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Wearable Systems in Physical Rehabilitation: Opportunities and Challenges

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 52023

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Special Issue Editors

Prof. Dr. Annick Timmermans

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Guest Editor

REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
Interests: rehabilitation; movement registration; feedback; virtual reality; serious gaming

Prof. Dr. Benedicte Vanwanseele

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Guest Editor

Department of Movement Sciences, KU Leuven, Leuven, Belgium
Interests: musculoskeletal loading during sport and exercise; monitoring of load and quality of movement

Dr. Liesbet De Baets

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Guest Editor

Hasselt University, REVAL - Rehabilitation Research Center, Hasselt, Belgium
Interests: musculoskeletal rehabilitation; movement registration;virtual reality

Special Issue Information

Dear Colleagues,

Thanks to recent technological advances, wearable technologies have opened up new exciting opportunities for rehabilitation. Wearable devices enable the continuous monitoring of posture, movement quantity and quality outside the movement laboratory, and the monitoring of vital signs which can be used as diagnostic tools for the evaluation of cardiorespiratory conditions. Such applications offer, on their own or in combined use, an interesting starting point for individualised rehabilitation interventions; as well as a monitoring tool for patient progress during rehabilitation. They can also enable augmented feedback during motor training or in combination with monitoring applications, offering a basis for blended care and telerehabilitation. They, however, also come with their own challenges, such as their reliability, validity and feasibility within clinical contexts and clinical populations, and the challenging interpretability of the data to effectively inform clinical practice.

This Special Issue aims to explore the opportunities and challenges regarding the application of sensor technologies for the detection, prevention and management of musculoskeletal, neurological and cardiovascular problems in a physical rehabilitation context.

Contributions that address but are not restricted to the following topics are welcome:

Wearable sensors;
Reliability and validity of sensor-based measurements;
Sensor-based feedback on motor performance;
Sensor-based measurement of therapy adherence;
Smart clothing/textiles technologies for rehabilitation purposes;
Smart-phone applications for patient monitoring in rehabilitaton context;
Patient activity monitoring;
Pervasive and unobstructive patient monitoring solutions;
Sensor-based telerehabilitation;
Monitoring of physical condition of persons through lifespan;
Body sensors networks;
The integration of multiple sensors information.

Submitted papers should present novel contributions. Relevant systematic and/or topical reviews are also welcome.

Prof. Dr. Annick Timmermans
Prof. Dr. Benedicte Vanwanseele
Dr. Liesbet De Baets
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

wearable sensors
rehabilitation
movement registration
feedback
monitoring
detection
prevention
cardiovascular
musculoskeletal
neurological

Published Papers (12 papers)

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Research

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22 pages, 11969 KiB

Open AccessArticle

Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App

by Jill Emmerzaal, Arne De Brabandere, Yves Vanrompay, Julie Vranken, Valerie Storms, Liesbet De Baets, Kristoff Corten, Jesse Davis, Ilse Jonkers, Benedicte Vanwanseele and Annick Timmermans

Sensors 2020, 20(23), 6967; https://doi.org/10.3390/s20236967 - 05 Dec 2020

Cited by 7 | Viewed by 2938

Abstract

(1) Background: Joint loading is an important parameter in patients with osteoarthritis (OA). However, calculating joint loading relies on the performance of an extensive biomechanical analysis, which is not possible to do in a free-living situation. We propose the concept and design of a novel blended-care app called JOLO (Joint Load) that combines free-living information on activity with lab-based measures of joint loading in order to estimate a subject’s functional status. (2) Method: We used an iterative design process to evaluate the usability of the JOLO app through questionnaires. The user interfaces that resulted from the iterations are described and provide a concept for feedback on functional status. (3) Results: In total, 44 people (20 people with OA and 24 health-care providers) participated in the testing of the JOLO app. OA patients rated the latest version of the JOLO app as moderately useful. Therapists were predominantly positive; however, their intention to use JOLO was low due to technological issues. (4) Conclusion: We can conclude that JOLO is promising, but further technological improvements concerning activity recognition, the development of personalized joint loading predictions and a more comfortable means to carry the device are needed to facilitate its integration as a blended-care program. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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15 pages, 4438 KiB

