Posts Tagged exoskeletons

[WEB] Wearable robotics in the rehabilitation continuum of care: assessment, treatment and home assistance: Editorial

Editorial on the Research Topic
Wearable robotics in the rehabilitation continuum of care: assessment, treatment, and home assistance

Global population aging is posing long-term challenges to societal welfare and sustainability. The prevalence of age-associated chronic diseases is causing a growing demand for physical and cognitive rehabilitation. Healthcare providers are faced with the challenge of guaranteeing a continuum of care after hospital discharge, and the management and delivery of outpatient and home rehabilitation has become critical (Gonzaga et al., 2023).

Wearable robotics can support people affected by neurological conditions in recovering their motor functions by aiding therapists in providing customized, task-specific rehabilitation training, or by augmenting human movement capabilities in activities of daily living (ADLs). The number of commercially available wearable robotics is rising across different application domains, predominantly in the healthcare and industry sectors. Nevertheless, several open challenges remain regarding actuation, sensing, and control, which limit the wide adoption of these devices outside the controlled laboratory environment (Babič et al., 2021).

Exoskeletons made of physically compliant structures, usually referred to as soft exoskeletons or exosuits, are promising for their capability to assist users with mild to moderate impairments while maintaining a lightweight and compact structure. Maldonado-Mejía et al. presented a fabric-based hand exoskeleton with pneumatic actuation (ExHand), designed for assisting grasping tasks in ADLs. The capability of the device to maintain stable contact with different objects was verified on 10 participants without any hand impairments. Di Natali et al. developed a modular lower-limb exosuit for walking assistance (XoSoft). The exosuit uses soft pneumatic quasi-passive actuators, which can modulate the forces generated by the deformation of an elastic tendon via a variable stiffness textile-based clutch. This approach mimics the behavior of the human muscle and tendons and allows the injection of positive energy into the gait cycle without requiring powerful actuators.

Over the last few years, hybrid neuroprostheses combining low-power robotic actuation with functional electrical stimulation (FES) have also been proposed. Such hybrid systems provide biomimetic assistance by distributing the power resources between the user’s muscles and the robotic actuator. A recent review paper analyzed the efficacy of hybrid neuroprostheses employed in randomized controlled trials for upper-limb impairment after stroke, showing their positive effects in the recovery of upper-limb motor function (Höhler et al., 2023). Still, most of the current hybrid devices use both actuation types to address separate functions for each (e.g., distal or proximal joint actuation). Dunkelberger et al. presented a controller based on model predictive control for a hybrid upper-limb powered exoskeleton to assist individuals with spinal cord injuries. The controller was designed to provide an optimal distribution of power at the same joint, favoring FES over robotic actuation to assist in tracking movements.

Physical training, either via FES or via robotic devices, can also be combined with cognitive training by means of serious games based on virtual or augmented reality. Such hybrid combinations are suggested to increase patients’ engagement, motivation, and adherence to the treatment. Höhler et al. recruited 18 patients after stroke in a randomized crossover trial to investigate the feasibility and benefits of combining serious games with contralaterally electromyography-triggered FES. The results of this study also provide valuable insight into the potential translation of such systems to the home environment.

To ensure a continuum of care from hospital to home rehabilitation, it is paramount to develop devices that are intuitive, portable, and easy to use. These devices should be designed in a way that allows them to be worn and used without the supervision of the therapist, gathering at the same time quantitative measures to monitor the progress of the therapy. Bressi et al. investigated the use of a robotic end-effector type device (iCONE, Heaxel, Italy) for the home-based rehabilitation of chronic stroke patients. The study encourages the exploration of possible correlations between the clinical evaluation scales and the metrics obtained via the robot sensors, with the inclusion of a larger pool of participants. Urrutia et al. investigated the correlation between the scores of the Modified Ashwort Scale and the measurements obtained with the Amadeo® (Tyromotion, Austria) finger-hand rehabilitation device for the assessment of joint spasticity. Making a reliable and standardized robotic assessment of joint spasticity is still an open challenge due to the need to capture an intricate interaction of neurophysiological mechanisms (Pilla et al., 2020).

Longitudinal assessment protocols merging clinical evaluations with the quantitative measurement of physiological and biomechanical parameters are crucial for achieving more efficient and cost-effective rehabilitation programs. Such assessments would be extremely valuable for the stratification of patients into groups with similar characteristics to identify the most appropriate and customized treatment plan for each individual. Tesfazgi et al. analyzed the sources of uncertainty in the estimation of the human arm impedance using upper-limb wearable robotics. These uncertainties arise from the physical human–robot interaction, and their identification plays a pivotal role in the reliable and automated estimation of the user’s neuromechanical state. This, in turn, can open new possibilities for true customization of rehabilitation treatments. Das et al. proposed a method for the online classification of compensatory movement strategies based on kinematic information. The automatic detection of compensatory motion could be exploited to inform the patient about the correct execution of the task, e.g., during home training without the therapist’s supervision. In addition, such information could be used to adjust the assistance profiles of robotic devices to enforce the proper movement kinematics. Finally, Wu et al. demonstrated the benefits of closed-loop cueing training for people with Parkinson’s disease. This strategy can provide patients with adaptive, optimized cues to improve their gait performance by learning a personalized model of the user’s responsiveness to the cues.

