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Mangal NK, Tiwari AK. A review of the evolution of scientific literature on technology-assisted approaches using RGB-D sensors for musculoskeletal health monitoring. Comput Biol Med 2021; 132:104316. [PMID: 33721734 DOI: 10.1016/j.compbiomed.2021.104316] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/03/2021] [Accepted: 03/03/2021] [Indexed: 10/22/2022]
Abstract
The human musculoskeletal (MSK) system (also known as the locomotor system) provides strength and assistance to perform functional tasks and daily life activities. The MSK health monitoring plays a vital role in maintaining the body mobility and quality of life. Manual approaches for musculoskeletal health monitoring are subjective and require a clinician's intervention. The evolution in motion tracking technology enables us to capture the fine details of body movements. The research community has proposed various approaches to help clinicians in diagnosis and monitor treatment sessions. This paper succinctly reviews the evolution of technology-assisted approaches for musculoskeletal health monitoring, using motion capture sensors. To streamline the search through the literature database, the PICOS framework and PRISMA method have been incorporated. The present study reviews methods to transform motion capture data into kinematics variables and factors that affect the tracking performance of RGB-D sensors. Furthermore, widely utilized time-series filters for skeletal data denoising and smoothing for kinematics analysis, stochastic models for movement modeling, rule-based and template-based approaches for rehabilitation exercises assessment, and telerehabilitation sessions for remote health monitoring are explored. This article analyzes skeletal tracking methods by providing advantages and drawbacks of the state of the art rehabilitation sessions assessment, skeletal joint kinematics analysis, and MSK Telerehabilitation approaches. It also discusses the possible future research avenues to improve musculoskeletal disorder diagnosis and treatment monitoring. Our review signifies that RGB-D sensor-based approaches are inexpensive and portable for disorder diagnosis and treatment monitoring. It can also be a viable option for clinicians to provide contactless healthcare access to patients in the current scenario of the COVID-19 pandemic.
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Test-Retest, Inter-Rater and Intra-Rater Reliability for Spatiotemporal Gait Parameters Using SANE (an eaSy gAit aNalysis systEm) as Measuring Instrument. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175781] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Studies have demonstrated the validity of Kinect-based systems to measure spatiotemporal parameters of gait. However, few studies have addressed test-retest, inter-rater and intra-rater reliability for spatiotemporal gait parameters. This study aims to assess test-retest, inter-rater and intra-rater reliability of SANE (eaSy gAit aNalysis system) as a measuring instrument for spatiotemporal gait parameters. SANE comprises a depth sensor and a software that automatically estimates spatiotemporal gait parameters using distances between ankles without the need to manually indicate where each gait cycle begins and ends. Gait analysis was conducted by 2 evaluators for 12 healthy subjects during 4 sessions. The reliability was evaluated using Intraclass Correlation Coefficients (ICC). In addition, the Standard Error of the Measurement (SEM), and Smallest Detectable Change (SDC) was calculated. SANE showed from an acceptable to an excellent test-retest, inter-rater and intra-rater reliability; test-retest reliability ranged from 0.62 to 0.81, inter-rater reliability ranged from 0.70 to 0.95 and intra-rater ranged from 0.74 to 0.92. The subject behavior had a greater effect on the reliability of SANE than the evaluator performance. The reliability values of SANE were comparable with other similar studies. SANE, as a feasible and markerless system, has large potential for assessing spatiotemporal gait parameters.
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Khan MH, Zöller M, Farid MS, Grzegorzek M. Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3312. [PMID: 32532113 PMCID: PMC7313697 DOI: 10.3390/s20113312] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 12/20/2022]
Abstract
Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints' locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints' locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.
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Affiliation(s)
- Muhammad Hassan Khan
- Research Group for Pattern Recognition, University of Siegen, Hölderlinstr 3, 57076 Siegen, Germany;
- Punjab University College of Information Technology, University of the Punjab, Lahore 54000, Pakistan;
| | - Martin Zöller
- Research Group for Pattern Recognition, University of Siegen, Hölderlinstr 3, 57076 Siegen, Germany;
| | - Muhammad Shahid Farid
- Punjab University College of Information Technology, University of the Punjab, Lahore 54000, Pakistan;
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany;
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Liao Y, Vakanski A, Xian M, Paul D, Baker R. A review of computational approaches for evaluation of rehabilitation exercises. Comput Biol Med 2020; 119:103687. [PMID: 32339122 PMCID: PMC7189627 DOI: 10.1016/j.compbiomed.2020.103687] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 02/26/2020] [Accepted: 02/29/2020] [Indexed: 12/27/2022]
Abstract
Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches. The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.
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Affiliation(s)
- Yalin Liao
- Department of Computer Science, University of Idaho, Idaho Falls, USA
| | | | - Min Xian
- Department of Computer Science, University of Idaho, Idaho Falls, USA
| | - David Paul
- Department of Movement Sciences, University of Idaho, Moscow, USA
| | - Russell Baker
- Department of Movement Sciences, University of Idaho, Moscow, USA
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Ding WL, Zheng YZ, Su YP, Li XL. Kinect-based virtual rehabilitation and evaluation system for upper limb disorders: A case study. J Back Musculoskelet Rehabil 2018; 31:611-621. [PMID: 29578471 DOI: 10.3233/bmr-140203] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To help patients with disabilities of the arm and shoulder recover the accuracy and stability of movements, a novel and simple virtual rehabilitation and evaluation system called the Kine-VRES system was developed using Microsoft Kinect. METHODS First, several movements and virtual tasks were designed to increase the coordination, control and speed of the arm movements. The movements of the patients were then captured using the Kinect sensor, and kinematics-based interaction and real-time feedback were integrated into the system to enhance the motivation and self-confidence of the patient. Finally, a quantitative evaluation method of upper limb movements was provided using the recorded kinematics during hand-to-hand movement. RESULTS A preliminary study of this rehabilitation system indicates that the shoulder movements of two participants with ataxia became smoother after three weeks of training (one hour per day). CONCLUSION This case study demonstrated the effectiveness of the designed system, which could be promising for the rehabilitation of patients with upper limb disorders.
