2021
DOI: 10.3390/s21072340
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Abstract: Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detectio… Show more

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Cited by 16 publications
(4 citation statements)
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References 33 publications
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“…At present, researchers are attempting to design complex sensing environments that use multimodality, because a complex system can collect more accurate motion data [19]. We gather human motion data using a multimodal sensing system [20]. The system was composed of one Lidar sensor and ten IMU sensors.…”
Section: Related Work a Sensing System And Benchmark Data Setmentioning
confidence: 99%
“…At present, researchers are attempting to design complex sensing environments that use multimodality, because a complex system can collect more accurate motion data [19]. We gather human motion data using a multimodal sensing system [20]. The system was composed of one Lidar sensor and ten IMU sensors.…”
Section: Related Work a Sensing System And Benchmark Data Setmentioning
confidence: 99%
“…The user performing the boxing action was well within the LiDAR's field of view (1.5 m away from LiDAR system and within a 5 m sensing range), as shown in Figure 11. Furthermore, we estimate the body segment's orientation and joint position using an open-source framework [33]. In this comparative study, we used the estimated orientations and positions from LiDAR measurements and IMU sensors as the ground truth.…”
Section: A Experimental Setupmentioning
confidence: 99%
“…A vector-based joint position estimation framework [33] was used to evaluate the proposed system's position estimation accuracy. The fullbody skeleton joint positions tracked from the LiDAR's point cloud were fused with those estimated using the IMU sensors.…”
Section: ) Position Accuracy Comparisonmentioning
confidence: 99%
“…There is also research in the literature on the use of IMUs, kinematic models and vision techniques for human pose prediction. For instance, the authors in [3] used IMU data and a kinematic model to track the upper limbs in neurorehabilitation exercises; the work from [4] showed that fusing IMU and vision data outperforms IMU-only approaches; and multiple studies, such as [5] and [6], have developed 3-D pose estimation algorithms with IMU, LiDAR and multiple-camera data.…”
Section: Introductionmentioning
confidence: 99%