9 research outputs found
Self-Aligning Finger Exoskeleton for the Mobilization of the Metacarpophalangeal Joint
In the context of hand and finger rehabilitation,
kinematic compatibility is key for the acceptability
and clinical exploitation of robotic devices. Different kinematic
chain solutions have been proposed in the state of
the art, with different trade-offs between characteristics
of kinematic compatibility, adaptability to different anthropometries,
and the ability to compute relevant clinical
information. This study presents the design of a novel
kinematic chain for the mobilization of the metacarpophalangeal
(MCP) joint of the long fingers and a mathematical
model for the real-time computation of the joint angle and
transferred torque. The proposed mechanism can self-align
with the human joint without hindering force transfer or
inducing parasitic torque. The chain has been designed
for integration into an exoskeletal device aimed at rehabilitating
traumatic-hand patients. The exoskeleton actuation
the unit has a series-elastic architecture for compliant human-robot
interaction and has been assembled and preliminarily
tested in experiments with eight human subjects. Performance
has been investigated in terms of (i) the accuracy of
the MCP joint angle estimation through comparison with
a video-based motion tracking system, (ii) residual MCP
torque when the exoskeleton is controlled to provide null
output impedance and (iii) torque-tracking performance.
Results showed a root-mean-square error (RMSE) below
5 degrees in the estimated MCP angle. The estimated residual
MCP torque resulted below 7 mNm. Torque tracking performance
shows an RMSE lower than 8 mNm in following
sinusoidal reference profiles. The results encourage further
investigations of the device in a clinical scenario
A Repertoire of Virtual-Reality, Occupational Therapy Exercises for Motor Rehabilitation Based on Action Observation
There is a growing interest in action observation treatment (AOT), i.e., a rehabilitative procedure combining action observation, motor imagery, and action execution to promote the recovery, maintenance, and acquisition of motor abilities. AOT studies employed basic upper limb gestures as stimuli, butâin principleâthe AOT approach can be effectively extended to more complex actions like occupational gestures. Here, we present a repertoire of virtual-reality (VR) stimuli depicting occupational therapy exercises intended for AOT, potentially suitable for occupational safety and injury prevention. We animated a humanoid avatar by fitting the kinematics recorded by a healthy subject performing the exercises. All the stimuli are available via a custom-made graphical user interface, which allows the user to adjust several visualization parameters like the viewpoint, the number of repetitions, and the observed movementâs speed. Beyond providing clinicians with a set of VR stimuli promoting via AOT the recovery of goal-oriented, occupational gestures, such a repertoire could extend the use of AOT to the field of occupational safety and injury prevention
A Gravity-Compensated Upper-Limb Exoskeleton for Functional Rehabilitation of the Shoulder Complex
In the last decade, several exoskeletons for shoulder rehabilitation have been presented in the literature. Most of these devices focus on the shoulder complex and limit the normal mobility of the rest of the body, forcing the patient into a fixed standing or sitting position. Nevertheless, this severely limits the range of activities that can potentially be simulated during the rehabilitation, preventing the execution of occupational therapy which involves the execution of tasks based on activities of daily living (ADLs). These tasks involve different muscular groups and whole-body movements, such as, e.g., picking up objects from the ground. To enable whole-body functional rehabilitation, the challenge is to shift the paradigm of robotic rehabilitation towards machines that can enable wide workspaces and high mobility. In this perspective, here we present Float: an upper-limb exoskeleton designed to promote and accelerate the motor and functional recovery of the shoulder joint complex following post-traumatic or post-surgical injuries. Indeed, Float allows the patient to move freely in a very large workspace. The key component that enables this is a passive polyarticulated arm which supports the total exoskeleton weight and allows the patient to move freely in space, empowering rehabilitation through a deeper interaction with the surrounding environment. A characterization of the reachable workspace of both the exoskeleton and the polyarticulated passive arm is presented. These results support the conclusion that a patient wearing Float can perform a wide variety of ADLs without bearing its weight
A Gravity-Compensated Upper-Limb Exoskeleton for Functional Rehabilitation of the Shoulder Complex
In the last decade, several exoskeletons for shoulder rehabilitation have been presented in the literature. Most of these devices focus on the shoulder complex and limit the normal mobility of the rest of the body, forcing the patient into a fixed standing or sitting position. Nevertheless, this severely limits the range of activities that can potentially be simulated during the rehabilitation, preventing the execution of occupational therapy which involves the execution of tasks based on activities of daily living (ADLs). These tasks involve different muscular groups and whole-body movements, such as, e.g., picking up objects from the ground. To enable whole-body functional rehabilitation, the challenge is to shift the paradigm of robotic rehabilitation towards machines that can enable wide workspaces and high mobility. In this perspective, here we present Float: an upper-limb exoskeleton designed to promote and accelerate the motor and functional recovery of the shoulder joint complex following post-traumatic or post-surgical injuries. Indeed, Float allows the patient to move freely in a very large workspace. The key component that enables this is a passive polyarticulated arm which supports the total exoskeleton weight and allows the patient to move freely in space, empowering rehabilitation through a deeper interaction with the surrounding environment. A characterization of the reachable workspace of both the exoskeleton and the polyarticulated passive arm is presented. These results support the conclusion that a patient wearing Float can perform a wide variety of ADLs without bearing its weight