Open AccessArticle

Neckio: Motivating Neck Exercises in Computer Workers

by Panos Markopoulos, Xiaoyu Shen, Qi Wang and Annick Timmermans

Sensors 2020, 20(17), 4928; https://doi.org/10.3390/s20174928 - 31 Aug 2020

Cited by 5 | Viewed by 3223

Abstract

Neck pain is common among computer workers who may spend too much time in a static posture facing their display. Regular breaks and variety in one’s posture can help to prevent discomfort and pain. In order to understand how to support computer workers to do so regularly, we surveyed a convenience sample of computer workers (N = 130) regarding their work habits and their attitudes towards neck exercises at the workplace. The survey showed that they are highly motivated, but not able to comply with a neck exercise program. To address this challenge, we designed Neckio, a system that is aimed at encouraging posture variation and facilitating neck exercises at work. Neckio consists in an interactive application and a wireless angulation sensing appliance that can be mounted on the headset that office workers often use for reasons of privacy. Next to providing an interactive exercise program suitable for the workplace, its design places emphasis on an engaging user experience. We report a short-term user experience valuation of Neckio in an actual office environment (N = 10). Participants rated the overall user experience positively and reported to be intrinsically motivated to do the neck exercises. These results indicate the potential of the Neckio as a behavior change support technology to reduce the risk of developing neck pain in computer workers. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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20 pages, 6527 KiB

Open AccessArticle

Daily Life Benefits and Usage Characteristics of Dynamic Arm Supports in Subjects with Neuromuscular Disorders

by Johannes Essers, Alessio Murgia, Anneliek Peters and Kenneth Meijer

Sensors 2020, 20(17), 4864; https://doi.org/10.3390/s20174864 - 28 Aug 2020

Cited by 7 | Viewed by 2734

Abstract

Neuromuscular disorders cause progressive muscular weakness, which limits upper extremity mobility and performance during activities of daily life. Dynamic arm supports can improve mobility and quality of life. However, their use is often discontinued over time for unclear reasons. This study aimed to evaluate whether users of dynamic arm supports demonstrate and perceive quantifiable mobility benefits over a period of two months. Nine users of dynamic arm supports were included in this observational study. They had different neuromuscular disorders and collectively used four different arm supports. They were observed for three consecutive weeks during which they were equipped with a multi-sensor network of accelerometers to assess the actual use of the arm support and they were asked to provide self-reports on the perceived benefits of the devices. Benefits were experienced mainly during anti-gravity activities and the measured use did not change over time. The self-reports provided contextual information in domains such as participation to social life, in addition to the sensor system. However self-reports overestimated the actual use by up to three-fold compared to the accelerometer measures. A combination of objective and subjective methods is recommended for meaningful and quantifiable mobility benefits during activities of daily life. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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19 pages, 2845 KiB

Open AccessArticle

Smartphone App with an Accelerometer Enhances Patients’ Physical Activity Following Elective Orthopedic Surgery: A Pilot Study

by Hanneke C. van Dijk-Huisman, Anouk T.R. Weemaes, Tim A.E.J. Boymans, Antoine F. Lenssen and Rob A. de Bie

Sensors 2020, 20(15), 4317; https://doi.org/10.3390/s20154317 - 02 Aug 2020

Cited by 25 | Viewed by 6885

Abstract

Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective activity monitoring and provides patients with insights into their recovery progress and offers a tailored exercise program. The aim of this study was to investigate the potential of Hospital Fit to enhance PA levels and functional recovery following orthopedic surgery. PA was measured with an accelerometer postoperatively until discharge. The control group received standard physiotherapy, while the intervention group used Hospital Fit in addition to physiotherapy. The time spent active and functional recovery (modified Iowa Level of Assistance Scale) on postoperative day one (POD1) were measured. Ninety-seven patients undergoing total knee or hip arthroplasty were recruited. Hospital Fit use, corrected for age, resulted in patients standing and walking on POD1 for an average increase of 28.43 min (95% confidence interval (CI): 5.55–51.32). The odds of achieving functional recovery on POD1, corrected for the American Society of Anesthesiologists classification, were 3.08 times higher (95% CI: 1.14–8.31) with Hospital Fit use. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ PA levels and functional recovery during hospitalization. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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18 pages, 2825 KiB

