Measuring Physical Behavior after Stroke : Sedentary behavior, body postures & movements, and arm use

The primary aim of this thesis was to investigate two methodological aspects of measuring physical behavior from the perspective of stroke rehabilitation. The methodological aspects were: 1) the effect of applying different operationalizations of the construct to be measured, and 2) the validity of a measurement device. These aspects were investigated with respect to three components of physical behavior: sedentary behavior, body postures & movements, and arm use. Another aim was to apply physical behavior monitoring to describe daily-life arm use in people after stroke. It was found that different operationalizations of sedentary behavior had a clear effect on the outcomes related to the total amount of sedentary time and the way sedentary time accumulates in bouts, in healthy people and in people after stroke. In both groups, the differences were not only significant but also large enough to acknowledge differences between the different operationalizations. Next, we found that the Activ8 Physical Activity Monitor was sufficiently valid to detect body postures & movements in people after stroke. The Activ8 Arm Use was developed and proved to be sufficiently valid to measure arm use during lying/sitting and standing in people after stroke. Therefore, both these activity monitors can be used to measure components of physical behavior in stroke rehabilitation. The results of using the Activ8-AUM in people after stroke showed that, 3 weeks after the stroke, the arm use ratio was low, i.e. the arms were used in a non-symmetrical way and with low use of the affected arm. During the first 26 weeks after the stroke, although the arm use ratio increased it remained significantly lower than the ratio in healthy people, as reported by others. Moreover, both the arm use ratio and its increase showed considerable variability between participants. The arm use ratio seems to be non-linearly related with arm function, because the positive relation between arm use and arm function was more clearly observed at higher levels of arm function

Low-Cost Wearable Data Acquisition for Stroke Rehabilitation: A Proof-of-Concept Study on Accelerometry for Functional Task Assessment

Background: An increasingly aging society and consequently rising number of patients with poststroke-related neurological dysfunctions are forcing the rehabilitation field to adapt to ever-growing demands. Although clinical reasoning within rehabilitation is dependent on patient movement performance analysis, current strategies for monitoring rehabilitation progress are based on subjective time-consuming assessment scales, not often applied. Therefore, a need exists for efficient nonsubjective monitoring methods. Wearable monitoring devices are rapidly becoming a recognized option in rehabilitation for quantitative measures. Developments in sensors, embedded technology, and smart textile are driving rehabilitation to adopt an objective, seamless, efficient, and cost-effective delivery system. This study aims to assist physiotherapists’ clinical reasoning process through the incorporation of accelerometers as part of an electronic data acquisition system. Methods: A simple, low-cost, wearable device for poststroke rehabilitation progress monitoring was developed based on commercially available inertial sensors. Accelerometry data acquisition was performed for 4 first-time poststroke patients during a reach-press-return task. Results: Preliminary studies revealed acceleration profiles of stroke patients through which it is possible to quantitatively assess the functional movement, identify compensatory strategies, and help define proper movement. Conclusion: An inertial data acquisition system was designed and developed as a low-cost option for monitoring rehabilitation. The device seeks to ease the data-gathering process by physiotherapists to complement current practices with accelerometry profiles and aid the development of quantifiable methodologies and protocols.info:eu-repo/semantics/publishedVersio

How to gain evidence in neurorehabilitation: a personal view

Neurorehabilitation is an emerging field driven by developments in neuroscience and biomedical engineering. Most patients that require neurorehabilitation have had a stroke, but other diseases of the brain, spinal cord, or nerves can also be alleviated. Modern therapies in neurorehabilitation focus on reducing impairment and improving function in daily life. As compared with acute care medicine, the clinical evidence for most neurorehabilitative treatments (modern or conventional) is sparse. Clinical trials support constraint-induced movement therapy for the arm and aerobic treadmill training for walking, both high-intensity interventions requiring therapist time (i.e., cost) and patient motivation. Promising approaches for the future include robotic training, telerehabilitation at the patient's home, and supportive therapies that promote motivation and compliance. It is argued that a better understanding of the neuroscience of recovery together with results from small-scale and well-focused clinical experiments are necessary to design optimal interventions for specific target groups of patient

Developing Predictive Models for Upper Extremity Post–Stroke Motion Quality Estimation Using Decision Trees and Bagging Forest

Stroke is one of the leading causes of long–term disability. Approximately twothirds of stroke survivors require long-term rehabilitation, which suggests the importance of understanding the post-stroke recovery process during his activities of daily living. This problem is formulated as quantifying and estimating the poststroke movement quality in real world settings. To address this need, we have developed an approach that quantifies physical activities and can evaluate the performance quality. Wearable accelerometer and gyroscope are used to measure the upper extremity motions and to develop a mathematical framework to objectively relates sensors’ data to clinical performance indices. In this article we employ two machine learning classification methods, Bootstrap Aggregating (Bagging) Forest and Decision Tree (DT), to relate the post-stroke kinematic data to quality of the corresponding motion. We then compare the accuracy of the resulted two prediction models using cross-validation approaches. Our findings indicate that Bagging forest approach is superior to the computationally simpler DTs for unstable data sets including those derived from stroke survivors in this project

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

Sensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends

Background: As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field. Methods: A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis. Results: Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies. Conclusions: This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field