1
|
Ali MM, Medhat Hassan M, Zaki M. Human Pose Estimation for Clinical Analysis of Gait Pathologies. Bioinform Biol Insights 2024; 18:11779322241231108. [PMID: 38757143 PMCID: PMC11097739 DOI: 10.1177/11779322241231108] [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: 07/27/2023] [Accepted: 01/19/2024] [Indexed: 05/18/2024] Open
Abstract
Gait analysis serves as a critical diagnostic tool for identifying neurologic and musculoskeletal damage. Traditional manual analysis of motion data, however, is labor-intensive and heavily reliant on the expertise and judgment of the therapist. This study introduces a binary classification method for the quantitative assessment of gait impairments, specifically focusing on Duchenne muscular dystrophy (DMD), a prevalent and fatal neuromuscular genetic disorder. The research compares spatiotemporal and sagittal kinematic gait features derived from 2D and 3D human pose estimation trajectories against concurrently recorded 3D motion capture (MoCap) data from healthy children. The proposed model leverages a novel benchmark dataset, collected from YouTube and publicly available datasets of their typically developed peers, to extract time-distance variables (e.g. speed, step length, stride time, and cadence) and sagittal joint angles of the lower extremity (e.g. hip, knee, and knee flexion angles). Machine learning and deep learning techniques are employed to discern patterns that can identify children exhibiting DMD gait disturbances. While the current model is capable of distinguishing between healthy subjects and those with DMD, it does not specifically differentiate between DMD patients and patients with other gait impairments. Experimental results validate the efficacy of our cost-effective method, which relies on recorded RGB video, in detecting gait abnormalities, achieving a prediction accuracy of 96.2% for Support Vector Machine (SVM) and 97% for the deep network.
Collapse
Affiliation(s)
- Manal Mostafa Ali
- Department of Computer and System Engineering, Al-Azhar University, Cairo, Egypt
| | - Maha Medhat Hassan
- Department of Computer and System Engineering, Al-Azhar University, Cairo, Egypt
| | - M Zaki
- Department of Computer and System Engineering, Al-Azhar University, Cairo, Egypt
| |
Collapse
|
2
|
Alhazmi AK, Alanazi MA, Alshehry AH, Alshahry SM, Jaszek J, Djukic C, Brown A, Jackson K, Chodavarapu VP. Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine. SENSORS (BASEL, SWITZERLAND) 2024; 24:268. [PMID: 38203130 PMCID: PMC10781319 DOI: 10.3390/s24010268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.
Collapse
Affiliation(s)
- Abdullah K. Alhazmi
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| | - Mubarak A. Alanazi
- Electrical Engineering Department, Jubail Industrial College, Royal Commission for Jubail and Yanbu, Jubail Industrial City 31961, Saudi Arabia;
| | - Awwad H. Alshehry
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| | - Saleh M. Alshahry
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| | - Jennifer Jaszek
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Cameron Djukic
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Anna Brown
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Kurt Jackson
- Department of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (J.J.); (C.D.); (A.B.); (K.J.)
| | - Vamsy P. Chodavarapu
- Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA; (A.K.A.); (A.H.A.); (S.M.A.)
| |
Collapse
|
3
|
Dang X, Tang Y, Hao Z, Gao Y, Fan K, Wang Y. PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds. SENSORS (BASEL, SWITZERLAND) 2023; 24:142. [PMID: 38203004 PMCID: PMC10781080 DOI: 10.3390/s24010142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
Gait recognition, crucial in biometrics and behavioral analytics, has applications in human-computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.
Collapse
Affiliation(s)
- Xiaochao Dang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.T.); (Z.H.); (Y.G.); (K.F.); (Y.W.)
- Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China
| | - Yangyang Tang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.T.); (Z.H.); (Y.G.); (K.F.); (Y.W.)
| | - Zhanjun Hao
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.T.); (Z.H.); (Y.G.); (K.F.); (Y.W.)
- Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China
| | - Yifei Gao
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.T.); (Z.H.); (Y.G.); (K.F.); (Y.W.)
| | - Kai Fan
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.T.); (Z.H.); (Y.G.); (K.F.); (Y.W.)
| | - Yue Wang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.T.); (Z.H.); (Y.G.); (K.F.); (Y.W.)
| |
Collapse
|
4
|
Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
Collapse
Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
| |
Collapse
|
5
|
Inai T, Takabayashi T. Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity. PeerJ 2023; 11:e16131. [PMID: 37744216 PMCID: PMC10512936 DOI: 10.7717/peerj.16131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background and purpose Continuous monitoring of lower-limb movement may help in the early detection and control/reduction of diseases (such as the progression of orthopedic diseases) by applying suitable interventions. Therefore, it is invaluable to calculate the lower-limb movement (sagittal joint angles) while walking daily for continuous evaluation of such risks. Although cameras in a motion capture system are necessary for calculating lower-limb sagittal joint angles during gait, the method is unrealistic considering the setting is difficult to achieve in daily life. Therefore, the estimation of lower-limb sagittal joint angles during walking based on variables, which can be measured using wearable sensors (e.g., foot acceleration and angular velocity), is important. This study estimates the lower-limb sagittal joint angles during gait from the norms of foot acceleration and angular velocity using machine learning and validates the accuracy of the estimated joint angles with those obtained using a motion capture system. Methods Healthy adults (n = 200) were asked to walk at a comfortable speed (10 trials), and their lower-limb sagittal joint angles, foot accelerations, and angular velocities were obtained. Using these variables, we established a feedforward neural network and estimated the lower-limb sagittal joint angles. Results The average root mean squared errors of the lower-limb sagittal joint angles during gait ranged between 2.5°-7.0° (hip: 7.0°; knee: 4.0°; and ankle: 2.5°). Conclusion These results show that we can estimate the lower-limb sagittal joint angles during gait using only the norms of foot acceleration and angular velocity, which can help calculate the lower-limb sagittal joint angles during daily walking.
Collapse
Affiliation(s)
- Takuma Inai
- National Institute of Advanced Industrial Science and Technology, Takamatsu City, Japan
| | | |
Collapse
|
6
|
Zhang G, Li S, Zhang K, Lin YJ. Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar. SENSORS (BASEL, SWITZERLAND) 2023; 23:7208. [PMID: 37631744 PMCID: PMC10459214 DOI: 10.3390/s23167208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure-in addition to using the traditional index-the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier.
Collapse
Affiliation(s)
- Guangcheng Zhang
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (G.Z.); (S.L.); (K.Z.)
| | - Shenchen Li
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (G.Z.); (S.L.); (K.Z.)
| | - Kai Zhang
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (G.Z.); (S.L.); (K.Z.)
| | - Yueh-Jaw Lin
- College of Engineering and Engineering Technology, Northern Illinois University, DeKalb, IL 60115, USA
| |
Collapse
|
7
|
Zeng X, Báruson HSL, Sundvall A. Walking Step Monitoring with a Millimeter-Wave Radar in Real-Life Environment for Disease and Fall Prevention for the Elderly. SENSORS (BASEL, SWITZERLAND) 2022; 22:9901. [PMID: 36560270 PMCID: PMC9784666 DOI: 10.3390/s22249901] [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: 10/30/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
We studied the use of a millimeter-wave frequency-modulated continuous wave radar for gait analysis in a real-life environment, with a focus on the measurement of the step time. A method was developed for the successful extraction of gait patterns for different test cases. The quantitative investigation carried out in a lab corridor showed the excellent reliability of the proposed method for the step time measurement, with an average accuracy of 96%. In addition, a comparison test between the millimeter-wave radar and a continuous-wave radar working at 2.45 GHz was performed, and the results suggest that the millimeter-wave radar is more capable of capturing instantaneous gait features, which enables the timely detection of small gait changes appearing at the early stage of cognitive disorders.
Collapse
|