Open AccessArticle

A Gesture-Controlled Rehabilitation Robot to Improve Engagement and Quantify Movement Performance

by Ava D. Segal, Mark C. Lesak, Anne K. Silverman and Andrew J. Petruska

Sensors 2020, 20(15), 4269; https://doi.org/10.3390/s20154269 - 31 Jul 2020

Cited by 3 | Viewed by 3729

Abstract

Rehabilitation requires repetitive and coordinated movements for effective treatment, which are contingent on patient compliance and motivation. However, the monotony, intensity, and expense of most therapy routines do not promote engagement. Gesture-controlled rehabilitation has the potential to quantify performance and provide engaging, cost-effective treatment, leading to better compliance and mobility. We present the design and testing of a gesture-controlled rehabilitation robot (GC-Rebot) to assess its potential for monitoring user performance and providing entertainment while conducting physical therapy. Healthy participants (n = 11) completed a maze with GC-Rebot for six trials. User performance was evaluated through quantitative metrics of movement quality and quantity, and participants rated the system usability with a validated survey. For participants with self-reported video-game experience (n = 10), wrist active range of motion across trials (mean ± standard deviation) was 41.6 ± 13° and 76.8 ± 16° for pitch and roll, respectively. In the course of conducting a single trial with a time duration of 68.3 ± 19 s, these participants performed 27 ± 8 full wrist motion repetitions (i.e., flexion/extension), with a dose-rate of 24.2 ± 5 ^reps/_min. These participants also rated system usability as excellent (score: 86.3 ± 12). Gesture-controlled therapy using the GC-Rebot demonstrated the potential to be an evidence-based rehabilitation tool based on excellent user ratings and the ability to monitor at-home compliance and performance. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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15 pages, 1409 KiB

Open AccessArticle

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation

by Hélène De Cannière, Federico Corradi, Christophe J. P. Smeets, Melanie Schoutteten, Carolina Varon, Chris Van Hoof, Sabine Van Huffel, Willemijn Groenendaal and Pieter Vandervoort

Sensors 2020, 20(12), 3601; https://doi.org/10.3390/s20123601 - 26 Jun 2020

Cited by 21 | Viewed by 4396

Abstract

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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15 pages, 4292 KiB

Open AccessArticle

Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People

by Fabien Buisseret, Louis Catinus, Rémi Grenard, Laurent Jojczyk, Dylan Fievez, Vincent Barvaux and Frédéric Dierick

Sensors 2020, 20(11), 3207; https://doi.org/10.3390/s20113207 - 05 Jun 2020

Cited by 42 | Viewed by 5245

Abstract

Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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17 pages, 1142 KiB

Open AccessArticle

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

by Salvatore Tedesco, Colum Crowe, Andrew Ryan, Marco Sica, Sebastian Scheurer, Amanda M. Clifford, Kenneth N. Brown and Brendan O’Flynn

Sensors 2020, 20(11), 3029; https://doi.org/10.3390/s20113029 - 27 May 2020

Cited by 22 | Viewed by 4179

Abstract

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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19 pages, 1179 KiB

Open AccessArticle

Concurrent Validity of a Novel Wireless Inertial Measurement System for Assessing Trunk Impairment in People with Stroke

by Norah Alhwoaimel, Martin Warner, Ann-Marie Hughes, Federico Ferrari, Jane Burridge, Seng Kwee Wee, Geert Verheyden and Ruth Turk

Sensors 2020, 20(6), 1699; https://doi.org/10.3390/s20061699 - 18 Mar 2020

Cited by 2 | Viewed by 3307

Abstract

Background: The Trunk Impairment Scale (TIS) is recommended for clinical research use to assess trunk impairment post-stroke. However, it is observer-dependent and neglects the quality of trunk movements. This study proposes an instrumented TIS (iTIS) using the Valedo system, comprising portable inertial sensors, as an objective measure of trunk impairment post-stroke. Objective: This study investigates the concurrent and discriminant ability of the iTIS in chronic stroke participants. Methods: Forty participants (20 with chronic stroke, 20 healthy, age-matched) were assessed using the TIS and iTIS simultaneously. A Spearman rank correlation coefficient was used to examine concurrent validity. A ROC curve was used to determine whether the iTIS could distinguish between stroke participants with and without trunk impairment. Results: A moderate relationship was found between the observed iTIS parameters and the clinical scores, supporting the concurrent validity of the iTIS. The small sample size meant definitive conclusions could not be drawn about the parameter differences between stroke groups (participants scoring zero and one on the clinical TIS) and the parameter cut-off points. Conclusions: The iTIS can detect small changes in trunk ROM that cannot be observed clinically. The iTIS has important implications for objective assessments of trunk impairment in clinical practice. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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17 pages, 3272 KiB

Open AccessArticle

m-SFT: A Novel Mobile Health System to Assess the Elderly Physical Condition

by Raquel Ureña, Francisco Chiclana, Alvaro Gonzalez-Alvarez, Enrique Herrera-Viedma and Jose A. Moral-Munoz

