients are trained with 2-3 game items per day, and different game items can be changed regularly.
1. Engage in the vegetable farm: In the virtual farm, ask the patient to control the small hands to grab fruits and vegetables and collect as many stars as possible.
  NOTE: This activity primarily targets the range of motion in elbow and wrist flexion and extension.
2. Participate in defend the base: In the scene of the virtual military base, ask the patient to accurately control the bullseye to shoot all the eliminated monsters.
  NOTE: This exercise aims to enhance muscle control in the elbow and wrist, improving the accuracy of shooting actions.
3. Play color dodgeball: On different roads and obstacles, ask the patient to control the ball to avoid obstacles of different colors to obtain gold coins.
  NOTE: This exercise involves shoulder, elbow, and wrist movements to improve muscle strength and joint mobility.
4. Navigate Star Wars: In the virtual space environment, ask the patient to control the position of the aircraft to shoot to destroy the virus while avoiding movement and enemy attacks, training muscle endurance and reaction force.
  NOTE: This training boosts upper limb endurance, reaction speed, and accuracy, and improves elbow and shoulder coordination and strength.
5. Participate in quality ball: Ask the patient to control the ball to reach and stay in the bull 's-eye; the closer the ball is to the bull 's-eye, the higher the score is.
  NOTE: This activity exercises elbow flexion, extension, shoulder adduction, abduction, and activates the biceps and triceps for precise control.
6. Play super ping-pong: In the virtual ball environment, ask the patient to control the ping-pong board to hit the ball and play table tennis with the opponent. The difficulty is advanced and upgraded, and the reaction and hand-eye coordination abilities are trained.
7. Engage in block world: Ask the patient to control bullseye shooting to destroy blocks, watch out for enemy attacks and collect as many coins as possible, train thinking strategy and hand-eye coordination.
8. Play ball: Ask the patient to control the ball to touch the target; the ball is scored, watch out for enemy attacks, and collect as many coins as possible.
9. Participate in legendary gunner: Ask the patient to hold the handle and continuously exert force in the direction of the arrow. The muscle groups of the upper limbs are isometric contraction, and the force is stored to fire to destroy the target.

3. Follow-up procedure

Evaluate all patients for FMA, BRS, and MBI again after 8 weeks of training by the same rehabilitation therapist.
Enter all data into the software for statistical analysis. Use a paired sample t-test for intra-group comparison and two independent sample t-tests for inter-group comparison. Consider P < 0.05 as statistically significant.

Representative Results

A total of 24 patients were enrolled and randomly assigned to either the control or experimental group (Table 1). There was no statistically significant difference between the two groups for sex, age, disease duration, or stroke type (P > 0.05). After 8 weeks of upper limb training, the Fugl-Meyer Assessment for Upper Extremity (FMA-UE)¹² was used to evaluate upper limb motor function, while the Brunnstrom Recovery Stage for Arm (BRS-Arm)¹⁶ and Brunnstrom Recovery Stage for Hand (BRS-Hand) were used to assess the upper limb recovery stage. The Modified Barthel Index (MBI)¹⁷ was used to evaluate patients' daily living abilities.

After rehabilitation training, both groups showed significant improvement in FMA-UE and BRS-Hand (Figure 3, Table 2). Furthermore, the upper limb rehabilitation robot training group demonstrated superior FMA-UE and MBI scores compared to the control group, indicating that the combination of upper limb rehabilitation robot training with conventional training can enhance upper limb motor function and promote daily living ability recovery in stroke patients during the recovery period.

Regarding efficacy, the rehabilitation outcomes of the upper limb rehabilitation robot combined with conventional upper limb rehabilitation training were better than those of solely using conventional training, suggesting that the upper limb rehabilitation robot could be considered an adjunctive treatment modality in clinical practice.

Figure 1: Setup for upper limb rehabilitation. A subject is seated at the upper limb rehabilitation robot, assisted by a physical therapist. The setup demonstrates the positioning and interaction between the subject and the rehabilitation equipment. Please click here to view a larger version of this figure.

