Neural Interface-Based Motor Neuroprosthesis in Poststroke Upper Limb Neurorehabilitation: An Individual Patient Data Meta-analysis.

OBJECTIVE: To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation. DATA SOURCES: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed. STUDY SELECTION: Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed. DATA EXTRACTION: Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD. DATA SYNTHESIS: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs ≤4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively). CONCLUSION: Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.

Mobile Robotic Balance Assistant (MRBA): a gait assistive and fall intervention robot for daily living.

BACKGROUND: Aging degrades the balance and locomotion ability due to frailty and pathological conditions. This demands balance rehabilitation and assistive technologies that help the affected population to regain mobility, independence, and improve their quality of life. While many overground gait rehabilitation and assistive robots exist in the market, none are designed to be used at home or in community settings. METHODS: A device named Mobile Robotic Balance Assistant (MRBA) is developed to address this problem. MRBA is a hybrid of a gait assistive robot and a powered wheelchair. When the user is walking around performing activities of daily living, the robot follows the person and provides support at the pelvic area in case of loss of balance. It can also be transformed into a wheelchair if the user wants to sit down or commute. To achieve instability detection, sensory data from the robot are compared with a predefined threshold; a fall is identified if the value exceeds the threshold. The experiments involve both healthy young subjects and an individual with spinal cord injury (SCI). Spatial Parametric Mapping is used to assess the effect of the robot on lower limb joint kinematics during walking. The instability detection algorithm is evaluated by calculating the sensitivity and specificity in identifying normal walking and simulated falls. RESULTS: When walking with MRBA, the healthy subjects have a lower speed, smaller step length and longer step time. The SCI subject experiences similar changes as well as a decrease in step width that indicates better stability. Both groups of subjects have reduced joint range of motion. By comparing the force sensor measurement with a calibrated threshold, the instability detection algorithm can identify more than 93% of self-induced falls with a false alarm rate of 0%. CONCLUSIONS: While there is still room for improvement in the robot compliance and the instability identification, the study demonstrates the first step in bringing gait assistive technologies into homes. We hope that the robot can encourage the balance-impaired population to engage in more activities of daily living to improve their quality of life. Future research includes recruiting more subjects with balance difficulty to further refine the device functionalities.

Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies.

BACKGROUND: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. METHODS: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. RESULTS: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. CONCLUSIONS: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.

Objective.Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. This study aims to assess the feasibility of applying MI-BCI models pre-trained using data from healthy individuals to detect MI in stroke patients.Approach.We introduce a new transfer learning approach where features from two-class MI data of healthy individuals are used to detect MI in stroke patients. We compare the results of the proposed method with those obtained from analyses within stroke data. Experiments were conducted using Deep ConvNet and state-of-the-art subject-specific machine learning MI classifiers, evaluated on OpenBMI two-class MI-EEG data from healthy subjects and two-class MI versus rest data from stroke patients.Main results.Results of our study indicate that through domain adaptation of a model pre-trained using healthy subjects' data, an average MI detection accuracy of 71.15% (±12.46%) can be achieved across 71 stroke patients. We demonstrate that the accuracy of the pre-trained model increased by 18.15% after transfer learning (p<0.001). Additionally, the proposed transfer learning method outperforms the subject-specific results achieved by Deep ConvNet and FBCSP, with significant enhancements of 7.64% (p<0.001) and 5.55% (p<0.001) in performance, respectively. Notably, the healthy-to-stroke transfer learning approach achieved similar performance to stroke-to-stroke transfer learning, with no significant difference (p>0.05). Explainable AI analyses using transfer models determined channel relevance patterns that indicate contributions from the bilateral motor, frontal, and parietal regions of the cortex towards MI detection in stroke patients.Significance.Transfer learning from healthy to stroke can enhance the clinical use of BCI algorithms by overcoming the challenge of insufficient clinical data for optimal training.

Self-Administered Botulinum Injection Causing Rare Masquerade of Systemic Botulism.

This report describes the case of a patient who presented to the Emergency Department (ED) with a one-week history of difficulty in breathing, generalized weakness, dysphagia, and difficulty in walking. She had self-administered 100 units of onabotulinumtoxin A (BoNT-A) by injection into her face two weeks prior for cosmetic purposes. This case study highlights the rare but potential complication of systemic botulism.

Unraveling stroke gait deviations with movement analytics, more than meets the eye: a case control study.

