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Wearable Sensors and Systems for Rehabilitation

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 25133

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Special Issue Editors

Dr. Seungmin Rho

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Guest Editor

Department of Software, Sejong University, Seoul, Korea
Interests: Enterprise Information Systems, Multimedia Systems; information fusion; ACM Transactions on Embedded Computing; Journal of Real-Time Image Processing; Future Generation Computer System; Engineering Applications of Artificial Intelligence, New Review of Hypermedia and Multimedia; Multimedia Tools and Applications; Personal and Ubiquitous Computing; telecommunication systems; Ad Hoc & Sensor Wireless Networks and etc.

Dr. Naveen Chilamkurti

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Guest Editor

Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
Interests: network security; CPS; sensor network; IoT; AI-based information processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Neeraj Kumar

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Guest Editor

Computer Science and Engineering, Thapar Institute of Engineering and Technology, Deemed University, Patiala 147004, India
Interests: SDN; cyber physical systems; security; smart cities; deep learning; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With recent technological advances, engineering has played a crucial role in improving the quality of lives of persons with disabilities as well as that of healthcare service providers, developing therapeutic interventions, assistive technologies, and objective monitoring methods to measure outcomes in the field of physical rehabilitation. However, there are many open challenges and opportunities to integrate engineering concepts into rehabilitation, not only reducing healthcare costs, but also increasing population wellbeing and wealth. This motivates researchers to conduct studies, design and develop novel rehabilitative and assistive technologies, and investigate methods to help people to monitor, improve, and recover cognitive and motor functions. Specifically, the challenge is to transfer the research results and new knowledge to all stakeholders concerned, such as users and their caregivers, physicians, physiotherapists, as well as occupational therapists, hospitals, clinics, and industry, raising a general awareness of the importance of rehabilitation engineering.

The purpose of this Special Issue (SI) is to demonstrate high-quality research and contributions as well as reviews that deal with the opportunities and challenges regarding the application of wearable and sensor technologies for the prevention, detection, monitoring, and management of a diverse range of health issues, including neurological, musculoskeletal, and cardiovascular problems in a physical rehabilitation context.

Topics of interest include but are not limited to:

Wearable sensors;
Patient activity monitoring;
Reliability and validity of sensor-based measurements;
Sensor-based feedback on motor performance;
Sensor-based measurement of therapy adherence;
Sensor-based telerehabilitation;
Body sensors networks;
Smart clothing/textiles technologies for rehabilitation purposes;
Multimodal information fusion;
Intelligent signal processing;
Telerehabilitation and telemonitoring;
Deep learning and machine learning techniques;
Smart-phone applications for patient monitoring in rehabilitation context;
Pervasive and unobtrusive patient monitoring solutions;
Monitoring of physical condition of persons through lifespan;
The integration of multiple sensor information.

Dr. Seungmin Rho
Dr. Naveen Chilamkurti
Prof. Neeraj Kumar
Guest Editors

Manuscript Submission Information

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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.

Published Papers (5 papers)

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Research

19 pages, 1381 KiB

Open AccessArticle

Online Adaptive Prediction of Human Motion Intention Based on sEMG

by Zhen Ding, Chifu Yang, Zhipeng Wang, Xunfeng Yin and Feng Jiang

Sensors 2021, 21(8), 2882; https://doi.org/10.3390/s21082882 - 20 Apr 2021

Cited by 15 | Viewed by 2989

Abstract

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact

236 \pm 9

ms in advance. Meanwhile, the proposed feature extraction method can achieve

90.71 \pm 3.42 %

accuracy of sEMG reconstruction and can guarantee

73.70 \pm 5.01 %

accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to

87.65 \pm 3.83 %

and decreases the angle prediction error from

4 . 03^{\circ}

2 . 36^{\circ}

. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)

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16 pages, 4090 KiB

Open AccessArticle

The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion

by Peter Beshara, Judy F. Chen, Andrew C. Read, Pierre Lagadec, Tian Wang and William Robert Walsh