Figure 2: Game interface in upper limb rehabilitation robot. The figure showcases the game content integrated into the upper limb rehabilitation robot's movement scheme. This interface is designed to engage the subject and facilitate therapeutic exercises through interactive gameplay. Please click here to view a larger version of this figure.

Figure 3: Statistical analysis of rehabilitation outcomes. The figure presents the statistical analysis of (A) Fugl-Meyer Assessment for Upper Extremity (FMA-UE), (B) Brunnstrom Recovery Stages for Arm (BRS-Arm) and (C) Hand (BRS-Hand), and (D) Modified Barthel Index (MBI) for the control and experimental groups before and after treatment. Significant differences are indicated by *P < 0.05, **P < 0.01, and ***P < 0.001. Please click here to view a larger version of this figure.

Group	n	Sex (n)		Age (`x±s, y )	Course of the disease (`x±s , d)	Type of stroke (n)		Hemiplegic side (n)
Group	n	Male	Female	Age (`x±s, y )	Course of the disease (`x±s , d)	Ischemic	Hemorrhagic	Left	Right
Control group (n = 12)	12	7	5	52.25 ± 6.81	33.33 ± 9.51	8	4	6	6
Experimental group (n = 12)	12	8	4	53.50 ± 7.98	32.41 ± 9.39	6	6	5	7
P		>0.05		>0.05	>0.05	>0.05		>0.05

Table 1: Baseline characteristics of control and experimental groups.

Group		FMA-UE	BRS-Arm	BRS-Hand	MBI
Control group (n = 12)	Per-treatment	18.50 ± 3.09	2.75 ± 0.97	1.92 ± 0.79	53.58 ± 11.22
Control group (n = 12)	Post-treatment	21.08 ± 2.94 *	3.17 ± 1.03	2.75 ± 0.97 *	56.75 ± 12.18
Experimental group (n = 12)	Per-treatment	18.67 ± 3.73	2.92 ± 0.79	2.00 ± 0.60	54.5 ± 12.04
Experimental group (n = 12)	Post-treatment	24.08 ± 2.78 ***^#	3.83 ± 0.94 *	3.5 ± 1.09 ***	67.83 ± 12.63 *^#
*P < 0.05, compared to pre-treatment; ^#P < 0.05, compared to the control group

Table 2: Comparison of FMA-UE and MBI scores pre- and post-training. This table compares the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Modified Barthel Index (MBI) scores between the control and experimental groups before and after the training period. The values are presented as mean ± standard deviation (x ± s).

Discussion

Building upon previous research²⁰, this study adopts an integrated approach by combining robotic training for upper limb rehabilitation with conventional therapeutic methods for post-stroke recovery. The current findings suggest that this integration substantially enhances upper limb motor function and improves the ability to perform activities of daily living (ADLs), surpassing the outcomes achieved with traditional rehabilitation techniques alone.

This investigation examines the efficacy of robotic interventions within virtual environments. Hondori et al. developed a system using webcam-based color marker tracking for hand rehabilitation, but it faced limitations such as dependence on external markers and hand tracking precision²¹. Similarly, Cipresso et al. and Avola et al. used Kinect and infrared depth cameras for VR-based rehabilitation, which were limited by game variety and discomfort from head-mounted displays²²^,²³. In contrast, our system employs advanced visual feedback and real-time quantitative data, significantly enhancing patient engagement and training effectiveness.

Further advancements have been made in refining exoskeleton gloves and enhancing remote rehabilitation capabilities. Cordella et al. and Farulla et al. focused on accurate motion replication and force feedback using sophisticated camera systems and master-slave control mechanisms²⁴^,²⁵. Recent advancements in gesture control and brain/neural-computer interfaces for exoskeleton-assisted hand rehabilitation have been introduced by Liu Wei and Soskadar et al., who utilize MYO armbands and incorporate EEG and EOG technologies to enhance gesture recognition and system adaptability over time²⁶^,²⁷^,²⁸. Our system builds on these advancements by offering automatic adjustment of assistance or resistance based on the patient's muscle strength, ensuring personalized and dynamic training optimization, which aligns with earlier studies demonstrating the benefits of personalized training²⁹.