Overall, the articles in this Research Topic provide different insights for the further development of wearable technologies across the rehabilitation continuum of care. These insights revolve around three main pillars: the need for customization of the rehabilitation treatment, the importance of an objective quantification and characterization of the patient’s conditions, and the value of smart mechatronic designs to guarantee the seamless and intuitive use of wearable robotics. Further advancements in each of these three pillars will be paramount to ultimately enable the translation of wearable robotics into our daily lives.

Source

, , , ,

Leave a comment

[ARTICLE] Development and simulation of a device for upper limb rehabilitation – Full Text

Abstract

Robotics with exoskeletons has opened a new era of research in the field of modern rehabilitation and assistive technologies. The technology promises to improve the functionality of the upper limbs, which are necessary for daily operations. Exoskeleton technology is developing rapidly but requires interdisciplinary research to solve technical problems such as kinematic compatibility and the development of effective human-robot interaction. This article presents a new design of a device that helps to rehabilitate the upper extremities. The proposed design is characterized by a lightweight structure with an adaptable geometry for various users with low cost and easy to wear characteristics. The CAD model is developed for design details and for modeling, the results of which provide data on the feasibility of the proposed design and its characteristics in the main operational characteristics.

1. Introduction

Currently, rehabilitation technologies are part of strategies that facilitate the integration of people with trauma that generates disability, this is a harmonious understanding of technology, technology, and health [1]. James Reswick describes rehabilitation engineering as the application of science and technology to reduce the limitations of people with disabilities [2]. Rehabilitation engineering is interdisciplinary and unites specialists such as doctors, nurses, physiotherapists, occupational therapists, biologists, engineers, physicists, and chemists. Indeed, rehabilitation devices are developed by a group of specialized professionals with interdisciplinary training, as in the case of bioengineering, which is the application of knowledge gathered in a fruitful balance between engineering and medical science [3]. For this reason, many researchers are currently working intensively on the creation of universal robots that can be worn on the body. Today, research in this area has made it possible to obtain various solid exoskeletons that move synchronously with human limbs. Many of them can support the weight of the human body and even the additional load that comes from lifting weights by a person. In some cases, such exoskeletons can also replace human muscles or bones. However, this type of robot has two obvious drawbacks: it is rigid and very heavy, so a person may have difficulty carrying it. In practice, in addition to this, there are various soft and flexible exosuits that help to perform individual movements of parts of the human body, while consuming less energy. Another advantage of such exo costumes: they do not damage ligaments, tendons, and joints. These robots are capable not only of normalizing partially damaged motor functions, but also of performing lost motor functions, if we take, for example, the elderly or disabled. Another part of people who need exoskeletons includes people with muscle weakness or other degenerative diseases [4].

The goal of this research project is to create one or more complex structures called harmonious exoskeletons that can help people with muscle weakness or other degenerative diseases. In particular, the emphasis is on structures designed to support daily movements of a person with hands (for example, holding a glass of water) and rehabilitation of muscle movements.

This article is organized as follows. The section physical model and design of the human hand describes a methodology that includes the physical model and design of the human hand. The conceptual design section shows the implementation of the methodology for designing an exoskeleton for passive rehabilitation of the upper limb with a detailed description of each stage. The conclusion and further work are presented in Section 3.[…]

Continue

Table 1. Arm muscles [8]

, , , , , , , , ,

Leave a comment

[Abstract] AGREE: A compliant-controlled upper-limb exoskeleton for physical rehabilitation of neurological patients

Abstract

In this work, we introduce the Agree exoskeleton, a robotic device designed to assist in upper-limb physical rehabilitation for post-stroke survivors. We detail the exoskeleton design at the mechatronic, actuation, and control levels. The Agree exoskeleton features a lightweight and adaptable mechanical design, which can be used with both the right and left arm, supporting three active degrees-of-freedom at the shoulder and one at the elbow. The device embodies a spring-pulley anti-gravity system to minimize torque requirements and has torque sensors on each joint for safe and smooth interaction with the user. The Agree control system, which employs a loadcell-based impedance control method, offers various modes of human-robot interaction, such as passive-assisted, active-assisted, and active-resistive exercises. Results from our experimental characterization demonstrate that the exoskeleton is capable of both compliant and rigid behavior, providing a wide range of haptic impedance and transparent behavior to both user-generated and therapist-generated forces. Our findings indicate that the Agree exoskeleton may be a viable option for safely assisting patients with neurological conditions.

