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

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Methods and good practice guidelines for human joint kinematics estimation through magnetic and inertial wearable sensors

According to the World Health Organization, the ability to move is recognized as a key factor for the human well-being. From the wearable Magnetic and Inertial Measurement Units (MIMUs) signals it is possible to extract several digital mobility outcomes including the joint kinematics. To this end, it is first required to estimate the orientation of the MIMUs by means of a sensor fusion algorithm (SFA). After that, the relative orientation is computed and then decomposed to obtain the joint angles. However, the MIMUs do not provide a direct output of the physical quantity of interest which can be only determined after an ad hoc processing of their signals. It follows that the joint angle accuracy mostly depends on multiple factors. The first one is the magnitude of the MIMU measurements errors and up to date there is still a lack of methods for their characterization. A second crucial factor is the choice of the SFA to use. Despite the abundance of formulations in the literature, no-well established conclusions about their accuracy have been reached yet. The last factor is the biomechanical model used to compute the joint angles. In this context, unconstrained methods offer a simple way to decompose the relative orientation using the Euler angles but suffer from the inherent issues related to the SFA. In contrast, constrained approaches aim at increasing the robustness of the estimates by adopting models in which an objective function is minimized through the definition of physiological constraints. This thesis proposed the methods to accurately estimate the human joint kinematics starting from the MIMU signals. Three main contributions were provided. The first consisted in the design of a comprehensive battery of tests to completely characterize the sources of errors affecting the quality of the measurements. These tests rely on simple hypotheses based on the sensor working principles and do not require expensive equipment. Nine parameters were defined to quantify the signal accuracy improvements (if any) of 24 MIMUs before and after the refinement of their calibration coefficients. The second contribution was focused on the SFAs. Ten among the most popular SFAs were compared under different experimental conditions including different MIMU models and rotation rate magnitudes. To perform a “fair” comparison it was necessary to set the optimal parameter values for each SFA. The most important finding was that all the errors fall within a range from 3.8 deg to 7.1 deg thus making it impossible to draw any conclusions about the best performing SFA since no statistically significant differences were found. In addition, the orientation accuracy was heavily influenced by the experimental variables. After that, a novel method was designed to estimate the suboptimal parameter values of a given SFA without relying on any orientation reference. The maximum difference between the errors obtained using optimal and suboptimal parameter values amounted to 3.7 deg and to 0.6 deg on average. The last contribution consisted in the design of an unconstrained and a constrained methods for estimating the joint kinematics without considering the magnetometer to avoid the ferromagnetic disturbances. The unconstrained method was employed in a telerehabilitation platform in which the joint angles were estimated in real time. Errors collected during the execution of a full-body protocol were lower than 5 deg (considered the acceptability threshold). However, this method may be inaccurate after few minutes since no solutions can be taken to mitigate the drift error. To overcome this limitation a constrained method was developed based on a robotic model of the upper limb to set appropriate constraints. Errors relative to a continuous robot motion for twenty minutes were lower than 3 deg at most suggesting the feasibility of employing these solutions in the rehabilitation programs to properly plan the treatment and to accurately evaluate the outcomes

Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine

Fifteen years of wireless sensors for balance assessment in neurological disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined

Design and test of an automated version of the modified Jebsen test of hand function using Microsoft Kinect

Abstract Background The present paper describes the design and evaluation of an automated version of the Modified Jebsen Test of Hand Function (MJT) based on the Microsoft Kinect sensor. Methods The MJT was administered twice to 11 chronic stroke subjects with varying degrees of hand function deficits. The test times of the MJT were evaluated manually by a therapist using a stopwatch, and automatically using the Microsoft Kinect sensor. The ground truth times were assessed based on inspection of the video-recordings. The agreement between the methods was evaluated along with the test-retest performance. Results The results from Bland-Altman analysis showed better agreement between the ground truth times and the automatic MJT time evaluations compared to the agreement between the ground truth times and the times estimated by the therapist. The results from the test-retest performance showed that the subjects significantly improved their performance in several subtests of the MJT, indicating a practice effect. Conclusions The results from the test showed that the Kinect can be used for automating the MJT