1
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Thomas J, Hall JB, Schauffler R, Guess TM. Objective Clinical Measurement Tools for Functional Evaluation of the Surgical Patient. J Knee Surg 2024; 37:577-585. [PMID: 37562433 DOI: 10.1055/s-0043-1772222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Following knee surgery, clinicians have traditionally used visually rated or time-based assessments of lower extremity movement quality to measure surgical outcomes, plan rehabilitation interventions, and measure success. These methods of assessment are prone to error and do not fully capture a patient's inefficient movement patterns post surgery. Further, currently available systems which objectively measure kinematics during these tasks are expensive and unidimensional. For these reasons, recent research has called for the development of objective and low-cost precision rehabilitation tools to improve clinical measurement of movement tasks. The purpose of this article is to highlight two such tools and their applications to knee surgery. The systems highlighted within this article are the Mizzou Point-of-Care Assessment System (MPASS) and the Mizzou Knee Arthrometer Testing System (MKATS). MPASS has demonstrated high levels of agreement with the gold-standard Vicon system in measuring kinematics during sit-to-stand (R > 0.71), lateral step-down (intraclass correlation coefficient [ICC] > 0.55, apart from ankle flexion), and drop vertical jump tasks (ICC > 0.62), as well as gait (R > 0.87). MKATS has been used to quantify differences in tibiofemoral motion between groups during lateral step-down, step-up-and-over, and step-up/step-down tasks. Objective measurement of clinical tasks using portable and inexpensive instruments, such as the MPASS and MKATS, can help clinicians identify inefficient movement patterns and asymmetries which may damage and wear down supporting structures within the knee and throughout the kinetic chain causing pain and discomfort. Identifying these issues can help clinicians to plan interventions and measure their progress at a lower cost than currently available systems. The MPASS and MKATS are useful tools which have many applications to knee surgery.
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Affiliation(s)
- Jacob Thomas
- College of Health Sciences, University of Missouri, Columbia, Missouri
| | - Jamie B Hall
- Department of Physical Therapy, University of Missouri, Columbia, Missouri
| | - Rose Schauffler
- Department of Mechanical Engineering, University of Missouri, Columbia, Missouri
| | - Trent M Guess
- Department of Physical Therapy, University of Missouri, Columbia, Missouri
- Department of Orthopaedic Surgery, University of Missouri, Columbia, Missouri
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2
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Armstrong K, Zhang L, Wen Y, Willmott AP, Lee P, Ye X. A marker-less human motion analysis system for motion-based biomarker identification and quantification in knee disorders. Front Digit Health 2024; 6:1324511. [PMID: 38384738 PMCID: PMC10880093 DOI: 10.3389/fdgth.2024.1324511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/09/2024] [Indexed: 02/23/2024] Open
Abstract
In recent years the healthcare industry has had increased difficulty seeing all low-risk patients, including but not limited to suspected osteoarthritis (OA) patients. To help address the increased waiting lists and shortages of staff, we propose a novel method of automated biomarker identification and quantification for the monitoring of treatment or disease progression through the analysis of clinical motion data captured from a standard RGB video camera. The proposed method allows for the measurement of biomechanics information and analysis of their clinical significance, in both a cheap and sensitive alternative to the traditional motion capture techniques. These methods and results validate the capabilities of standard RGB cameras in clinical environments to capture clinically relevant motion data. Our method focuses on generating 3D human shape and pose from 2D video data via adversarial training in a deep neural network with a self-attention mechanism to encode both spatial and temporal information. Biomarker identification using Principal Component Analysis (PCA) allows the production of representative features from motion data and uses these to generate a clinical report automatically. These new biomarkers can then be used to assess the success of treatment and track the progress of rehabilitation or to monitor the progression of the disease. These methods have been validated with a small clinical study, by administering a local anaesthetic to a small population with knee pain, this allows these new representative biomarkers to be validated as statistically significant (p -value < 0.05 ). These significant biomarkers include the cumulative acceleration of elbow flexion/extension in a sit-to-stand, as well as the smoothness of the knee and elbow flexion/extension in both a squat and sit-to-stand.