Source

, , , , , , , , , , , , , , , ,

Leave a comment

[Abstract + References] Effect of robot-assisted gait training on quality of life and depression in neurological impairment: A systematic review and meta-analysis

Abstract

Objective

Robot-assisted gait training (RAGT) is often used as a rehabilitation tool for neurological impairments. The purpose of this study is to investigate the effects of rehabilitation with robotic devices on quality of life and depression.

Data sources

Two electronic databases (MEDLINE and Scopus) were searched for studies from inception up to December 2022.

Review methods

Randomized controlled trials (RCTs) and non-RCTs were pooled separately for analyses, studying each one’s mental and physical health and depression. Random effect meta-analyses were run using standardized mean difference and 95% confidence interval (CI).

Results

A total of 853 studies were identified from the literature search. 31 studies (17 RCTs and 14 non-RCTs) including 1151 subjects met the inclusion criteria. 31 studies were selected for the systematic review and 27 studies for the meta-analysis. The outcome measure of mental health significantly improved in favor of the RAGT group in RCTs and non-RCTs (adjusted Hedges’g 0.72, 95% CI: 0.34–1.10, adjusted Hedges g = 0.80, 95% CI 0.21-1.39, respectively). We observed a significant effect of RAGT on physical health in RCTs and non-RCTs (adjusted Hedges’g 0.58, 95% CI 0.28, 0.88, adjusted Hedges g = 0.73, 95% CI 0.12, 1.33). After realizing a sensitivity analysis in RCTs, a positive impact on depression is observed (Hedges’ g of −0.66, 95% CI −1.08 to −0.24).

Conclusion

This study suggests that RAGT could improve the quality of life of patients with neurological impairments. A positive impact on depression is also observed in the short term. Further studies are needed to differentiate grounded and overgrounded exoskeletons as well as RCT comparing overground exoskeletons with a control group.

Get full access to this article

View all access and purchase options for this article.

GET ACCESS

References

1. Stolze H, Klebe S, Baecker C, et al. Prevalence of gait disorders in hospitalized neurological patients. Mov Disord 2005; 20: 89–94.

Crossref

PubMed

ISI

Google Scholar

2. Rodríguez-Fernández A, Lobo-Prat J, Font-Llagunes JM. Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments. J Neuroeng Rehabil 2021; 18: 1–21.

Crossref

PubMed

Google Scholar

3. Fang CY, Tsai JL, Li GSet al. et al. Effects of robot-assisted gait training in individuals with spinal cord injury: A meta-analysis. Biomed Res Int 2020; 2020: 2102785.

Crossref

PubMed

Google Scholar

4. Wang L, Zheng Y, Dang Y, et al. Effects of robot-assisted training on balance function in patients with stroke: A systematic review and meta-analysis. J Rehabil Med 2021; 53: jrm00174.

Crossref

PubMed

Google Scholar

5. Nedergård H, Arumugam A, Sandlund Met al. et al. Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: A systematic review and meta-analysis. J Neuroeng Rehabil 2021; 18: 64.

Crossref

PubMed

Google Scholar

6. Bowman T, Gervasoni E, Amico AP, et al. What is the impact of robotic rehabilitation on balance and gait outcomes in people with multiple sclerosis? A systematic review of randomized control trials. Eur J Phys Rehabil Med. Edizioni Minerva Medica 2021; 57: 246–253.

Crossref

PubMed

Google Scholar

7. Cochrane Handbook for Systematic Reviews of Interventions | Cochrane Training. [cited 13 Dec 2022], https://training.cochrane.org/handbook.

Google Scholar

8. Morrison A, Polisena J, Husereau D, et al. The effect of English-language restriction on systematic review-based meta-analyses: A systematic review of empirical studies. Int J Technol Assess Health Care 2012; 28: 138–144.

Crossref

PubMed

ISI

Google Scholar

9. Higgins JPT, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928.

Crossref

PubMed

Google Scholar

10. Cantrell A, Croot E, Johnson M, et al. Access to primary and community health-care services for people 16 years and over with intellectual disabilities: A mapping and targeted systematic review. Health Serv Deliv Res 2020; 8: 1–142.

Crossref

Google Scholar

11. Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol 2005; 5: 13.

Crossref

PubMed

Google Scholar

12. Sconza C, Negrini F, di Matteo B, et al. Robot-assisted gait training in patients with multiple sclerosis: A randomized controlled crossover trial. Medicina (Lithuania) 2021; 57: 713.

Crossref

PubMed

Google Scholar

13. van Nes IJW, van Dijsseldonk RB, van Herpen FHMet al. et al. Improvement of quality of life after 2-month exoskeleton training in patients with chronic spinal cord injury. J Spinal Cord Med 2022: 1–7. https://doi.org/10.1080/10790268.2022.2052502.