Sensors 2020, 20(24), 7238; https://doi.org/10.3390/s20247238 - 17 Dec 2020

Cited by 17 | Viewed by 5748

Abstract

Background: Objective assessment of shoulder joint active range of motion (AROM) is critical to monitor patient progress after conservative or surgical intervention. Advancements in miniature devices have led researchers to validate inertial sensors to capture human movement. This study investigated the construct validity as well as intra- and inter-rater reliability of active shoulder mobility measurements using a coupled system of inertial sensors and the Microsoft Kinect (HumanTrak). Methods: 50 healthy participants with no history of shoulder pathology were tested bilaterally for fixed and free ROM: (1) shoulder flexion, and (2) abduction using HumanTrak and goniometry. The repeat testing of the standardised protocol was completed after seven days by two physiotherapists. Results: All HumanTrak shoulder movements demonstrated adequate reliability (intra-class correlation (ICC) ≥ 0.70). HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.93 and 0.85) than goniometry (ICCs: 0.75 and 0.53) for measuring free shoulder flexion and abduction AROM, respectively. Similarly, HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.81 and 0.94) than goniometry (ICCs: 0.70 and 0.93) for fixed flexion and abduction AROM, respectively. Construct validity between HumanTrak and goniometry was adequate except for free abduction. The differences between raters were predominately acceptable and below ±10°. Conclusions: These results indicated that the HumanTrak system is an objective, valid and reliable way to assess and track shoulder ROM. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)

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17 pages, 3410 KiB

Open AccessArticle

Subtask Segmentation of Timed Up and Go Test for Mobility Assessment of Perioperative Total Knee Arthroplasty

by Chia-Yeh Hsieh, Hsiang-Yun Huang, Kai-Chun Liu, Kun-Hui Chen, Steen Jun-Ping Hsu and Chia-Tai Chan

Sensors 2020, 20(21), 6302; https://doi.org/10.3390/s20216302 - 05 Nov 2020

Cited by 16 | Viewed by 6230

Abstract

Total knee arthroplasty (TKA) is one of the most common treatments for people with severe knee osteoarthritis (OA). The accuracy of outcome measurements and quantitative assessments for perioperative TKA is an important issue in clinical practice. Timed up and go (TUG) tests have been validated to measure basic mobility and balance capabilities. A TUG test contains a series of subtasks, including sit-to-stand, walking-out, turning, walking-in, turning around, and stand-to-sit tasks. Detailed information about subtasks is essential to aid clinical professionals and physiotherapists in making assessment decisions. The main objective of this study is to design and develop a subtask segmentation approach using machine-learning models and knowledge-based postprocessing during the TUG test for perioperative TKA. The experiment recruited 26 patients with severe knee OA (11 patients with bilateral TKA planned and 15 patients with unilateral TKA planned). A series of signal-processing mechanisms and pattern recognition approaches involving machine learning-based multi-classifiers, fragmentation modification and subtask inference are designed and developed to tackle technical challenges in typical classification algorithms, including motion variability, fragmentation and ambiguity. The experimental results reveal that the accuracy of the proposed subtask segmentation approach using the AdaBoost technique with a window size of 128 samples is 92%, which is an improvement of at least 15% compared to that of the typical subtask segmentation approach using machine-learning models only. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)

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16 pages, 3877 KiB

Open AccessArticle

Quantitative Analysis of EEG Power Spectrum and EMG Median Power Frequency Changes after Continuous Passive Motion Mirror Therapy System

by Taewoong Park, Mina Lee, Taejong Jeong, Yong-Il Shin and Sung-Min Park

Sensors 2020, 20(8), 2354; https://doi.org/10.3390/s20082354 - 21 Apr 2020

Cited by 11 | Viewed by 4684

Abstract

Robotic mirror therapy (MT), which allows movement of the affected limb, is proposed as a more effective method than conventional MT (CMT). To improve the rehabilitation effectiveness of post-stroke patients, we developed a sensory stimulation-based continuous passive motion (CPM)-MT system with two different operating protocols, that is, asynchronous and synchronous modes. To evaluate their effectiveness, we measured brain activation through relative and absolute power spectral density (PSD) changes of electroencephalogram (EEG) mu rhythm in three cases with CMT and CPM-MT with asynchronous and synchronous modes. We also monitored changes in muscle fatigue, which is one of the negative effects of the CPM device, based on median power frequency (MPF) and root mean square (RMS). Relative PSD was most suppressed when subjects used the CPM-MT system under synchronous control: 22.11%, 15.31%, and 16.48% on Cz, C3, and C4, respectively. The absolute average changes in MPF and RMS were 1.59% and 9.78%, respectively, with CPM-MT. Synchronous mode CPM-MT is the most effective method for brain activation, and muscle fatigue caused by the CPM-MT system was negligible. This study suggests the more effective combination rehabilitation system for MT by utilizing CPM and magnetic-based MT task to add action execution and sensory stimulation compared with CMT. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)

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15 pages, 2237 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

by Jung-Yeon Kim, Geunsu Park, Seong-A Lee and Yunyoung Nam

Sensors 2020, 20(6), 1622; https://doi.org/10.3390/s20061622 - 14 Mar 2020

Cited by 40 | Viewed by 4691

Abstract

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms—including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons—were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)

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