ABSTRACT
A comfortable and healthy lighting environment cannot only make people comfortable and improve work efficiency, but also avoid visual fatigue caused by the discomfortable lighting environment. In order to solve these problems, this paper proposes an intelligent lighting system that takes natural lighting and the occupancy situation as input and dimming factor K (0Subject(s)
Lighting
, Vision, Ocular
, Humans
, Computer Simulation
, Software
ABSTRACT
According to the working scenes, a proper light environment can enable people to maintain greater attention and meditation. A posture detection system in different working scenes is proposed in this paper, and different lighting conditions are provided for changes in body posture. This aims to stimulate the nervous system and improve work efficiency. A brainwave acquisition system was used to capture the participants' optimal attention and meditation. The posture data are collected by ten miniature inertial measurement units (IMUs). The gradient descent method is used for information fusion and updating the participant's attitude after sensor calibration. Compared with the optical capture system, the reliability of the system is verified, and the correlation coefficient of both joint angles is as high as 0.9983. A human rigid body model is designed for reconstructing the human posture. Five classical machine learning algorithms, including logistic regression, support vector machine (SVM), decision tree, random forest, and k-nearest neighbor (KNN), are used as classification algorithms to recognize different postures based on joint angles series. The results show that SVM and random forest achieve satisfactory classification effects. The effectiveness of the proposed method is demonstrated in the designed systematic experiment.
ABSTRACT
Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers' motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers' actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, including entry, pull, exit and recovery. The experiment shows that our method can provide comprehensive motion evaluation information under real on-water scenario, and the phase identification of kayaker's motions is up to 98% validated by videography method. The proposed approach can provide quantitative information for coaches and athletes, which can be used to improve the training effects.
Subject(s)
Posture , Wearable Electronic Devices , Algorithms , Biomechanical Phenomena , Humans , Motion , SportsABSTRACT
The slanted-edge method for modulation transfer function (MTF) measurement uses edge target images whose gray values are often affected by noise and other factors, decreasing its accuracy. We first analyze the ill-posedness in the edge spread function (ESF) regression caused by noise. Second, we propose a regularized slanted-edge method to solve this problem by incorporating a Tikhonov regularization term. Combined with varying precision weights, the ESF is solved using the variational principle, and the MTF is estimated using the regularized ESF. The regularized slanted-edge method is verified for Gaussian, gamma, and Rayleigh noise. The results show that our method improves the accuracy by 0.01-9.02% and 4.33% on average. The proposed method is more robust to noise and accurate than the slanted-edge method.