PubMed

Google Scholar

14. Sawada T, Okawara H, Matsubayashi K, et al. Influence of body weight-supported treadmill training with voluntary-driven exoskeleton on the quality of life of persons with chronic spinal cord injury: A pilot study. Int J Rehabil Res 2021; 44: 343–349.

Crossref

PubMed

Google Scholar

15. Maggio MG, Naro A, de Luca R, et al. Body representation in patients with severe spinal cord injury: A pilot study on the promising role of powered exoskeleton for gait training. J Pers Med 2022; 12: 619. https://doi.org/10.3390/jpm12040619.

PubMed

Google Scholar

16. Louie DR, Mortenson WB, Durocher M, et al. Efficacy of an exoskeleton-based physical therapy program for non-ambulatory patients during subacute stroke rehabilitation: A randomized controlled trial. J Neuroeng Rehabil 2021; 18: 149.

Crossref

PubMed

Google Scholar

17. Meng G, Ma X, Chen P, et al. Effect of early integrated robot-assisted gait training on motor and balance in patients with acute ischemic stroke: A single-blinded randomized controlled trial. Ther Adv Neurol Disord 2022; 15, 1-10 https://doi.org/10.1177/17562864221123195.

PubMed

Google Scholar

18. Miura K, Tsuda E, Kogawa M, et al. Effects of gait training with a voluntary-driven wearable cyborg, hybrid assistive limb (HAL), on quality of life in patients with neuromuscular disease, able to walk independently with aids. J Clin Neurosci 2021; 89: 211–215.

Crossref

PubMed

Google Scholar

19. Platz T, Gillner A, Borgwaldt Net al. et al. Device-training for individuals with thoracic and lumbar spinal cord injury using a powered exoskeleton for technically assisted mobility: Achievements and user satisfaction. Biomed Res Int 2016; 2016: 8459018.

Crossref

PubMed

Google Scholar

20. Poritz J, Patterson L, Tseng SCet al. et al. Evaluation of quality of life measures for wearable robotic therapy in individuals with neurological disability: A preliminary report. In: 2017 international symposium on wearable robotics and rehabilitation, WeRob, Houston, TX, USA, 2017, pp. 1–2.

Crossref

Google Scholar

21. Tsai CY, Asselin PK, Hong E, et al. Exoskeletal-assisted walking may improve seated balance in persons with chronic spinal cord injury: A pilot study. Spinal Cord Ser Cases 2021; 7: 20.

Crossref

PubMed

Google Scholar

22. Juszczak M, Gallo E, Bushnik T. Examining the effects of a powered exoskeleton on quality of life and secondary impairments in people living with spinal cord injury. Top Spinal Cord Inj Rehabil 2018; 24: 336–342.

Crossref

PubMed

Google Scholar

23. Calabrò RS, de Cola MC, Leo A, et al. Robotic neurorehabilitation in patients with chronic stroke: Psychological well-being beyond motor improvement. Int J Rehabil Res 2015; 38: 219–225.

Crossref

PubMed

Google Scholar

24. McGibbon C, Sexton A, Gryfe P, et al. Effect of using of a lower-extremity exoskeleton on disability of people with multiple sclerosis. Disabil Rehabil: Assist Technol 2021: 1–8.

Crossref

Google Scholar

25. Baunsgaard CB, Nissen UV, Brust AK, et al. Exoskeleton gait training after spinal cord injury: An exploratory study on secondary health conditions. J Rehabil Med 2018; 50: 806–813.

Crossref

PubMed

Google Scholar

26. Bertolucci F, di Martino S, Orsucci D, et al. Robotic gait training improves motor skills and quality of life in hereditary spastic paraplegia. NeuroRehabilitation 2015; 36: 93–99.

Crossref

PubMed

Google Scholar

27. Chun A, Asselin PK, Knezevic S, et al. Changes in bowel function following exoskeletal-assisted walking in persons with spinal cord injury: An observational pilot study. Spinal Cord 2020; 58: 459.

Crossref

PubMed

Google Scholar

28. Kim HS, Park JH, Lee HS, et al. Effects of wearable powered exoskeletal training on functional mobility, physiological health and quality of life in non-ambulatory spinal cord injury patients. J Korean Med Sci 2021; 36: 1–15.

Google Scholar

29. Kozlowski AJ, Fabian M, Lad Det al. et al. Feasibility and safety of a powered exoskeleton for assisted walking for persons with multiple sclerosis: A single-group preliminary study. Arch Phys Med Rehabil 2017; 98: 1300–1307.

Crossref

PubMed

Google Scholar

30. Wu M, Landry JM, Kim Jet al. et al. Robotic resistance/assistance training improves locomotor function in individuals poststroke: A randomized controlled study. Arch Phys Med Rehabil 2014; 95: 799–806.