Background: This study aimed to identify and quantify the kinematic and kinetic gait deviations in post-stroke hemiplegic patients with matched healthy controls using Statistical Parametric Mapping (SPM). Methods: Fifteen chronic stroke patients [4 females, 11 males; age 53.7 (standard deviation 12.2) years; body mass 65.4 (10.4) kg; standing height 168.5 (9.6) cm] and 15 matched healthy controls [4 females, 11 males; age 52.9 (11.7) years; body weight 66.5 (10.7) years; standing height 168.3 (8.8) cm] were recruited. In a 10-m walking task, joint angles, ground reaction forces (GRF), and joint moments were collected, analyzed, and compared using SPM for an entire gait cycle. Results: Generally, when comparing the stroke patients' affected (hemiplegic) and less-affected (contralateral) limbs with the control group, SPM identified significant differences in the late stance phase and early swing phase in the joint angles and moments in bilateral limbs (all p < 0.005). In addition, the vertical and anteroposterior components of GRF were significantly different in various periods of the stance phase (all p < 0.005), while the mediolateral component showed no differences between the two groups. Conclusion: SPM was able to detect abnormal gait patterns in both the affected and less-affected limbs of stroke patients with significant differences when compared with matched controls. The findings draw attention to significant quantifiable gait deviations in the less-affected post-stroke limb with the potential impact to inform gait retraining strategies for clinicians and physiotherapists.

Characteristics and Functional Impact of Unplanned Acute Care Unit Readmissions during Inpatient Traumatic Brain Injury Rehabilitation: A Retrospective Cohort Study.

BACKGROUND: This study investigated the incidence, characteristics and functional outcomes associated with unplanned Acute Care Unit Readmissions (ACUR) during inpatient traumatic brain injury (TBI) rehabilitation in an Asian cohort. METHODS: A retrospective review of electronic medical records from a single rehabilitation unit was conducted from 1 January 2012 to 31 December 2014. Inclusion criteria were first TBI, aged >18 years, admitted <6 months of TBI. ACUR were characterized into neurological, medical or neurosurgical subtypes. The main outcome measure was discharge and Functional Independence Measure (FIM™). Secondary outcomes included rehabilitation length of stay (RLOS). RESULTS: Of 121 eligible TBI records, the incidence of ACUR was 14% (n = 17), comprising neurologic (76.5%) and medical (23.5%) subtypes occurring at median of 13 days (IQR 6, 28.5) after rehabilitation admission. Patients without ACUR had a significantly higher admission mean (SD) FIM score compared to those with ACUR (FIM ACUR-negative 63.4 (21.1) vs. FIM ACUR-positive 50.53(25.4), p = 0.026). Significantly lower discharge FIM was noted in those with ACUR compared to those without. (FIM ACUR-positive 65.8(31.4) vs. FIM ACUR-negative 85.4 (21.1), p = 0.023) Furthermore, a significant near-doubling of RLOS was noted in ACUR patients compared to non-ACUR counterparts (ACUR-positive median 55 days (IQR 34.50, 87.50) vs. ACUR-negative median 28 days (IQR 16.25, 40.00), p = 0.002). CONCLUSIONS: This study highlights the significant negative functional impact and lengthening of rehabilitation duration of ACUR on inpatient rehabilitation outcome for TBI.

Inpatient Rehabilitation Outcomes after Primary Severe Haemorrhagic Stroke: A Retrospective Study Comparing Surgical versus Non-Surgical Management.

