Journal of NeuroEngineering and Rehabilitation最新文献

Introduction: There is currently a lack of easy-to-use and effective robotic devices for upper-limb rehabilitation after stroke. Importantly, most current systems lack the provision of somatosensory information that is congruent with the virtual training task. This paper introduces a novel haptic robotic system designed for upper-limb rehabilitation, focusing on enhancing sensorimotor rehabilitation through comprehensive haptic rendering.

Methods: We developed a novel haptic rehabilitation device with a unique combination of degrees of freedom that allows the virtual training of functional reach and grasp tasks, where we use a physics engine-based haptic rendering method to render whole-hand interactions between the patients' hands and virtual tangible objects. To evaluate the feasibility of our system, we performed a clinical mixed-method usability study with seven patients and seven therapists working in neurorehabilitation. We employed standardized questionnaires to gather quantitative data and performed semi-structured interviews with all participants to gain qualitative insights into the perceived usability and usefulness of our technological solution.

Results: The device demonstrated ease of use and adaptability to various hand sizes without extensive setup. Therapists and patients reported high satisfaction levels, with the system facilitating engaging and meaningful rehabilitation exercises. Participants provided notably positive feedback, particularly emphasizing the system's available degrees of freedom and its haptic rendering capabilities. Therapists expressed confidence in the transferability of sensorimotor skills learned with our system to activities of daily living, although further investigation is needed to confirm this.

Conclusion: The novel haptic robotic system effectively supports upper-limb rehabilitation post-stroke, offering high-fidelity haptic feedback and engaging training tasks. Its clinical usability, combined with positive feedback from both therapists and patients, underscores its potential to enhance robotic neurorehabilitation.

Background: The handstand is an essential skill in acrobatic sports. This skill requires the athlete to maintain an inverted upright stance with only the hands supported, which requires a great effort of muscular coordination and motor control. Several factors influence the ability to control the posture, including fatigue, which is a bit studied constraint of handstand performance.

Research question: With the aim to find out whether variability in movement control can be an indicator of fatigue, the present study was carried out.

Method: Fourteen male acrobatic gymnasts were required to perform handstands. The time series for analyzing variability were capturing using Force Platforms, which is a traditional laboratory instrument, and Inertial Measurement Units (IMU), which is a more recent and less widely used, but more accessible tool. For this purpose, an analysis of the amount of variability was carried out, using the standard deviation. And analysis of the structure of variability (or complexity), using Detrended Fluctuation Analysis (DFA) and Fuzzy Entropy (FuEn).

Results: Our results reveal that fatigue causes significant increases in the amount of variability in the medio-lateral axis on the force platform, and in the IMU located in the area of the L5 vertebra. These changes are accompanied by increased auto-correlation in the medio-lateral axis of the force platform, and more unpredictable behavior in the L5 IMU.

Background: Neck pain has a significant global impact, ranking as the fourth leading cause of disability. Recurrent neck pain often leads to impaired sensorimotor control, particularly in craniocervical flexion (CFF). The Craniocervical Flexion Test (CCFT) has been widely investigated to assess the performance of deep cervical flexor muscles. However, its use requires skilled assessors who need to subjectively detect compensations, progressive increases in range of motion (ROM) or excessive superficial flexor activation during the test. The aim of this study was to design and develop a novel Craniocervical Flexion Movement Control Test (CFMCT) based on inertial sensor technology and real-time computer feedback and to evaluate its safety and usability, as well as inter and intra-rater reliability in both healthy individuals and patients with neck pain.

Methods: We used inertial sensor technology associated with new software that provides real-time computer feedback to assess CCF movement control through two independent test protocols, the progressive consecutive stages protocol (including progressive incremental stages of ROM) and the random stages protocol (providing dynamic and less predictable movement patterns). We determined intra and inter-rater reliability and standard error of the measurement for both protocols. The participants rated their usability was analysed through the System Usability Scale (SUS) and possible secondary effects associated with the tests were registered.