Crossref

PubMed

ISI

Google Scholar

31. Calabrò RS, Russo M, Naro A, et al. Robotic gait training in multiple sclerosis rehabilitation: Can virtual reality make the difference? Findings from a randomized controlled trial. J Neurol Sci 2017; 377: 25–30.

Crossref

PubMed

Google Scholar

32. Russo M, Dattola V, de Cola MC, et al. The role of robotic gait training coupled with virtual reality in boosting the rehabilitative outcomes in patients with multiple sclerosis. Int J Rehabil Res 2018; 41: 166–172.

Crossref

PubMed

Google Scholar

33. Park C, Oh-Park M, Dohle C, et al. Effects of innovative hip-knee-ankle interlimb coordinated robot training on ambulation, cardiopulmonary function, depression, and fall confidence in acute hemiplegia. NeuroRehabilitation 2020; 46: 577–587.

Crossref

PubMed

Google Scholar

34. Taveggia G, Borboni A, Mulé Cet al. et al. Conflicting results of robot-assisted versus usual gait training during postacute rehabilitation of stroke patients: A randomized clinical trial. Int J Rehabil Res 2016; 39: 29.

Crossref

PubMed

Google Scholar

35. Dundar U, Toktas H, Solak O, et al. A comparative study of conventional physiotherapy versus robotic training combined with physiotherapy in patients with stroke. Top Stroke Rehabil. 2015; 21: 453–461.

Crossref

Google Scholar

36. Gorman PH, Forrest GF, Asselin PK, et al. The effect of exoskeletal-assisted walking on spinal cord injury bowel function: Results from a randomized trial and comparison to other physical interventions. J Clin Med 2021; 10: 64.

Crossref

Google Scholar

37. Manuli A, Maggio MG, Latella D, et al. Can robotic gait rehabilitation plus virtual reality affect cognitive and behavioural outcomes in patients with chronic stroke? A randomized controlled trial involving three different protocols. J Stroke Cerebrovasc Dis 2020; 29: 104994.

Crossref

PubMed

Google Scholar

38. Straudi S, Fanciullacci C, Martinuzzi C, et al. The effects of robot-assisted gait training in progressive multiple sclerosis: A randomized controlled trial. Mult Scler 2016; 22: 373–384.

Crossref

PubMed

ISI

Google Scholar

39. Mustafaoglu R, Erhan B, Yeldan Iet al. et al. Does robot-assisted gait training improve mobility, activities of daily living and quality of life in stroke? A single-blinded, randomized controlled trial. Acta Neurol Belg 2020; 120: 335–344.

Crossref

PubMed

Google Scholar

40. de Luca R, Maresca G, Balletta T, et al. Does overground robotic gait training improve non-motor outcomes in patients with chronic stroke? Findings from a pilot study. J Clin Neurosci 2020; 81: 240–245.

Crossref

PubMed

Google Scholar

41. Russo M, Maggio MG, Naro A, et al. Can powered exoskeletons improve gait and balance in multiple sclerosis? A retrospective study. Int J Rehabil Res 2021; 44: 126–130.

Crossref

PubMed

Google Scholar

42. The EuroQol Group. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy (New York) 1990; 16: 199–208.

Crossref

PubMed

ISI

Google Scholar

43. Ware JE, Kosinski M, Keller SD. A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Med Care 1996; 34: 220–233.

Crossref

PubMed

ISI

Google Scholar

44. Ware JEJr, Sherbourne CD, Ware JJet al. et al. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992; 30: 473–483.

Crossref

PubMed

ISI

Google Scholar

45. Williams LS, Weinberger M, Harris LEet al. et al. Development of a stroke-specific quality of life scale. Stroke 1999; 30: 1362–1369.

Crossref

PubMed

ISI

Google Scholar

46. Tulsky DS, Kisala PA. The spinal cord injury – quality of life (SCI-QOL) measurement system: Development, psychometrics, and item bank calibration. J Spinal Cord Med 2015; 38: 51.

Google Scholar

47. Tulsky DS, Kisal PA, Tate DGet al. et al. Development and psychometric characteristics of the SCI-QOL bladder management difficulties and bowel management difficulties item banks and short forms and the SCI-QOL bladder complications scale. J Spinal Cord Med 2015; 38: 288–302.

Crossref

PubMed

Google Scholar

48. Cella D, Lai JS, Nowinski CJ, et al. Neuro-QOL: Brief measures of health-related quality of life for clinical research in neurology. Neurology. 2012; 78: 1860.

Crossref

PubMed

ISI

Google Scholar

49. Diener E, Emmons RA, Larsem RJet al. et al. The satisfaction with life scale. J Pers Assess 1985; 49: 71–75.

Crossref

PubMed

ISI

Google Scholar

50. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23: 56–62.

Crossref

PubMed

ISI

Google Scholar

51. Grossi E, Compare A. Psychological general well-being Index (PGWB). In: Michalos AC (ed) Encyclopedia of quality of life and well-being research. Netherlands: Springer, 2014, pp. 5152–5156.