In this study, a thorough analysis was conducted on the unique features of the upper limb rehabilitation robot and its ability to surpass traditional rehabilitation methods in clinical practice. Initially, the upper limb rehabilitation robot utilizes an advanced visual feedback system, providing real-time, quantitative visual feedback through large screens and scenario interaction³⁰. The system employs a progressive gaming format, which not only enhances the interactivity and appeal of the training but also significantly increases patient engagement and proactivity, similar to findings from previous studies. Research has shown that this method effectively improves the functional recovery of the affected upper limb by optimizing the recruitment process of muscle fibers³¹.

Compared to traditional sanding board training, the upper limb rehabilitation robot's visual feedback and task-oriented training offer distinct advantages, echoing previous research findings³⁰. One study has demonstrated that the system's visual feedback mechanism not only facilitates the correct linkage between sensory collection and motor control but also fosters precise movement patterns through continuous brain stimulation, enhancing the coordination and precision of upper limb movements³⁰. Additionally, the upper limb rehabilitation robot can automatically adjust the assistance or resistance applied based on changes in the patient's muscle strength, thus achieving personalized and dynamic optimization of the training. This feature greatly enhances patient initiative and training outcomes, consistent with prior research³².

The design of the system not only enhances rehabilitation outcomes but also optimizes the allocation of therapeutic resources, significantly improving treatment efficiency³². By allowing a therapist to supervise multiple patients using the upper limb rehabilitation robot simultaneously, the system efficiently utilizes the energy of rehabilitation department therapists, consistent with prior research. Therefore, the upper limb rehabilitation robot not only demonstrates breakthroughs in technological innovation but also shows immense potential in providing continuous and efficient rehabilitation services, making it a crucial tool for improving the rehabilitation training of patients with post-stroke upper limb impairments. These features establish the upper limb rehabilitation robot as an instrument of significant clinical relevance in the field of upper limb rehabilitation.

Through extensive repetitive training facilitated by the upper limb rehabilitation robot, multiple functions can be improved in patients¹¹. The intensive, repetitive training provided by the robotic system is instrumental in promoting neuroplasticity and functional restoration, targeting proprioceptive deficits and enhancing sensorimotor integration. The system's task-oriented gaming environment and progressive training protocols not only bolster patient motivation but also contribute to notable improvements in motor control³³^,³⁴. However, the study's limitations, including its small sample size, may not fully represent the broader population. Moreover, the study's limited duration and lack of long-term follow-up highlight the necessity for further research to confirm the enduring benefits of robotic-assisted training in the context of stroke rehabilitation. This study advocates for the integration of robotic and traditional therapies, pointing to a significant enhancement in recovery outcomes and supporting the widespread clinical adoption of personalized, engaging rehabilitation strategies.

Disclosures

The authors have nothing to disclose.

Acknowledgements

We are also thankful to the healthcare professionals and staff members at the First Affiliated Hospital of Zhejiang University for their support and cooperation throughout the research process.

Materials

Upper Limb Rehabilitation Robot[Fourier M2]	Shanghai Fourier Intelligence, China	ArmMotus M2	The upper limb intelligent force feedback motion control training system [M2] is a new generation of upper limb intelligent force feedback rehabilitation robot training system independently developed by Shanghai Fourier Intelligence. Based on core technologies such as force feedback, this training system can sense the patient's force and whether there is any spasticity when the patient completes the predetermined action, and then change the power assist or resistance of the device itself, so as to improve the upper limb motor dysfunction. Through goal-oriented training, M2 endows games with training, increases the enthusiasm of patients, and more effectively exercises the gross motor function and cognitive function of patients' upper limbs.
SAS software	SAS Institute		https://www.sas.com/en_in/home.html
SPSS software	IBM	version 26	https://www.ibm.com/products/spss-statistics

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Zhang, T., Yao, Z., Chen, F., Wang, J., Shi, W., Zheng, J., Zhang, Z., Chen, Z. Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot. J. Vis. Exp. (211), e66938, doi:10.3791/66938 (2024).

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