BACKGROUND: Haemorrhagic stroke, accounting for 10-20% of all strokes, often requires decompressive surgery as a life-saving measure for cases with massive oedema and raised intracranial pressure. This study was conducted to compare the demographics, characteristics and rehabilitation profiles of patients with severe haemorrhagic stroke who were managed surgically versus those who were managed non-surgically. METHODS: A single-centre retrospective study of electronic medical records was conducted over a 3-year period from 1 January 2018 to 31 December 2020. The inclusion criteria were first haemorrhagic stroke, age of >18 years and an admission Functional Independence Measure (FIM™) score of 18-40 upon admission to the rehabilitation centre. The primary outcome measure was discharge FIM™. Secondary outcome measures included modified Rankin Scale (mRS), rehabilitation length of stay (RLOS) and complication rates. RESULTS: A total of 107 patients' records were analysed; 45 (42.1%) received surgical intervention and 62 (57.9%) patients underwent non-surgical management. Surgically managed patients were significantly younger than non-surgical patients, with a mean age of [surgical 53.1 (SD 12) vs. non-surgical 61.6 (SD 12.3), p = 0.001]. Admission FIM was significantly lower in the surgical vs. non-surgical group [23.7 (SD6.7) vs. 26.71 (SD 7.4), p = 0.031). However, discharge FIM was similar between both groups [surgical 53.91 (SD23.0) vs. non-surgical 57.0 (SD23.6), p = 0.625). Similarly, FIM gain (surgical 30.1 (SD 21.1) vs. non-surgical 30.3 (SD 21.1), p = 0.094) and RLOS [surgical 56.2 days (SD 21.5) vs. non-surgical 52.0 days (SD 23.4), p = 0.134) were not significantly different between groups. The majority of patients were discharged home (surgical 73.3% vs. non-surgical 74.2%, p = 0.920) despite a high level of dependency. CONCLUSIONS: Our findings suggest that patients with surgically managed haemorrhagic stroke, while older and more dependent on admission to rehabilitation, achieved comparable FIM gains, discharge FIM and discharge home rates after ~8 weeks of rehabilitation. This highlights the importance of rehabilitation, especially for surgically managed haemorrhagic stroke patients.

Changing Epidemiology and Functional Outcomes of Inpatient Rehabilitation in Asian Traumatic Brain Injury Cases before and during the COVID-19 Pandemic: A Retrospective Cohort Study.

BACKGROUND: This study aimed to compare acute injury and rehabilitation characteristics for traumatic brain injury (TBI) inpatients during the pre and post COVID-19 pandemic periods. METHODS: A retrospective study of TBI inpatients between 1 April 2018 and 31 December 2019 (pre COVID-19 period), and 1 July 2020 and 31 March 2022 (post COVID-19 period) was performed to compare demographics, premorbid comorbidity, TBI characteristics, rehabilitation complications, admission and discharge functional independence measure (FIM®), length of stay and discharge status. RESULTS: A total of 187 data sets were analyzed (82 pre COVID-19 and 105 post COVID-19). Post COVID-19 TBI inpatients were older by 11 years (pre COVID-19 mean 55 years vs. post COVID-19 mean 66 years, and p < 0.001), with 23% higher female inpatients (pre COVID-19 13.4% vs. post COVID-19 36.2%, and p < 0.001) and 25% higher presence of comorbidities (pre COVID-19 52.4% vs. post COVID-19 77.1%, and p < 0.001). In the post COVID-19 group, total discharge FIM (Td-FIM) was significantly lower by ~12 points (pre COVID-19 94.5 vs. post COVID-19 82, and p = 0.011), Td-FIM ≥ 91 was lower by ~18% (pre COVID-19 53.7% vs. post COVID-19 36.2%, and p = 0.017), and the need for caregivers increased by ~17% (pre COVID-19 68% vs. post COVID-19 85.4%, and p = 0.006) Conclusions: Our findings signal a demographic shift towards older, frailer TBI with lower functional independence levels post COVID-19.

Rehabilitation of Older Asian Traumatic Brain Injury Inpatients: A Retrospective Study Comparing Functional Independence between Age Groups.

Across traumatic brain injury (TBI) severities, a geriatric TBI tsunami has emerged. Mixed outcomes are reported for elderly TBI with positive functional improvements with acute inpatient rehabilitation. We studied the effect of age at TBI on discharge functional outcomes, levels of independence and length of stay. A retrospective analysis of Asian TBI patients during inpatient rehabilitation over a 4-year period was conducted. Independent variables included admission GCS, post-traumatic amnesia (PTA) duration and injury subtypes. Primary outcomes were discharge Functional Independence Measure (Td-FIM) and FIM gain. In total, 203 datasets were analysed; 60.1% (122) were aged ≥65 years (older), while 39.9% (81) were <65 years (younger). At discharge, older TBI had a significantly lower Td-FIM by 15 points compared to younger (older 90/126 vs. younger 105/126, p < 0.001). Median FIM gains (younger 27 vs. older 23, p = 0.83) and rehabilitation LOS (older 29.5 days vs. younger 27.5 days, p = 0.79) were similar for both age groups. Older TBIs had significantly lower independence (Td-FIM category ≥ 91) levels (49.4% older vs. 63.9% younger, p = 0.04), higher institutionalisation rates (23.5% older vs. 10.7% younger, p = 0.014) and need for carers (81.5% older vs. 66.4% younger, p = 0.019) on discharge. Although 77% of older TBI patients returned home, a significantly higher proportion needed care. This study supports the functional benefits of TBI rehabilitation in increasing independence regardless of age without incurring longer inpatient rehabilitation days.