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Affiliation(s)
- W L Ding
- Laboratory of Pattern Recognition and Intelligent Systems, Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Department of Automation, Institute of Electrical Engineering, Yanshan University, Qinghuangdao, Hebei, China
| | - Y Z Zheng
- Laboratory of Pattern Recognition and Intelligent Systems, Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Department of Automation, Institute of Electrical Engineering, Yanshan University, Qinghuangdao, Hebei, China
| | - Y P Su
- Qinhuangdao First People's Hospital, Qinhuangdao, Hebei, China
| | - X L Li
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Khan MH, Schneider M, Farid MS, Grzegorzek M. Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model. SENSORS (BASEL, SWITZERLAND) 2018; 18:E3202. [PMID: 30248968 PMCID: PMC6210538 DOI: 10.3390/s18103202] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/17/2018] [Accepted: 09/20/2018] [Indexed: 12/20/2022]
Abstract
Movement analysis of infants' body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.
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Affiliation(s)
- Muhammad Hassan Khan
- Research Group for Pattern Recognition, University of Siegen, 57076 Siegen, Germany.
| | - Manuel Schneider
- Research Group for Pattern Recognition, University of Siegen, 57076 Siegen, Germany.
| | - Muhammad Shahid Farid
- College of Information Technology, University of the Punjab, 54000 Lahore, Pakistan.
| | - Marcin Grzegorzek
- Research Group for Pattern Recognition, University of Siegen, 57076 Siegen, Germany.
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Mateo F, Soria-Olivas E, Carrasco JJ, Bonanad S, Querol F, Pérez-Alenda S. HemoKinect: A Microsoft Kinect V2 Based Exergaming Software to Supervise Physical Exercise of Patients with Hemophilia. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2439. [PMID: 30050026 PMCID: PMC6111835 DOI: 10.3390/s18082439] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 07/06/2018] [Accepted: 07/24/2018] [Indexed: 12/19/2022]
Abstract
Patients with hemophilia need to strictly follow exercise routines to minimize their risk of suffering bleeding in joints, known as hemarthrosis. This paper introduces and validates a new exergaming software tool called HemoKinect that intends to keep track of exercises using Microsoft Kinect V2's body tracking capabilities. The software has been developed in C++ and MATLAB. The Kinect SDK V2.0 libraries have been used to obtain 3D joint positions from the Kinect color and depth sensors. Performing angle calculations and center-of-mass (COM) estimations using these joint positions, HemoKinect can evaluate the following exercises: elbow flexion/extension, knee flexion/extension (squat), step climb (ankle exercise) and multi-directional balance based on COM. The software generates reports and progress graphs and is able to directly send the results to the physician via email. Exercises have been validated with 10 controls and eight patients. HemoKinect successfully registered elbow and knee exercises, while displaying real-time joint angle measurements. Additionally, steps were successfully counted in up to 78% of the cases. Regarding balance, differences were found in the scores according to the difficulty level and direction. HemoKinect supposes a significant leap forward in terms of exergaming applicability to rehabilitation of patients with hemophilia, allowing remote supervision.
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Affiliation(s)
- Fernando Mateo
- Department of Electronic Engineering, University of Valencia, Avda. Universitat, 46100-Burjassot, Spain.
- Intelligent Data Analysis Laboratory, University of Valencia, Avda. Universitat, 46100-Burjassot, Spain.
| | - Emilio Soria-Olivas
- Department of Electronic Engineering, University of Valencia, Avda. Universitat, 46100-Burjassot, Spain.
- Intelligent Data Analysis Laboratory, University of Valencia, Avda. Universitat, 46100-Burjassot, Spain.
| | - Juan J Carrasco
- Intelligent Data Analysis Laboratory, University of Valencia, Avda. Universitat, 46100-Burjassot, Spain.
- Department of Physiotherapy, University of Valencia, Carrer de Gascó Oliag, 5, 46010-Valencia, Spain.
| | - Santiago Bonanad
- Haemostasis and Thrombosis Unit, University and Polytechnic Hospital La Fe, Avinguda de Fernando Abril Martorell, 106, 46026-Valencia, Spain.
| | - Felipe Querol
- Department of Physiotherapy, University of Valencia, Carrer de Gascó Oliag, 5, 46010-Valencia, Spain.
- Haemostasis and Thrombosis Unit, University and Polytechnic Hospital La Fe, Avinguda de Fernando Abril Martorell, 106, 46026-Valencia, Spain.
| | - Sofía Pérez-Alenda
- Department of Physiotherapy, University of Valencia, Carrer de Gascó Oliag, 5, 46010-Valencia, Spain.
- Haemostasis and Thrombosis Unit, University and Polytechnic Hospital La Fe, Avinguda de Fernando Abril Martorell, 106, 46026-Valencia, Spain.
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A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation. J Med Eng 2014; 2014:846514. [PMID: 27006935 PMCID: PMC4782741 DOI: 10.1155/2014/846514] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 11/03/2014] [Accepted: 11/17/2014] [Indexed: 01/12/2023] Open
Abstract
This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson's, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect's accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.
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