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Affiliation(s)
- Kai Armstrong
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, United Kingdom
| | - Lei Zhang
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, United Kingdom
| | - Yan Wen
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, United Kingdom
| | - Alexander P. Willmott
- School of Sport and Exercise Science, University of Lincoln, Lincoln, United Kingdom
| | - Paul Lee
- School of Sport and Exercise Science, University of Lincoln, Lincoln, United Kingdom
- MSK Doctors, Sleaford, United Kingdom
| | - Xujiong Ye
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, United Kingdom
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3
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Brambilla C, Marani R, Romeo L, Lavit Nicora M, Storm FA, Reni G, Malosio M, D'Orazio T, Scano A. Azure Kinect performance evaluation for human motion and upper limb biomechanical analysis. Heliyon 2023; 9:e21606. [PMID: 38027881 PMCID: PMC10663858 DOI: 10.1016/j.heliyon.2023.e21606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/21/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Human motion tracking is a valuable task for many medical applications. Marker-based optoelectronic systems are considered the gold standard in human motion tracking. However, their use is not always feasible in clinics and industrial environments. On the other hand, marker-less sensors became valuable tools, as they are inexpensive, noninvasive and easy to use. However, their accuracy can depend on many factors including sensor positioning, light conditions and body occlusions. In this study, following previous works on the feasibility of marker-less systems for human motion monitoring, we investigate the performance of the Microsoft Azure Kinect sensor in computing kinematic and dynamic measurements of static postures and dynamic movements. According to our knowledge, it is the first time that this sensor is compared with a Vicon marker-based system to assess the best camera positioning while observing the upper body part movements of people performing several tasks. Twenty-five healthy volunteers were monitored to evaluate the effects of the several testing conditions, including the Azure Kinect positions, the light conditions, and lower limbs occlusions, on the tracking accuracy of kinematic, dynamic, and motor control parameters. From the statistical analysis of the performed measurements, the camera in the frontal position was the most reliable, the lighting conditions had almost no effects on the tracking accuracy, while the lower limbs occlusion worsened the accuracy of the upper limbs. The assessment of human static postures and dynamic movements based on experimental data proves the feasibility of applying the Azure Kinect to the biomechanical monitoring of human motion in several fields.
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Affiliation(s)
- Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Roberto Marani
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Laura Romeo
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
- Department of Electrical and Information Engineering (DEI), Polytechnic of Bari, Bari, Italy
| | - Matteo Lavit Nicora
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
- Industrial Engineering Department, University of Bologna, Bologna, Italy
| | - Fabio A. Storm
- Bioengineering Laboratory, Scientific Institute, IRCCS “Eugenio Medea”, 23842 Bosisio Parini, Lecco, Italy
| | - Gianluigi Reni
- Informatics Department, Autonomous Province of Bolzano, Bolzano, Italy
| | - Matteo Malosio
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Tiziana D'Orazio
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
| | - Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy
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Hii CST, Gan KB, Zainal N, Mohamed Ibrahim N, Azmin S, Mat Desa SH, van de Warrenburg B, You HW. Automated Gait Analysis Based on a Marker-Free Pose Estimation Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:6489. [PMID: 37514783 PMCID: PMC10384445 DOI: 10.3390/s23146489] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/30/2023]
Abstract
Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.