Exploring the Feasibility of Computer Vision for Detecting Post-Stroke Compensatory Movements.

Compensatory movements are commonly observed post-stroke and can negatively affect long-term motor recovery. In this context, a system that monitors movement quality and provides feedback would be beneficial. In this study, we aimed to detect compensatory movements during seated reaching using a conventional tablet camera and an open-source markerless body pose tracking algorithm called MediaPipe [1]. We annotated compensatory movements of stroke patients per frame based on the comparison between the paretic and non-paretic arms. We trained a binary classification model using the XGBoost algorithm to detect compensatory movements, which showed an average accuracy of 0.92 (SD 0.07) in leave-one-trial-out cross-validation across four participants. Although we observed good model performance, we also encountered challenges such as missing landmarks and misalignment, when using MediaPipe Pose. This study highlights the feasibility of using near real-time compensatory movement detection with a simple camera system in stroke rehabilitation. More work is necessary to assess the generalizability of our approach across diverse groups of stroke survivors and fully implement near real-time compensatory movement detection on a mobile device.

Versatile clinical movement analysis using statistical parametric mapping in MovementRx.

Clinical gait analysis is an important biomechanics field that is often influenced by subjectivity in time-varying analysis leading to type I and II errors. Statistical Parametric Mapping can operate on all time-varying joint dynamics simultaneously, thereby overcoming subjectivity errors. We present MovementRx, the first gait analysis modelling application that correctly models the deviations of joints kinematics and kinetics both in 3 and 1 degrees of freedom; presented with easy-to-understand color maps for clinicians with limited statistical training. MovementRx is a python-based versatile GUI-enabled movement analysis decision support system, that provides a holistic view of all lower limb joints fundamental to the kinematic/kinetic chain related to functional gait. The user can cascade the view from single 3D multivariate result down to specific single joint individual 1D scalar movement component in a simple, coherent, objective, and visually intuitive manner. We highlight MovementRx benefit by presenting a case-study of a right knee osteoarthritis (OA) patient with otherwise undetected postintervention contralateral OA predisposition. MovementRx detected elevated frontal plane moments of the patient's unaffected knee. The patient also revealed a surprising adverse compensation to the contralateral limb.

Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform.

Background: Stroke is a leading cause of lifelong disability worldwide, partially driven by a reduced ability to use the upper limb in daily life causing increased dependence on caregivers. However, post-stroke functional impairments have only been investigated using limited clinical scores, during short-term longitudinal studies in relatively small patient cohorts. With the addition of technology-based assessments, we propose to complement clinical assessments with more sensitive and objective measures that could more holistically inform on upper limb impairment recovery after stroke, its impact on upper limb use in daily life, and on overall quality of life. This paper describes a pragmatic, longitudinal, observational study protocol aiming to gather a uniquely rich multimodal database to comprehensively describe the time course of upper limb recovery in a representative cohort of 400 Asian adults after stroke. Particularly, we will characterize the longitudinal relationship between upper limb recovery, common post-stroke impairments, functional independence and quality of life. Methods: Participants with stroke will be tested at up to eight time points, from within a month to 3 years post-stroke, to capture the influence of transitioning from hospital to community settings. We will perform a battery of established clinical assessments to describe the factors most likely to influence upper limb recovery. Further, we will gather digital health biomarkers from robotic or wearable sensing technology-assisted assessments to sensitively characterize motor and somatosensory impairments and upper limb use in daily life. We will also use both quantitative and qualitative measures to understand health-related quality of life. Lastly, we will describe neurophysiological motor status using transcranial magnetic stimulation. Statistics: Descriptive analyses will be first performed to understand post-stroke upper limb impairments and recovery at various time points. The relationships between digital biomarkers and various domains will be explored to inform key aspects of upper limb recovery and its dynamics using correlation matrices. Multiple statistical models will be constructed to characterize the time course of upper limb recovery post-stroke. Subgroups of stroke survivors exhibiting distinct recovery profiles will be identified. Conclusion: This is the first study complementing clinical assessments with technology-assisted digital biomarkers to investigate upper limb sensorimotor recovery in Asian stroke survivors. Overall, this study will yield a multimodal data set that longitudinally characterizes post-stroke upper limb recovery in functional impairments, daily-life upper limb use, and health-related quality of life in a large cohort of Asian stroke survivors. This data set generates valuable information on post-stroke upper limb recovery and potentially allows researchers to identify different recovery profiles of subgroups of Asian stroke survivors. This enables the comparisons between the characteristics and recovery profiles of stroke survivors in different regions. Thus, this study lays out the basis to identify early predictors for upper limb recovery, inform clinical decision-making in Asian stroke survivors and establish tailored therapy programs. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05322837.

