Improved 3D Human Motion Capture Using Kinect Skeleton and Depth Sensor

Bilesan, Alireza; Komizunai, Shunsuke; Tsujita, Teppei; Konno, Atsushi

doi:10.20965/jrm.2021.p1408

Cited by 15 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, it could communicate with people through the Japanese language [10]. While the development of China's robotics field is relatively late, in 1986, China's 863 program was launched, and since then, China's robotics industry has entered a high-speed development stage [11]. National policy has provided a high degree of support for the mobile robot industry, significantly increasing researchers' enthusiasm.…”

Section: Mobile Robotsmentioning

confidence: 99%

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

Ning,

Huang

2024

Information

View full text Add to dashboard Cite

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose.

show abstract

Section: Mobile Robotsmentioning

confidence: 99%

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

Ning,

Huang

2024

Information

View full text Add to dashboard Cite

show abstract

“…Kinect is a portable device with an integrated depth sensor and a micro camera. Different from the traditional camera system, the depth camera technology obtains 3D information about subjects with the application of the stereo calibration method, which can produce RGB images and dynamically capture the movement of the human body for accurate analysis of human posture [31,32]. As Fig.…”

Section: Assessment and Protocolmentioning

confidence: 99%

Identifying Sensors-based Parameters Associated with Fall Risk in Community-Dwelling Older Adults: An Investigation and Interpretation of Discriminatory Parameters

Wang

Cao

Zhao

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for developing effective clinical screening tools for identifying high-fall-risk older adults.Methods Forty-one individuals aged 65 years and above living in the community participated in this study. The older adults were classified as high-fall-risk and low-fall-risk individuals based on their BBS scores. The participants wore an inertial measurement unit (IMU) while conducting the Timed Up and Go (TUG) test. Simultaneously, a depth camera acquired images of the participants' movements during the experiment. After segmenting the data according to subtasks, 142 parameters were extracted from the sensor-based data. A t-test or Mann-Whitney U test was performed on the parameters for distinguishing older adults at high risk of falling. Multiple logistic regression was used to further quantify the role of different parameters in identifying high-fall-risk individuals.Results Fifteen participants were defined as high-fall-risk individuals, while twenty-six were defined as low-fall-risk individuals. 17 parameters were tested for significance with p-values less than 0.05. Multiple logistic regressions were constructed for the parameters with the p-values less than 0.005, including the coefficient of variation of the angular velocity in pitch (CV-Ang-Pitch) during the turn, usage of walking assistance, and the max and coefficient of variation of the angular velocity in yaw (Max-Ang-Yaw, CV-Ang-Yaw) during turn-to-sit. The results showed that the CV-Ang-Pitch during the turn was the parameter with the greatest effect on identifying high-fall-risk individuals.Conclusions High-fall-risk individuals typically exhibited a cautious gait, such as larger step width and shorter step length during walking. Besides, we identified some abnormal gait patterns of high-fall-risk individuals compared to low-fall-risk individuals, such as greater knee angle and a tendency to tilt the pelvis forward during turning. These findings provide insight into the mechanisms underlying the activities of high-fall-risk individuals compared to normal individuals and illustrate the role of sensors in identifying high-fall-risk individuals among community-dwelling older adults. (350 words)

show abstract

“…Marker-based systems, despite their precision, require setup and could potentially hinder natural movement [ 14 , 15 ]. In addition, despite the evolution of computer vision algorithms [ 16 , 17 , 18 , 19 ], markerless systems often lack the precision required for detailed biomechanical analysis [ 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel IMU-Based System for Work-Related Musculoskeletal Disorders Risk Assessment

Baklouti,

Chaker,

Rezgui

et al. 2024

Sensors

View full text Add to dashboard Cite

This study introduces a novel wearable Inertial Measurement Unit (IMU)-based system for an objective and comprehensive assessment of Work-Related Musculoskeletal Disorders (WMSDs), thus enhancing workplace safety. The system integrates wearable technology with a user-friendly interface, providing magnetometer-free orientation estimation, joint angle measurements, and WMSDs risk evaluation. Tested in a cable manufacturing facility, the system was evaluated with ten female employees. The evaluation involved work cycle identification, inter-subject comparisons, and benchmarking against standard WMSD risk assessments like RULA, REBA, Strain Index, and Rodgers Muscle Fatigue Analysis. The evaluation demonstrated uniform joint patterns across participants (ICC=0.72±0.23) and revealed a higher occurrence of postures warranting further investigation, which is not easily detected by traditional methods such as RULA. The experimental results showed that the proposed system’s risk assessments closely aligned with the established methods and enabled detailed and targeted risk assessments, pinpointing specific bodily areas for immediate ergonomic interventions. This approach not only enhances the detection of ergonomic risks but also supports the development of personalized intervention strategies, addressing common workplace issues such as tendinitis, low back pain, and carpal tunnel syndrome. The outcomes highlight the system’s sensitivity and specificity in identifying ergonomic hazards. Future efforts should focus on broader validation and exploring the relative influence of various WMSDs risk factors to refine risk assessment and intervention strategies for improved applicability in occupational health.

show abstract

Improved 3D Human Motion Capture Using Kinect Skeleton and Depth Sensor

Cited by 15 publications

References 30 publications

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

Identifying Sensors-based Parameters Associated with Fall Risk in Community-Dwelling Older Adults: An Investigation and Interpretation of Discriminatory Parameters

A Novel IMU-Based System for Work-Related Musculoskeletal Disorders Risk Assessment

Contact Info

Product

Resources

About