Effects of robot-assisted gait training on postural instability in Parkinson's disease: a systematic review
INTRODUCTION: Postural instability is a cardinal feature of Parkinson's disease, together with rest tremor, rigidity and bradykinesia. It is a highly disabling symptom that becomes increasingly common with disease progression and represents a major source of reduced quality of life in patients with Parkinson's disease. Rehabilitation aims to enable patients with Parkinson's disease to maintain their maximum level of mobility, activity and independence. To date, a wide range of rehabilitation approaches has been employed to treat postural instability in Parkinson's disease, including robotic training. Our main aim was to conduct a systematic review of current literature about the effects of robot-assisted gait training on postural instability in patients with Parkinson's disease.EVIDENCE ACQUISITION: A systematic search using the following MeSH terms (Parkinson disease; postural balance; robotics; rehabilitation) and string {("robotics [mh]" OR "robot-assisted" OR "electromechanical") and ("rehabilitation [mh]" OR "training") and ("postural balance [mh]")} was conducted on PubMed, Cochrane Library and PEDro electronic databases. Full text articles in English published up to December 2020 were included. Data about patient characteristics, robotic devices, treatment procedures and outcome measures were considered. Every included article got checked for quality. Level of evidence was defined for all studies.EVIDENCE SYNTHESIS: Three authors independently extracted and verified data. In total, 18 articles (2 systematic reviews, 9 randomized controlled trials, 4 uncontrolled studies and 3 case series/case reports) were included. Both end-effector and exoskeleton devices were investigated as to robot-assisted gait training modalities. No clear relationship between treatment parameters and clinical conditions was observed. We found a high level of evidence about the effects of robot-assisted gait training on balance and freezing of gait in patients with Parkinson's disease.CONCLUSIONS: This systematic review provides to the reader a complete overview of current literature and levels of evidence about the effects of robot-assisted gait training on postural instability issues (static and dynamic balance, freezing of gait, falls, confidence in activities of daily living and gait parameters related to balance skills) in patients with Parkinson's disease
What is the impact of robotic rehabilitation on balance and gait outcomes in people with multiple sclerosis? A systematic review of randomized control trials
INTRODUCTION: In recent years, robot-assisted gait training (RAGT) has been proposed as therapy for balance and gait dysfunctions in people with multiple sclerosis (PwMS). Through this systematic review, we aimed to discuss the impact of RAGT on balance and gait outcomes. Furthermore, characteristics of the training in terms of robots used, participants characteristics, protocols and combined therapeutic approaches have been described.
EVIDENCE ACQUISITION: As part of the Italian Consensus on robotic rehabilitation âCICERONEâ a systematic search was provided in PubMed, the Cochrane Library and PEDro to identify relevant studies published before December 2019. Only randomized control trials (RCT) involving RAGT for PwMS were included. PEDro scale was used to assess the risk of bias and the Oxford Center for Evidence-Based Medicine (OCEBM) was used to assess level of evidence of included studies.
EVIDENCE SYNTHESIS: The search on databases resulted in 336 records and, finally, 12 studies were included. RAGT was provided with Exoskeleton in ten studies (6-40 session, 2-5 per week) and with end-effector in two studies (12 sessions, 2-3 per week) with large variability in terms of participantsâ disability. All the exoskeletons were combined with bodyweight support treadmill and movement assistance varied from 0% to 100% depending on participantsâ disability, two studies combined exoskeleton with virtual reality. The end-effector speed ranged between 1.3 and 1.8 km/h, with bodyweight support starting from 50% and progressively reduced. In seven out of twelve studies RAGT was provided in a multimodal rehabilitation program or in combination with standard physical therapy. There is level 2 evidence that RAGT has positive impact in PwMS, reaching the minimally clinically importance difference in Berg Balance Scale, six-minute walking test and gait speed.
CONCLUSIONS: In available RCT, RAGT is mostly provided with exoskeleton devices and improves balance and gait outcomes in a clinically meaningful way. Considering several advantages in terms of safety, motor assistance and intensity of training provided, RAGT should be promoted for PwMS with severe disability in a multimodal rehabilitation context as an opportunity to maximize recovery
Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis
Background: Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors. Methods: PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used. Results: A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of â0.62 (95%CI â 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (ÎČ = â1.019, 95% CI â 1.827; â0.210, p-value = 0.0135). Conclusions: RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy
Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis
Background: Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors. Methods: PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used. Results: A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of â0.62 (95%CI â 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (ÎČ = â1.019, 95% CI â 1.827; â0.210, p-value = 0.0135). Conclusions: RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy