The use of flexible wearable sensors to monitor the impact of sleeping position and turning frequency on sleep and to study sleep patterns can help bedridden patients heal and recover. The flexible wearable sleeping-position monitoring device was designed and developed using a flexible angle sensor and a six-axis motion sensor to measure the dynamic changes in body posture during sleep. Based on the changes in the output parameters of the flexible angle sensor and the six-axis motion sensor, we determined the change in the subject’s lying position, verifying and analyzing the relationship between lying position preference, turning frequency, and sleep quality in healthy subjects. The sleeping-position monitoring device was worn by 13 subjects (7 males and 6 females) without sleep disorders before the sleep experiment. They performed more than 50 sleeping-position changes to ensure the accuracy of the monitoring device. Subjects slept in their beds for 8 h per night for 15 nights. During that time, they wore the sleeping-position monitoring device and a wristband sleep-monitoring bracelet on their left hand, and gathered the subjective sleep data using questionnaires. The results show that the most critical influencing factors are sleeping-position preference and frequency of turning. Data analysis reveals that subjects with a preference for right-sided lying and a lower frequency of turning had better sleep quality.
In this paper, we adjust the hyperparameters of the training model based on the gradient estimation theory and optimize the structure of the model based on the loss function theory of Mask R-CNN convolutional network and propose a scheme to help a tennis picking robot to perform target recognition and improve the ability of the tennis picking robot to acquire and analyze image information. By collecting suitable image samples of tennis balls and training the image samples using Mask R-CNN convolutional network an algorithmic model dedicated to recognizing tennis balls is output; the final data of various loss functions after gradient descent are recorded, the iterative graph of the model is drawn, and the iterative process of the neural network at different iteration levels is observed; finally, this improved and optimized algorithm for recognizing tennis balls is compared with other algorithms for recognizing tennis balls and a comparison is made. The experimental results show that the improved algorithm based on Mask R-CNN recognizes tennis balls with 92% accuracy between iteration levels 30 and 35, which has higher accuracy and recognition distance compared with other tennis ball recognition algorithms, confirming the feasibility and applicability of the optimized algorithm in this paper.
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