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Exoskeleton-Assisted Anthropomorphic Movement Training (EAMT) for Poststroke Upper Limb Rehabilitation: A Pilot Randomized Controlled Trial
Stroke is the leading cause of mortality and disability worldwide, and it places a substantial burden on health care services and the socioeconomic system.1 More than two-thirds of individuals with stroke have upper extremity motor impairment and functional deficits at hospital admission,2,3 manifesting as muscle weakness, loss of coordination, and abnormal synergies.4 Moreover, upper limb dysfunction leads to long-term limitations in activities of daily living (ADL) and social participation.5 Extensive studies have reported that participants can benefit from high-intensity, task-specific training programs based on motor-l...
Source: Archives of Physical Medicine and Rehabilitation - June 23, 2021 Category: Rehabilitation Authors: Ze-Jian Chen, Chang He, Feng Guo, Cai-Hua Xiong, Xiao-Lin Huang Tags: ORIGINAL RESEARCH Source Type: research

Invited Commentary on Comparison of Robotics, Functional Electrical Stimulation, and Motor Learning Methods for Treatment of Persistent Upper Extremity Dysfunction After Stroke: A Randomized Controlled Trial
In this issue of Archives of Physical Medicine and Rehabilitation, Jessica McCabe and colleagues report findings from their methodologically sound, dose-matched clinical trial in 39 patients beyond 6 months poststroke. In this phase II trial, the effects of 60 treatment sessions, each involving 3.5 hours of intensive practice plus either 1.5 hours of functional electrical stimulation (FES) or a shoulder-arm robotic therapy, were compared with 5 hours of intensive daily practice alone. Although no significant between-group differences were found on the primary outcome measure of Arm Motor Ability Test and the secondary outc...
Source: Archives of Physical Medicine and Rehabilitation - February 13, 2015 Category: Rehabilitation Authors: Gert Kwakkel, Erwin E. van Wegen, Carel M. Meskers Tags: INVITED Commentary Source Type: research

Editors' Selections From This Issue: Volume 96 / Number 6 / June 2015
In this month's podcast, we interview Janis J. Daly, PhD, MS, on the featured article Comparison of Robotics, Functional Electrical Stimulation, and Motor Learning Methods for Treatment of Persistent Upper Extremity Dysfunction After Stroke: A Randomized Controlled Trial by McCabe et al. (See the full article at page 981.) This podcast, and our full collection of author podcasts, is available at http://www.archives-pmr.org/content/podcast_collection.
Source: Archives of Physical Medicine and Rehabilitation - May 30, 2015 Category: Rehabilitation Source Type: research

Estimating Clinical Scores From Wearable Sensor Data In Stroke Survivors
To investigate the suitability of a machine learning algorithm based on data collected using two wearable 3-axis accelerometers to predict the total Functional Ability Scale (FAS) score during the performance of a battery of motor tasks taken from the Wolf Motor Function Test (WMFT).
Source: Archives of Physical Medicine and Rehabilitation - September 24, 2017 Category: Rehabilitation Authors: Claire Meagher, Stefano Sapienza, Catherine Adans-Dester, Anne O ’Brien, Shyamal Patel, Gloria Vergara-Diaz, Danilo Demarchi, Sunghoon Lee, Ann-Marie Hughes, Randie Black-Schaffer, Jane Burridge, Ross Zafonte, Paolo Bonato Source Type: research

Correction
In the article by McCabe et  al, Comparison of Robotics, Functional Electrical Stimulation, and Motor Learning Methods for Treatment of Persistent Upper Extremity Dysfunction After Stroke: A Randomized Controlled Trial, published in Archives of Physical Medicine and Rehabilitation 2015;96:981-90 (https://www.archives-pmr.org/ article/S0003-9993(14)01228-3/fulltext), Table 5 contained an error. In the last column (‘Mean Gain Score’), row one (ML Group, FM Score) the value is shown as 11 points on the FM scale.
Source: Archives of Physical Medicine and Rehabilitation - February 6, 2020 Category: Rehabilitation Tags: Departments Source Type: research

Measuring Activities of Daily Living in Stroke Patients with a Motion Machine Learning Algorithm
To investigate the feasibility to measure daily activities with wearable sensors using machine learning algorithms.
Source: Archives of Physical Medicine and Rehabilitation - March 31, 2021 Category: Rehabilitation Authors: Nathan Baune, Pin-Wei Chen, Igor Zwir, Alex W.K. Wong Tags: Late Breaking Research Papers Posters Source Type: research

Social Learning in a Virtual Environment After Stroke: A Thematic Analysis Of Stakeholder Experiences During The COVID-19 Pandemic
We explored stakeholders ’ experiences using videoconferencing to participate in group-based social learning during the COVID-19 pandemic.
Source: Archives of Physical Medicine and Rehabilitation - September 28, 2021 Category: Rehabilitation Authors: Emily Kringle, Elizabeth Skidmore, M. Carolyn Baum, Christine Rogers, Joy Hammel Tags: Research Poster 1710108 Source Type: research

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