Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people...Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders.Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity recognition.In this paper,an RGB-D-based ADL recognition system has been presented.Initially,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.Based on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)algorithm.Finally,random forest classifier has been used to classify activities into different categories.The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.展开更多
In this paper we discuss the learning convergence of the cerebellar model articulation controller (CMAC) in cyclic learning. We prove the following results. First, if the training samples are noiseless, the training a...In this paper we discuss the learning convergence of the cerebellar model articulation controller (CMAC) in cyclic learning. We prove the following results. First, if the training samples are noiseless, the training algorithm converges if and only if the learning rate is chosen from (0, 2). Second, when the training samples have noises, the learning algorithm will converge with a probability of one if the learning rate is dynandcally decreased. Third, in the case with noises, with a small but fixed learning rate ε.the mean square error of the weight sequences generated by the CMAC learning algorithm will be bounded by O(ε). Some simulation experlinents are carried out totest these results.展开更多
基金This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders.Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity recognition.In this paper,an RGB-D-based ADL recognition system has been presented.Initially,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.Based on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)algorithm.Finally,random forest classifier has been used to classify activities into different categories.The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.
文摘In this paper we discuss the learning convergence of the cerebellar model articulation controller (CMAC) in cyclic learning. We prove the following results. First, if the training samples are noiseless, the training algorithm converges if and only if the learning rate is chosen from (0, 2). Second, when the training samples have noises, the learning algorithm will converge with a probability of one if the learning rate is dynandcally decreased. Third, in the case with noises, with a small but fixed learning rate ε.the mean square error of the weight sequences generated by the CMAC learning algorithm will be bounded by O(ε). Some simulation experlinents are carried out totest these results.