Occurrence and temporal evolution of upper limb spasticity in stroke patients admitted to a rehabilitation unit.

OBJECTIVES: To document the temporal development and evolution of upper limb spasticity, and to establish clinical correlates and predictors of upper limb spasticity in a cohort of stroke patients. DESIGN: Prospective cohort study. SETTING: A rehabilitation unit. PARTICIPANTS: Patients (N=163) with a first-ever ischemic stroke. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Ashworth Scale for measuring upper limb spasticity, Motor Assessment Scale for upper limb activity, Motricity Index for upper limb strength, and Modified Barthel Index for self-care. Upper limb spasticity was defined as an Ashworth Scale score of 1 or greater. RESULTS: Upper limb spasticity occurred in 54 patients (33%) at 3 months after stroke. Development of spasticity at later stages of the stroke was infrequent, occurring in only 28 patients (17%). In patients with mild spasticity (Ashworth Scale score 1) at 3 months after stroke, worsening of spasticity occurred in only 1 patient. On the other hand, almost half of the patients with moderate spasticity (Ashworth Scale score 2) at 3 months progressed to severe spasticity (Ashworth Scale score 3). Poor upper limb activity was the most important correlate of "moderate to severe spasticity" (Ashworth Scale score ≥2) (P<.001), and poor upper limb strength on admission to rehabilitation, the most important predictor of "moderate to severe spasticity" (P<.001). CONCLUSIONS: Upper limb spasticity was relatively infrequent in this study, occurring in 33% of patients at 3 months after stroke. Selective monitoring to detect severe spasticity is recommended for patients with an Ashworth Scale score of 2 or greater at 3 months after stroke, and in patients with severe upper limb weakness on admission to rehabilitation.

Long-Term Outcomes of Patients with Primary Brain Tumors after Acute Rehabilitation: A Retrospective Analyses of Factors.

Although primary brain tumors are relatively rare, they cause significant morbidity and mortality due to the high rates of neurological impairment. The purpose of this study was to examine the physical and functional outcomes of patients with primary brain tumors who had undergone inpatient rehabilitation. This was a retrospective study which recruited 163 patients who had been admitted for inpatient rehabilitation. Rehabilitation outcomes, including the Functional Independence Measure (FIM) and Glasgow Outcome Scale (GOS), were recorded up to 1 year post-discharge. The majority of patients (79.1%) had low-grade (WHO Class I-II) tumors, 35 (21.5%) were diagnosed with GBM and 52 (31.9%) had recurrent brain tumors. Rehabilitation outcomes were sustained, with 125 (76.7%) and 113 (69.3%) patients having a GOS of ≥4 at 6 months and 1 year after discharge, respectively. A GOS of ≥4 at 1 year was negatively associated with high-grade tumors (p < 0.001) and radiotherapy (p = 0.028), and positively associated with a higher discharge FIM motor score (p < 0.001) and the presence of a caregiver after discharge (p = 0.034). Our study demonstrates significant positive functional benefits from 4 weeks of inpatient neuro-oncological rehabilitation for patients with primary brain tumors, as well as the importance of supportive care from caregivers.

The Impact of Stroke Subtype on Recovery and Functional Outcome after Inpatient Rehabilitation: A Retrospective Analysis of Factors.