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Affiliation(s)
- Chang Soon Tony Hii
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Kok Beng Gan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Nasharuddin Zainal
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Norlinah Mohamed Ibrahim
- Neurology Unit, Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
| | - Shahrul Azmin
- Neurology Unit, Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
| | - Siti Hajar Mat Desa
- Neurology Unit, Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Nursing, Hospital Canselor Tuanku Muhriz, Kuala Lumpur 56000, Malaysia
| | - Bart van de Warrenburg
- Department of Neurology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Huay Woon You
- Pusat GENIUS@Pintar Negara, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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Hackbarth M, Koschate J, Lau S, Zieschang T. Depth-Imaging for Gait Analysis on a Treadmill in Older Adults at Risk of Falling. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:479-486. [PMID: 37817821 PMCID: PMC10561749 DOI: 10.1109/jtehm.2023.3277890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 04/05/2023] [Accepted: 05/11/2023] [Indexed: 10/12/2023]
Abstract
BACKGROUND Accidental falls are a major health issue in older people. One significant and potentially modifiable risk factor is reduced gait stability. Clinicians do not have sophisticated kinematic options to measure this risk factor with simple and affordable systems. Depth-imaging with AI-pose estimation can be used for gait analysis in young healthy adults. However, is it applicable for measuring gait in older adults at a risk of falling? METHODS In this methodological comparison 59 older adults with and without a history of falls walked on a treadmill while their gait pattern was recorded with multiple inertial measurement units and with an Azure Kinect depth-camera. Spatiotemporal gait parameters of both systems were compared for convergent validity and with a Bland-Altman plot. RESULTS Correlation between systems for stride length (r=.992, [Formula: see text]) and stride time (r=0.914, [Formula: see text]) was high. Bland-Altman plots revealed a moderate agreement in stride length (-0.74 ± 3.68 cm; [-7.96 cm to 6.47 cm]) and stride time (-3.7±54 ms; [-109 ms to 102 ms]). CONCLUSION Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect cameras. Affordable and small depth-cameras agree with IMUs for gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact. Clinical Translation Statement- Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect. Affordable and small depth-cameras, developed for various purposes in research and industry, agree with IMUs in clinical gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy to assess function or monitor changes in gait is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact.
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Affiliation(s)
- Michel Hackbarth
- School of Medicine and Health ScienceDepartment for Health Services Research, Geriatrics DivisionCarl von Ossietzky University Oldenburg26129OldenburgGermany
| | - Jessica Koschate
- School of Medicine and Health ScienceDepartment for Health Services Research, Geriatrics DivisionCarl von Ossietzky University Oldenburg26129OldenburgGermany
| | - Sandra Lau
- School of Medicine and Health ScienceDepartment for Health Services Research, Geriatrics DivisionCarl von Ossietzky University Oldenburg26129OldenburgGermany
| | - Tania Zieschang
- School of Medicine and Health ScienceDepartment for Health Services Research, Geriatrics DivisionCarl von Ossietzky University Oldenburg26129OldenburgGermany
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Büker L, Quinten V, Hackbarth M, Hellmers S, Diekmann R, Hein A. How the Processing Mode Influences Azure Kinect Body Tracking Results. SENSORS (BASEL, SWITZERLAND) 2023; 23:878. [PMID: 36679675 PMCID: PMC9860777 DOI: 10.3390/s23020878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.
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Affiliation(s)
- Linda Büker
- Assistance Systems and Medical Device Technology, Department for Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
| | - Vincent Quinten
- Assistance Systems and Medical Device Technology, Department for Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
| | - Michel Hackbarth
- Geriatric Medicine, Department for Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
| | - Sandra Hellmers
- Assistance Systems and Medical Device Technology, Department for Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
| | - Rebecca Diekmann
- Assistance Systems and Medical Device Technology, Department for Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
| | - Andreas Hein
- Assistance Systems and Medical Device Technology, Department for Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
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Jo S, Song S, Kim J, Song C. Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:9819. [PMID: 36560187 PMCID: PMC9785788 DOI: 10.3390/s22249819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/06/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
(1) Background: The present study investigated the agreement between the Azure Kinect and marker-based motion analysis during functional movements. (2) Methods: Twelve healthy adults participated in this study and performed a total of six different tasks including front view squat, side view squat, forward reach, lateral reach, front view lunge, and side view lunge. Movement data were collected using an Azure Kinect and 12 infrared cameras while the participants performed the movements. The comparability between marker-based motion analysis and Azure Kinect was visualized using Bland-Altman plots and scatter plots. (3) Results: During the front view of squat motions, hip and knee joint angles showed moderate and high level of concurrent validity, respectively. The side view of squat motions showed moderate to good in the visible hip joint angles, whereas hidden hip joint angle showed poor concurrent validity. The knee joint angles showed variation between excellent and moderate concurrent validity depending on the visibility. The forward reach motions showed moderate concurrent validity for both shoulder angles, whereas the lateral reach motions showed excellent concurrent validity. During the front view of lunge motions, both the hip and knee joint angles showed moderate concurrent validity. The side view of lunge motions showed variations in concurrent validity, while the right hip joint angle showed good concurrent validity; the left hip joint showed poor concurrent validity. (4) Conclusions: The overall agreement between the Azure Kinect and marker-based motion analysis system was moderate to good when the body segments were visible to the Azure Kinect, yet the accuracy of tracking hidden body parts is still a concern.
