Estimation of Center of Mass for Sports Scene Using Weighted Visual Hull

Kaichi, Tomoya; Mori, Shohei; Saitō, Hideo; Takahashi, Kosuke; Mikami, Dan; Isogawa, Mariko; Kimata, Hideaki

doi:10.1109/cvprw.2018.00234

Cited by 8 publications

(12 citation statements)

References 19 publications

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“…This process is repeated for 10 times and the average results are presented. We compare the results of the following methods: the CNN-based model proposed in our preliminary work [19], the CNN + coarse-to-fine approach proposed in this paper, and two state-of-the-art methods: the SESC method [20] and the voxel reconstruction method [12]. Table 1 presents the estimation error and the requirements of each method.…”

Section: Com Estimation Resultsmentioning

confidence: 99%

“…In comparison, the proposed CNN-based approach is able to learn the subject's body parameters from the depth image without any identification process. Compared with the voxel reconstruction method [12], our proposed approach achieves comparable accuracy results, but requires only a single depth camera and avoids the complicated calibration and synchronization among multiple cameras. Therefore, it is more convenient for home and clinical use.…”

Section: Com Estimation Resultsmentioning

confidence: 99%

“…Kaichi et al proposed to reconstruct the 3D human body and then estimate the CoM position [12]. They used five cameras to capture multiple views of the human body and a 3D reconstruction approach to reconstruct the body.…”

Section: ) the Voxel Reconstruction Methodsmentioning

confidence: 99%

“…For the 3D position of the human's CoM, the horizontal CoM (i.e., the projection of CoM on the ground) is of greater importance [4], [5]. Since the CoM position of the human body cannot be directly measured, researchers have proposed to measure the Center of Pressure (CoP) of the ground reaction force in static/balanced postures to represent the horizontal CoM position [10]- [12], [20]. In a static/balanced posture (e.g., quiet standing), the only forces acting on the human body are the gravity (which acts on the CoM) and the ground reaction force.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, the body shape of the subject is either modeled as geometrical segments [9], [10] or estimated from an identification/calibration process [11], [20]. To achieve identification-free CoM estimation, Kaichi et al have proposed a voxel reconstruction approach to reconstruct the subject's 3D body using multiple cameras and estimate the CoM position by assigning weights to the body parts [12]. However, they need to carefully calibrate five cameras for the 3D body reconstruction, which makes it not suitable for home and clinical use.…”

Section: Introductionmentioning

confidence: 99%

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Using Sensors and Deep Learning to Enable On-Demand Balance Evaluation for Effective Physical Therapy

2020

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In traditional physical therapy, balance evaluation is performed by the physical therapist (PT) intermittently during clinic visits, which is subjective, inconvenient, and time-consuming. In this paper, we use sensors and deep learning to propose an automated balance evaluation system for home and clinical use. First, we propose a deep learning-based model to estimate the subject's Center of Mass (CoM) position using a depth camera, which outperforms other CoM estimation methods with high accuracy and ease of use. Then we propose a balance evaluation system to evaluate the subject's dynamic balance in a Gait Initiation (GI) task. The subject's CoM position is estimated by the proposed CoM estimation model and the Center of Pressure (CoP) position is measured by a Wii balance board. The CoP-CoM trajectory during the GI task is used to assess and quantify the patient's dynamic balance control. Using data collected from both healthy subjects and patients with Parkinson's Disease, the proposed balance evaluation model is able to quantify the subject's balance level which is consistent with the human PT's assessments in traditional balance evaluation tests. The proposed balance evaluation system can be used as a portable and low-cost tool for on-demand balance evaluation.

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Section: Com Estimation Resultsmentioning

confidence: 99%

Section: Com Estimation Resultsmentioning

confidence: 99%

Section: ) the Voxel Reconstruction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Sensors and Deep Learning to Enable On-Demand Balance Evaluation for Effective Physical Therapy

2020

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Deep neural network approach for estimating the three-dimensional human center of mass using joint angles

Chebel

Tunç

2021

Journal of Biomechanics

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Evaluation of center of mass estimation for obese using statically equivalent serial chain

Chebel

Tunç

2022

Sci Rep

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The complex structure of the human body makes its center of mass (CoM) estimation very challenging. The typically used estimation methods usually suffer from large estimation errors when applied to bodies with structural differences. Thus, a reliable estimation method is of utmost importance. In this paper, we present a detailed evaluation of a subject-specific CoM estimation technique named Statically Equivalent Serial Chain (SESC) by investigating its estimation ability over two different groups of subjects (Fit and Obese) in comparison to the segmental analysis method. For this study, we used an IMU-based motion capture system and a force platform to record the joint angles and corresponding center of pressure (CoP) values of twenty-five participants while performing a series of static postures. The root-mean-square errors (RMSE) of SESC’s estimation for both groups showed close and lower mean values, whereas the segmental analysis method showed significantly larger RMSE values in comparison to SESC (p < 0.05). In addition, we used the Bland–Altman analysis to evaluate the agreement between the two techniques and the ground truth CoP, which showed the accuracy, precision, and reliability of SESC over both groups. In contrast, the segmental analysis method did not present neither accurate nor precise estimations, as our analysis revealed considerable fixed and proportional biases.

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Estimation of Center of Mass for Sports Scene Using Weighted Visual Hull

Cited by 8 publications

References 19 publications

Using Sensors and Deep Learning to Enable On-Demand Balance Evaluation for Effective Physical Therapy

Using Sensors and Deep Learning to Enable On-Demand Balance Evaluation for Effective Physical Therapy

Deep neural network approach for estimating the three-dimensional human center of mass using joint angles

Evaluation of center of mass estimation for obese using statically equivalent serial chain

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