Rehabilitation training is the primary intervention to improve motor recovery after stroke, but a tool to measure functional training does not currently exist. To bridge this gap, we previously developed an approach to classify functional movement primitives using wearable sensors and a machine learning (ML) algorithm. We found that this approach had encouraging classification performance but had computational and practical limitations, such as training time, sensor cost, and magnetic drift. Here, we sought to refine this approach and determine the algorithm, sensor configurations, and data requirements needed to maximize computational and practical performance.
Motion data had been previously collected from 6 stroke patients wearing 11 inertial measurement units (IMUs) as they moved objects on a target array. To identify optimal ML performance, we evaluated 4 algorithms that are commonly used in activity recognition (linear discriminant analysis (LDA), naïve Bayes, support vector machine, and k-nearest neighbors). We compared their classification accuracy, computational complexity, and tuning requirements. To identify optimal sensor configuration, we progressively sampled fewer sensors and compared classification accuracy. To identify optimal data requirements, we compared accuracy using data from IMUs versus accelerometers.
We found that LDA had the highest classification accuracy (92%) of the algorithms tested. It also was the most pragmatic, with low training and testing times and modest tuning requirements. We found that 7 sensors on the paretic arm and back resulted in the best accuracy. Using this array, accelerometers had a lower accuracy (84%).
We refined strategies to accurately and pragmatically quantify functional movement primitives in stroke patients. We propose that this optimized ML-sensor approach could be a means to quantify training dose after stroke.
Current studies about motor imagery based rehabilitation training systems for stroke subjects lack an appropriate analytic method, which can achieve a considerable classification accuracy, at the same time detects gradual changes of imagery patterns during rehabilitation process and disinters potential mechanisms about motor function recovery. In this study, we propose an adaptive boosting algorithm based on the cortex plasticity and spectral band shifts. This approach models the usually predetermined spatial-spectral configurations in EEG study into variable preconditions, and introduces a ne…
New York University（纽约大学）；New York University School of Medicine（纽约大学医学院）；Courant Institute of Mathematical Sciences（库朗数学科学研究所）
Recovery after stroke is often incomplete, but rehabilitation training may potentiate recovery by engaging endogenous neuroplasticity. In preclinical models of stroke, high doses of rehabilitation training are required to restore functional movement to the affected limbs of animals. In humans, however, the necessary dose of training to potentiate recovery is not known. This ignorance stems from the lack of objective, pragmatic approaches for measuring training doses in rehabilitation activities. Here, to develop a measurement approach, we took the critical first step of automatically identifyi…
New York University Langone Health（纽约大学朗格尼健康中心）；New York University（纽约大学）；Columbia University（哥伦比亚大学）；Courant Institute of Mathematical Sciences, New York University（纽约大学库朗数学科学研究所）
Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetitions. The optimal training content and quantity are currently unknown because no practical tools exist to measure them. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately de…
Currently, strokes are the leading cause of adult disability in the United States. Traditional treatment and rehabilitation options such as physical therapy and tissue plasminogen activator are limited in their effectiveness and ability to restore mobility and function to the patient. As a result, there exists an opportunity to greatly improve the treatment for strokes. Machine learning, specifically techniques that utilize Brain-Computer Interfaces (BCIs) to help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic, show promising results whe…
Hainan Rural Credit Union（海南省农村信用社联合社）；University of Warwick（华威大学）；Nanjing Normal University（南京师范大学）；Newcastle University（纽卡斯尔大学）
Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we develop an automated system that can predict the assessment score in an objective manner. With wrist-worn sensors, accelerometer data is collected from 59 stroke surv…
Carnegie Mellon University（卡内基梅隆大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等理工学院）；Madeira Interactive Technology Institute, University of Madeira（马德拉大学马德拉交互技术研究所）
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e.g. stroke). During a physical therapy session, generating personalized feedback is critical to improve patient's engagement. However, prior work on socially assistive robotics for physical therapy has mainly utilized pre-defined corrective feedback even if patients have various physical and functional abilities. This paper presents an interactive approach of a socially assistive robot that can dynamically select kinematic feat…
STROKEVISION-BENCH: A Multimodal Video And 2D Pose Benchmark For Tracking Stroke Recovery
Center for Research in Computer Vision, University of Central Florida（计算机视觉研究中心，佛罗里达中央大学）；Mechanical and Aerospace Engineering, University of Central Florida（机械与航空航天工程，佛罗里达中央大学）
Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Universidad de Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）
Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient's functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognizing patients' movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particula…
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation
Hadley Research, LLC（哈德利研究有限责任公司）；Case Western Reserve University（凯斯西储大学）；Louis Stokes Cleveland Department of Veterans Affairs Medical Center（路易斯·斯托克斯克利夫兰退伍军人事务部医疗中心）
EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation
Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心机器人创新中心）；Institute of Medical Technology Systems, University of Duisburg-Essen（杜伊斯堡-埃森大学医学技术系统研究所）
Enhancing Robustness of Asynchronous EEG-Based Movement Prediction using Classifier Ensembles
Robotics Innovation Center（机器人创新中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Institute of Medical Technology Systems（医疗技术系统研究所）；University of Duisburg-Essen（杜伊斯堡- Essen大学）