Results: The progressive consecutive stages protocol and the random stages protocol were safe and easy to use (SUS scores of 82.00 ± 11.55 in the pain group and 79.56 ± 13.36 in the asymptomatic group). The progressive consecutive stages protocol demonstrated good inter-rater reliability (intraclass correlation coefficient [ICC] ≥ 0.75) and moderate to good intra-rater reliability (ICC 0.62-0.80). However, the random stages protocol exhibited lower intra-rater reliability, especially in the neck pain group, where the reliability values were poor in some cases (ICC 0.48-0.72).

Conclusion: The CFMCT (progressive consecutive stages protocol) is a promising instrument to evaluate CCF motor control in patients with chronic neck pain. It has potential for telehealth assessment and easy adherence for exercise prescription and seems to be a safe and usable tool for patients with pain and asymptomatic individuals. Its use as a test or for exercise needs to be further investigated to facilitate its transfer to clinical practice.

Background: Delivering HD-tDCS on individual motor hotspot with optimal electric fields could overcome challenges of stroke heterogeneity, potentially facilitating neural activation and improving motor function for stroke survivors. However, the intervention effect of this personalized HD-tDCS has not been explored on post-stroke motor recovery. In this study, we aim to evaluate whether targeting individual motor hotspot with HD-tDCS followed by EMG-driven robotic hand training could further facilitate the upper extremity motor function for chronic stroke survivors.

Methods: In this pilot randomized controlled trial, eighteen chronic stroke survivors were randomly allocated into two groups. The HDtDCS-group (n = 8) received personalized HD-tDCS using task-based fMRI to guide the stimulation on individual motor hotspot. The Sham-group (n = 10) received only sham stimulation. Both groups underwent 20 sessions of training, each session began with 20 min of HD-tDCS and was then followed by 60 min of robotic hand training. Clinical scales (Fugl-meyer Upper Extremity scale, FMAUE; Modified Ashworth Scale, MAS), and neuroimaging modalities (fMRI and EEG-EMG) were conducted before, after intervention, and at 6-month follow-up. Two-way repeated measures analysis of variance was used to compare the training effect between HDtDCS- and Sham-group.

Results: HDtDCS-group demonstrated significantly better motor improvement than the Sham-group in terms of greater changes of FMAUE scores (F = 6.5, P = 0.004) and MASf (F = 3.6, P = 0.038) immediately and 6 months after the 20-session intervention. The task-based fMRI activation significantly shifted to the ipsilesional motor area in the HDtDCS-group, and this activation pattern increasingly concentrated on the motor hotspot being stimulated 6 months after training within the HDtDCS-group, whereas the increased activation is not sustainable in the Sham-group. The neuroimaging results indicate that neural plastic changes of the HDtDCS-group were guided specifically and sustained as an add-on effect of the stimulation.

Conclusions: Stimulating the individual motor hotspot before robotic hand training could further enhance brain activation in motor-related regions that promote better motor recovery for chronic stroke.

Trial registration: This study was retrospectively registered in ClinicalTrials.gov (ID NCT05638464).

Introduction: Many stroke survivors do not receive optimal levels of personalised therapy to support their recovery. Use of technology for stroke rehabilitation has increased in recent years to help minimise gaps in service provision. Markerless motion capture technology is currently being used for musculoskeletal and occupational health screening and could offer a means to provide personalised guidance to stroke survivors struggling to access rehabilitation.

Aims: This study considered context, stakeholders, and key uncertainties surrounding the use of markerless motion capture technology in community stroke rehabilitation from the perspectives of stroke survivors and physiotherapists with a view to adapting an existing intervention in a new context.

Methods: Three focus groups were conducted with eight stroke survivors and five therapists. Data were analysed using reflexive thematic analysis.

Results: Five themes were identified: limited access to community care; personal motivation; pandemic changed rehabilitation practice; perceptions of technology; and role of markerless technology for providing feedback.

Conclusions: Participants identified problems associated with the access of community stroke rehabilitation, exacerbated by Covid-19 restrictions. Participants were positive about the potential for the use of markerless motion capture technology to support personalised, effective stroke rehabilitation in the future, providing it is developed to meet stroke survivor specific needs.