Crossref

Google Scholar

52. Williams LS, Brizendine EJ, Plue L, et al. Performance of the PHQ-9 as a screening tool for depression after stroke. Stroke 2005; 36: 635–638.

Crossref

PubMed

ISI

Google Scholar

53. Hubley AM. Beck depression inventory. In: Michalos AC (ed) Encyclopedia of quality of life and well-being Research. Netherlands: Springer, 2014, pp. 338–345.

Crossref

Google Scholar

54. Kim SY, Yang L, Park IJ, et al. Effects of innovative WALKBOT robotic-assisted locomotor training on balance and gait recovery in hemiparetic stroke: A prospective, randomized, experimenter blinded case-control study with a four-week follow-up. IEEE Trans Neural Syst Rehabil Eng 2015; 23: 636–642.

Crossref

PubMed

ISI

Google Scholar

55. Louie DR, Eng JJ, Lam T. Gait speed using powered robotic exoskeletons after spinal cord injury: A systematic review and correlational study. J Neuroeng Rehabil 2015; 12: 82.

Crossref

PubMed

Google Scholar

56. Baylor C, Yorkston K, Jensen Met al. et al. Scoping review of common secondary conditions after stroke and their associations with age and time post stroke. 2014; 21: 371–382.

Crossref

Google Scholar

57. Jensen MP, Truitt AR, Schomer KGet al. et al. Frequency and age effects of secondary health conditions in individuals with spinal cord injury: A scoping review. Spinal Cord 2013; 51: 882–892.

Crossref

PubMed

Google Scholar

58. Miller LE, Zimmermann AK, Herbert WG. Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: Systematic review with meta-analysis. Med Devices (Auckl) 2016; 9: 455–466.

PubMed

Google Scholar

Source

, , , , ,

Leave a comment

[Abstract + References] Development and evaluation of a hand exoskeleton for finger rehabilitation – Conference Publication

Abstract:

Exoskeleton robots are now prevalent in hand rehabilitation medical training, and they can effectively drive a variety of rehabilitative movements in a hand that has lost its motor ability. To adapt to the hand’s physiological structure and motion characteristics, a hybrid-driven exoskeleton hand based on tendon rope and linkage and its validation experiments are proposed in this paper. The exoskeleton hand can assist one to five fingers independently or even assist a joint alone. Wearing the robot retains the physiological touch of the hand to the maximum extent, which is beneficial to rehabilitation. In addition, patients can also carry out rehabilitation training independently, and the control mode is simple and practical. To verify whether the exoskeleton can reach the grip standard of healthy hands, the Leap Motion Controller is also used to conduct experimental verification of finger movement wearing the exoskeleton. The results show that the maximum average differences between the angles of the finger flexion motion joints (MCP and PIP) with and without the exoskeleton are 10.33 degrees and 11.06 degrees. It was verified that the exoskeleton could meet the requirements of finger flexion and extension for assisted motion within a specific error range.

References

1.

B. H. Dobkin, “Strategies for stroke rehabilitation”, The Lancet Neurology, vol. 3.9, pp. 528-536, 2004.

Show in Context CrossRef  Google Scholar 

2.

L. Bixia et al., “The effect of coordinated nursing on the early rehabilitation of stroke patients with hemiplegia”, General nursing., vol. 17, no. 12, pp. 52-54, 2019.

Show in Context Google Scholar 

3.

X. Lingfeng et al., “A study on the current status of rehabilitation therapy education in China”, China Rehabilitation., vol. 34, no. 10, pp. 557-560, 2019.

Show in Context Google Scholar 

4.

O. Sandoval-Gonzalez et al., “Design and development of a hand exoskeleton robot for active and passive rehabilitation”, International Journal of Advanced Robotic Systems, vol. 13.2, pp. 66, 2016.

Show in Context CrossRef  Google Scholar 

5.

Y. Yun et al., “Accurate torque control of finger joints with UT hand exoskeleton through Bowden cable SEA”, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.

Show in Context View Article 

 Google Scholar 

6.

A. Chiri et al., “HANDEXOS: Towards an exoskeleton device for the rehabilitation of the hand”, 2009 IEEE/RSJ international conference on intelligent robots and systems, 2009.

Show in Context View Article 

 Google Scholar 

7.

M. Fontana, A. Dettori, F. Salsedo et al., “Mechanical design of a novel hand exoskeleton for accurate force displaying”, 2009 IEEE International Conference on Robotics and Automation, 2009.

Show in Context View Article 

 Google Scholar 

8.

E. B. Brokaw et al., “Hand Spring Operated Movement Enhancer (HandSOME): a portable passive hand exoskeleton for stroke rehabilitation”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19.4, pp. 391-399, 2011.

Show in Context View Article 

 Google Scholar 

9.

X. Chen et al., “Soft exoskeleton glove for hand assistance based on human-machine interaction and machine learning”, 2020 IEEE International Conference on Human-Machine Systems (ICHMS), 2020.

Show in Context View Article 

 Google Scholar 

10.

M. K. Burns, D. Pei and R. Vinjamuri, “Myoelectric control of a soft hand exoskeleton using kinematic synergies”, IEEE transactions on biomedical circuits and systems, vol. 13.6, pp. 1351-1361, 2019.

Show in Context View Article 

 Google Scholar 

11.

H. Huang et al., “Characterization and evaluation of a cable-actuated flexible hand exoskeleton”, 2020 17th International Conference on Ubiquitous Robots (UR), 2020.

Show in Context View Article 

 Google Scholar 

12.

Y. Park, J. Lee and J. Bae, “Development of a wearable sensing glove for measuring the motion of fingers using linear potentiometers and flexible wires”, IEEE Transactions on Industrial Informatics, vol. 11.1, pp. 198-206, 2014.

Show in Context Google Scholar 

13.

I. M. Bullock, J. Borras and A. M. Dollar, “Assessing assumptions in kinematic hand models: a review”, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2012.

Show in Context View Article 

 Google Scholar 

14.

P. Chophuk et al., “Hand postures for evaluating trigger finger using leap motion controller”, 2015 8th Biomedical Engineering International Conference (BMEiCON), 2015.

Show in Context View Article 

 Google Scholar 

15.

H. Kaji and M. Sugano, “A noncontact tremor measurement system using leap motion”, Proceedings of the 6th international conference on informatics environment energy and applications, 2017.

Show in Context CrossRef  Google Scholar 

16.

P. Gunawardane and N. T. Medagedara, “Comparison of hand gesture inputs of leap motion controller & data glove in to a soft finger”, 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), 2017.

Show in Context View Article 

 Google Scholar 

17.

F. Weichert et al., “Analysis of the accuracy and robustness of the leap motion controller”, Sensors, vol. 13.5, pp. 6380-6393, 2013.

Show in Context CrossRef  Google Scholar 

18.

D. Bachmann, F. Weichert and G. Rinkenauer, “Review of three-dimensional human-computer interaction with focus on the leap motion controller”, Sensors, vol. 18.7, pp. 2194, 2018.

Show in Context CrossRef  Google Scholar 

Published in: 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)

, , , , , , , , , , , , , , , ,

Leave a comment

[Abstract + References] An adaptive upper-limb stroke rehabilitation system – AIP Conference Proceedings

ABSTRACT

People having disability is not uncommon in the world that we live in today. The number kept on increasing each year. People with disability are either born with it or they may have suffered from stroke or other similar illness which result in losing motor function. With the increasing number of patients, rehabilitation centres are shortage of therapist to cater the overwhelming number of patients. It should be noted that therapy has to be done as early and as frequent as possible for it to be effective. This causes therapists to suffer from fatigue due to handling a large amount of patient daily to ensure all are treated. With the advancement of technology, various types of robots have been developed to help the disabled community. These exoskeleton robots typically operate alongside human limbs. Thus, a study in this field based on engineering concept is vital. It should be noted that rehabilitation robot is not replacing therapist but are built with the purpose of assisting. In this project, an upper limb robot is developed to help in the rehabilitation process. A controller is used to control the speed of the motors to which is important for therapy. In the process, the patients are required to complete the task given by the therapist accordingly. If the patient is able to move the upper limb voluntarily, the speed of the motor would decrease accordingly up to 50% of the actual speed. By decreasing the speed, naturally the muscle would resist the motion therefore it exercise the muscle by encouraging it to move more.

REFERENCES

  1. 1.Costin, H., Bejinariu, S. I., Dimitriu, B., & Anton-Prisacariu, B. (2014). Assessment of rehabilitation degree in case of stroke by using image analysis of range of motion. EPE 2014 – Proceedings of the 2014 International Conference and Exposition on Electrical and Power Engineering, Epe, 557–560. https://doi.org/10.1109/ICEPE.2014.6969971Google ScholarCrossref
  2. 2.Sutapun, A., & Sangveraphunsiri, V. (2017). A novel design and implementation of a 4-DOF upper limb exoskeleton for stroke rehabilitation with active assistive control strategy. Engineering Journal, 21(7), 275–291. https://doi.org/10.4186/ej.2017.21.7.275Google ScholarCrossref
  3. 3.Kwakkel, G., Kollen, B. J., Grond, J. Van Der, & Prevo, A. J. H. (2003). Probability of Regaining Dexterity in the Flaccid Upper Limb. https://doi.org/10.1161/01.STR.0000087172.16305.CDGoogle ScholarCrossref
  4. 4.Guo, S., Zhang, F., Wei, W., Guo, J., & Ge, W. (2013). Development of force analysis-based exoskeleton for the upper limb rehabilitation system. 2013 ICME International Conference on Complex Medical Engineering, CME 2013, 285–289. https://doi.org/10.1109/ICCME.2013.6548256Google ScholarCrossref
  5. 5.Ren, Y., Park, H. S., & Zhang, L. Q. (2009). Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation. 2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009, 761–765. https://doi.org/10.1109/ICORR.2009.5209482Google ScholarCrossref
  6. 6.Rahman, M. H., Saad, M., Kenné, J. P., & Archambault, P. S. (2010). Exoskeleton robot for rehabilitation of elbow and forearm movements. 18th Mediterranean Conference on Control and Automation, MED’10 – Conference Proceedings, 1567–1572. https://doi.org/10.1109/MED.2010.5547826Google ScholarCrossref