Sensors 2020, 20(5), 1462; https://doi.org/10.3390/s20051462 - 06 Mar 2020

Cited by 9 | Viewed by 5087

Abstract

The development of innovative solutions that allow the aging population to remain healthier and independent longer is essential to alleviate the burden that this increasing segment of the population supposes for the long term sustainability of the public health systems. It has been claimed that promoting physical activity could prevent functional decline. However, given the vulnerability of this population, the activity prescription requires to be tailored to the individual’s physical condition. We propose mobile Senior Fitness Test (m-SFT), a novel m-health system, that allows the health practitioner to determine the elderly physical condition by implementing a smartphone-based version of the senior fitness test (SFT). The technical reliability of m-SFT has been tested by carrying out a comparative study in seven volunteers (53–61 years) between the original SFT and the proposed m-health system obtaining high agreement (intra-class correlation coefficient (ICC) between 0.93 and 0.99). The system usability has been evaluated by 34 independent health experts (mean = 36.64 years; standard deviation = 6.26 years) by means of the System Usability Scale (SUS) obtaining an average SUS score of 84.4 out of 100. Both results point out that m-SFT is a reliable and easy to use m-health system for the evaluation of the elderly physical condition, also useful in intervention programs to follow-up the patient’s evolution. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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16 pages, 647 KiB

Open AccessArticle

Assessment of Scapulothoracic, Glenohumeral, and Elbow Motion in Adhesive Capsulitis by Means of Inertial Sensor Technology: A Within-Session, Intra-Operator and Inter-Operator Reliability and Agreement Study

by Liesbet De Baets, Stefanie Vanbrabant, Carl Dierickx, Rob van der Straaten and Annick Timmermans

Sensors 2020, 20(3), 876; https://doi.org/10.3390/s20030876 - 06 Feb 2020

Cited by 11 | Viewed by 3173

Abstract

Adhesive capsulitis (AC) is a glenohumeral (GH) joint condition, characterized by decreased GH joint range of motion (ROM) and compensatory ROM in the elbow and scapulothoracic (ST) joint. To evaluate AC progression in clinical settings, objective movement analysis by available systems would be valuable. This study aimed to assess within-session and intra- and inter-operator reliability/agreement of such a motion capture system. The MVN-Awinda® system from Xsens Technologies (Enschede, The Netherlands) was used to assess ST, GH, and elbow ROM during four tasks (GH external rotation, combing hair, grasping a seatbelt, placing a cup on a shelf) in 10 AC patients (mean age = 54 (±6), 7 females), on two test occasions (accompanied by different operators on second occasion). Standard error of measurements (SEMs) were below 1.5° for ST pro-retraction and 4.6° for GH in-external rotation during GH external rotation; below 6.6° for ST tilt, 6.4° for GH flexion-extension, 7.1° for elbow flexion-extension during combing hair; below 4.4° for GH ab-adduction, 13° for GH in-external rotation, 6.8° for elbow flexion-extension during grasping the seatbelt; below 11° for all ST and GH joint rotations during placing a cup on a shelf. Therefore, to evaluate AC progression, inertial sensors systems can be applied during the execution of functional tasks. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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Review

Jump to: Research

20 pages, 903 KiB

Open AccessReview

Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review

by Louise Brennan, Enrique Dorronzoro Zubiete and Brian Caulfield

Sensors 2020, 20(1), 181; https://doi.org/10.3390/s20010181 - 28 Dec 2019

Cited by 23 | Viewed by 5881

Abstract

Digital biofeedback systems (DBSs) are used in physical rehabilitation to improve outcomes by engaging and educating patients and have the potential to support patients while doing targeted exercises during home rehabilitation. The components of feedback (mode, content, frequency and timing) can influence motor learning and engagement in various ways. The feedback design used in DBSs for targeted exercise home rehabilitation, as well as the evidence underpinning the feedback and how it is evaluated, is not clearly known. To explore these concepts, we conducted a scoping review where an electronic search of PUBMED, PEDro and ACM digital libraries was conducted from January 2000 to July 2019. The main inclusion criteria included DBSs for targeted exercises, in a home rehabilitation setting, which have been tested on a clinical population. Nineteen papers were reviewed, detailing thirteen different DBSs. Feedback was mainly visual, concurrent and descriptive, frequently providing knowledge of results. Three systems provided clear rationale for the use of feedback. Four studies conducted specific evaluations of the feedback, and seven studies evaluated feedback in a less detailed or indirect manner. Future studies should describe in detail the feedback design in DBSs and consider a robust evaluation of the feedback element of the intervention to determine its efficacy. Full article

(This article belongs to the Special Issue Wearable Systems in Physical Rehabilitation: Opportunities and Challenges)

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