The aims of this study were to compare inpatient rehabilitation outcomes between acute stroke subtypes of Cerebral Infarction (CI) and Intracerebral Hemorrhage (ICH), and to determine the predictors of discharge outcomes. A retrospective study of stroke inpatients was carried out using the discharge Functional Independence Measure (FIM) as the primary outcome measure. Relationships between stroke subtype, rehabilitation impairments, and medical complications on FIM -gain were analyzed. Altogether, 280 datasets including 211 (75.4%) CI and 69 (24.6%) ICH were analyzed. ICH patients were significantly younger than CI patients (55 years ICH vs. 64.0 years CI years, p < 0.001), had a 10-fold higher proportion needing ICU admission (ICH 82.6% vs. CI 7.6%, p < 0.001), and had significantly lower total admission FIM scores (67 points ICH vs. 74 CI points, p = 0.006), with lower motor-FIM scores in particular (38 points ICH vs. 48 points CI, p = 0.003). Significant functional improvements after inpatient rehabilitation, i.e., FIM gain, occurred regardless of stroke subtype (FIM-ICH Δ 27 vs. FIM-CI Δ 21, p = 0.05). Despite significantly worse initial stroke severity, ICH patients achieved similar functional gains, independence levels, and return-home rates compared with their CI counterparts after inpatient rehabilitation.

Soft, Lightweight Wearable Robots to Support the Upper Limb in Activities of Daily Living: A Feasibility Study on Chronic Stroke Patients.

Stroke can be a devastating condition that impairs the upper limb and reduces mobility. Wearable robots can aid impaired users by supporting performance of Activities of Daily Living (ADLs). In the past decade, soft devices have become popular due to their inherent malleable and low-weight properties that makes them generally safer and more ergonomic. In this study, we present an improved version of our previously developed gravity-compensating upper limb exosuit and introduce a novel hand exoskeleton. The latter uses 3D-printed structures that are attached to the back of the fingers which prevent undesired hyperextension of joints. We explored the feasibility of using this integrated system in a sample of 10 chronic stroke patients who performed 10 ADLs. We observed a significant reduction of 30.3 ± 3.5% (mean ± standard error), 31.2 ± 3.2% and 14.0 ± 5.1% in the mean muscular activity of the Biceps Brachii (BB), Anterior Deltoid (AD) and Extensor Digitorum Communis muscles, respectively. Additionally, we observed a reduction of 14.0 ± 11.5%, 14.7 ± 6.9% and 12.8 ± 4.4% in the coactivation of the pairs of muscles BB and Triceps Brachii (TB), BB and AD, and TB and Pectoralis Major (PM), respectively, typically associated to pathological muscular synergies, without significant degradation of healthy muscular coactivation. There was also a significant increase of elbow flexion angle ( 12.1±1.5° ). These results further cement the potential of using lightweight wearable devices to assist impaired users.

Identification of Secondary Biomechanical Abnormalities in the Lower Limb Joints after Chronic Transtibial Amputation: A Proof-of-Concept Study Using SPM1D Analysis.

SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using "Hotelling 2" and "T test 2" statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints × 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients' group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis.

Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study.

BACKGROUND: Rehabilitation of hand function is challenging, and only few studies have investigated robot-assisted rehabilitation focusing on distal joints of the upper limb. This paper investigates the feasibility of using the HapticKnob, a table-top end-effector device, for robot-assisted rehabilitation of grasping and forearm pronation/supination, two important functions for activities of daily living involving the hand, and which are often impaired in chronic stroke patients. It evaluates the effectiveness of this device for improving hand function and the transfer of improvement to arm function. METHODS: A single group of fifteen chronic stroke patients with impaired arm and hand functions (Fugl-Meyer motor assessment scale (FM) 10-45/66) participated in a 6-week 3-hours/week rehabilitation program with the HapticKnob. Outcome measures consisted primarily of the FM and Motricity Index (MI) and their respective subsections related to distal and proximal arm function, and were assessed at the beginning, end of treatment and in a 6-weeks follow-up. RESULTS: Thirteen subjects successfully completed robot-assisted therapy, with significantly improved hand and arm motor functions, demonstrated by an average 3.00 points increase on the FM and 4.55 on the MI at the completion of the therapy (4.85 FM and 6.84 MI six weeks post-therapy). Improvements were observed both in distal and proximal components of the clinical scales at the completion of the study (2.00 FM wrist/hand, 2.55 FM shoulder/elbow, 2.23 MI hand and 4.23 MI shoulder/elbow). In addition, improvements in hand function were observed, as measured by the Motor Assessment Scale, grip force, and a decrease in arm muscle spasticity. These results were confirmed by motion data collected by the robot. CONCLUSIONS: The results of this study show the feasibility of this robot-assisted therapy with patients presenting a large range of impairment levels. A significant homogeneous improvement in both hand and arm function was observed, which was maintained 6 weeks after end of the therapy.