Source

, , , , , ,

Leave a comment

[Abstract] Clinical Evaluation of a New Mechatronic Glove for Hand Rehabilitation

Abstract:

This paper presents the validation and the improvements of a glove-type mechatronic recovery system through clinical testing for patients with neurological disorders. This system, called MANUTEX, evaluates the flexion and extension of the hand, stimulates the increase of muscle strength, as well as the coordination of fine movements of the patient’s hand. Fine motor skills such as precision, dexterity and coordination of the hand are improved too. The use of an exoskeleton type system for passive mobilization and functional electrical stimulation (FES), with surface electrodes, improves the remaining voluntary movement the patients may perform. A comprehensive testing of the MANUTEX functionality was carried out in a technical laboratory of the technical university, as a preamble for a first set of clinical trials at the Neurology Department of the Clinical Rehabilitation Hospital of Iasi. The purpose of this paper is to present the overall validation process and clinical results of using a new mechatronic glove with in the rehabilitation process for post stroke neurological patients.

Published in: 2022 E-Health and Bioengineering Conference (EHB)

Source

, , , , , , , , , , , ,

Leave a comment

[Abstract] Mirror Therapy-Based Hand Rehabilitation

Abstract:

Our hands are critical, complex, and crucial human body parts with many functions. The loss of human hand function results in a severe compromise on the ability to feed and care for oneself and limits one’s work, social, and family life. Hands are often prone to injuries since it is a primary means of interacting with the world. Recovery of the normal functioning of the hand after stroke, hand injuries, and hand surgeries is only possible by proper and continuous rehabilitation. Usually, patients undergoing hand rehabilitation have to visit the rehabilitation center to attend their sessions regularly. And thus, transportation becomes a challenge. Also, if home exercises are given, the patients fear doing the exercises due to pain and lack of motivation. Another challenge is that the physiotherapist does not receive feedback on whether the patients’ given home exercises were correctly done. The proposed mirror therapy-based hand rehabilitation of fingers with a feedback mechanism consists of a transmitter and receiver exoskeleton. The designed transmitter hand exoskeleton initiates the hand movements, and these signals are picked up and transmitted to the receiver hand exoskeleton for the corresponding movement. In our proposed design, the focus is given to mobilizing the Metacarpophalangeal (MCP) and Proximal Interphalangeal (PIP) joints of all fingers. With the help of the proposed system, the patient can perform the prescribed exercises in their comfort at their homes without the help of a bystander. The proposed system is validated by recording Electromyography (EMG) signals and grip force measurements and comparing the values obtained for a normal hand and the hand with an exoskeleton.

Source

, , , , , , , , , , , , , , , , , ,

Leave a comment

[Abstract] Soft Exoskeleton for Hand Rehabilitation: An Overview

Abstract:

Robotics based rehabilitation have attracted much attention during the past two decades. Recent intensive research publications in the area indicates that soft exoskeleton is one of the promising technologies for robotic-based rehabilitation. This paper aims to review the up-to-date research work in soft exoskeletons material, manufacturing, sensing and control for hand rehabilitation. Applied materials, its preferred properties, and manufacturing technologies of soft exoskeletons are reviewed. Different position and force sensing technologies as well as recent control techniques applying bio-signals as control signals are reviewed. The major challenges, which are also recommendations for future research work, are highlighted.

Source

, , , , , , , , , , , ,

Leave a comment

[Abstract] Hybrid FES & Mechatronic Hand Control Method for Upper Limb Rehabilitation Systems

Abstract

This paper aims to describe a control concept algorithm developed as a new software for controlling both functional electrical stimulation (FES) and mechatronic components, inherited in an upper limb rehabilitation system for stroke people, but not limited to this specific affection. The main principle of the algorithm is to achieve a balanced control between FES and mechatronic exoskeleton, based on the input of the healthy hand via a sensory hand glove in order to help affected stroke patients to recover their affected hand mobility during the therapy.

Published in: 2022 E-Health and Bioengineering Conference (EHB)

Source

, , , , , , , , , , ,

Leave a comment