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Affiliation(s)
- Sungbae Jo
- Department of Physical Therapy, College of Health Science, Sahmyook University, Seoul 01795, Republic of Korea
| | - Sunmi Song
- Rehabilitation Science Program, Department of Health Science, Graduate School, Korea University, Seoul 02841, Republic of Korea
- Department of Health and Environmental Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea
- Department of Physical Therapy, College of Health Science, Korea University, Seoul 02841, Republic of Korea
| | - Junesun Kim
- Rehabilitation Science Program, Department of Health Science, Graduate School, Korea University, Seoul 02841, Republic of Korea
- Department of Health and Environmental Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea
- Department of Physical Therapy, College of Health Science, Korea University, Seoul 02841, Republic of Korea
- BK21FOUR Program: Learning Health Systems, College of Health Science, Korea University, Seoul 02841, Republic of Korea
| | - Changho Song
- Department of Physical Therapy, College of Health Science, Sahmyook University, Seoul 01795, Republic of Korea
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Ferraris C, Amprimo G, Masi G, Vismara L, Cremascoli R, Sinagra S, Pettiti G, Mauro A, Priano L. Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson's Disease Using the Azure Kinect Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166282. [PMID: 36016043 PMCID: PMC9412494 DOI: 10.3390/s22166282] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/13/2022] [Accepted: 08/19/2022] [Indexed: 05/27/2023]
Abstract
Arm swinging is a typical feature of human walking: Continuous and rhythmic movement of the upper limbs is important to ensure postural stability and walking efficiency. However, several factors can interfere with arm swings, making walking more risky and unstable: These include aging, neurological diseases, hemiplegia, and other comorbidities that affect motor control and coordination. Objective assessment of arm swings during walking could play a role in preventing adverse consequences, allowing appropriate treatments and rehabilitation protocols to be activated for recovery and improvement. This paper presents a system for gait analysis based on Microsoft Azure Kinect DK sensor and its body-tracking algorithm: It allows noninvasive full-body tracking, thus enabling simultaneous analysis of different aspects of walking, including arm swing characteristics. Sixteen subjects with Parkinson's disease and 13 healthy controls were recruited with the aim of evaluating differences in arm swing features and correlating them with traditional gait parameters. Preliminary results show significant differences between the two groups and a strong correlation between the parameters. The study thus highlights the ability of the proposed system to quantify arm swing features, thus offering a simple tool to provide a more comprehensive gait assessment.
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Affiliation(s)
- Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Gianluca Amprimo
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Giulia Masi
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Luca Vismara
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Riccardo Cremascoli
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Serena Sinagra
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Alessandro Mauro
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Lorenzo Priano
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
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Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review. SENSORS 2022; 22:s22134910. [PMID: 35808426 PMCID: PMC9269781 DOI: 10.3390/s22134910] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/23/2022] [Accepted: 06/27/2022] [Indexed: 12/25/2022]
Abstract
The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.
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