標(biāo)題: Titlebook: Deep Learning for Human Activity Recognition; Second International Xiaoli Li,Min Wu,Le Zhang Conference proceedings 2021 Springer Nature Si [打印本頁(yè)] 作者: 自由 時(shí)間: 2025-3-21 18:57
書目名稱Deep Learning for Human Activity Recognition影響因子(影響力)
書目名稱Deep Learning for Human Activity Recognition影響因子(影響力)學(xué)科排名
書目名稱Deep Learning for Human Activity Recognition網(wǎng)絡(luò)公開度
書目名稱Deep Learning for Human Activity Recognition網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning for Human Activity Recognition被引頻次
書目名稱Deep Learning for Human Activity Recognition被引頻次學(xué)科排名
書目名稱Deep Learning for Human Activity Recognition年度引用
書目名稱Deep Learning for Human Activity Recognition年度引用學(xué)科排名
書目名稱Deep Learning for Human Activity Recognition讀者反饋
書目名稱Deep Learning for Human Activity Recognition讀者反饋學(xué)科排名
作者: evaculate 時(shí)間: 2025-3-21 20:28
Conducting Multiple Comparisonsed data is interpreted by introducing visualization methods including axis-wise heatmap and model-oriented decision explanation. The experiments show that our approach can effectively improve the classifier’s test accuracy by GAN-based data augmentation while well preserving the authenticity of synt作者: CHIDE 時(shí)間: 2025-3-22 03:23 作者: GENUS 時(shí)間: 2025-3-22 06:50
Nigeria: The State that Lost Its Future our dataset and explored potential methods for increasing their performances. We show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos (data available at .).作者: pacifist 時(shí)間: 2025-3-22 09:10 作者: notion 時(shí)間: 2025-3-22 15:33
Development Process and Requirements,of Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain 作者: notion 時(shí)間: 2025-3-22 20:37 作者: B-cell 時(shí)間: 2025-3-23 00:28
Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks,to assess sidewalk barriers for wheelchair users. The results show that the proposed method estimates sidewalk accessibilities from wheelchair accelerations and extracts knowledge of accessibilities by weakly supervised and self-supervised approaches.作者: 保存 時(shí)間: 2025-3-23 04:15 作者: acrimony 時(shí)間: 2025-3-23 05:32 作者: extinct 時(shí)間: 2025-3-23 10:22
ARID: A New Dataset for Recognizing Action in the Dark, our dataset and explored potential methods for increasing their performances. We show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos (data available at .).作者: Morsel 時(shí)間: 2025-3-23 17:08 作者: concert 時(shí)間: 2025-3-23 19:20
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition inof Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain 作者: Outwit 時(shí)間: 2025-3-24 02:05 作者: 艱苦地移動(dòng) 時(shí)間: 2025-3-24 06:11
Conference proceedings 2021d in a virtual format.?.The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.?.作者: 休閑 時(shí)間: 2025-3-24 08:27 作者: Brain-Imaging 時(shí)間: 2025-3-24 14:07
Conference proceedings 2021conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format.?.The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of hu作者: aplomb 時(shí)間: 2025-3-24 18:11 作者: Occipital-Lobe 時(shí)間: 2025-3-24 19:10
Requirements Engineering and Storyboarding with data from the community the testing user likely belongs to. Verified on a series of benchmark wearable datasets, the proposed techniques significantly outperform the model trained with all users.作者: 無能性 時(shí)間: 2025-3-25 03:10
Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark, an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.作者: transplantation 時(shí)間: 2025-3-25 05:44 作者: 一夫一妻制 時(shí)間: 2025-3-25 07:53
Nigeria: The State that Lost Its Futureman actions. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on edge embedded device (e.g. Nvidia Jetson Xavier).作者: 事先無準(zhǔn)備 時(shí)間: 2025-3-25 14:32
Single Run Action Detector over Video Stream - A Privacy Preserving Approach,man actions. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on edge embedded device (e.g. Nvidia Jetson Xavier).作者: 排斥 時(shí)間: 2025-3-25 18:10 作者: Brain-Waves 時(shí)間: 2025-3-25 20:44
Deep Learning for Human Activity Recognition978-981-16-0575-8Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: Intellectual 時(shí)間: 2025-3-26 00:20 作者: 陳列 時(shí)間: 2025-3-26 07:01
978-981-16-0574-1Springer Nature Singapore Pte Ltd. 2021作者: GULF 時(shí)間: 2025-3-26 10:55 作者: 開始發(fā)作 時(shí)間: 2025-3-26 13:07
Conducting Multiple Comparisonsevices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of stan作者: 真實(shí)的人 時(shí)間: 2025-3-26 19:06 作者: Carcinogen 時(shí)間: 2025-3-27 00:47 作者: 脊椎動(dòng)物 時(shí)間: 2025-3-27 01:25
Requirements Engineering and Storyboardinggnize activities of unseen subjects. However, participants come from diverse demographics, so that different users can perform the same actions in diverse ways. Each subject might exhibit user-specific signal patterns, yet a group of users may perform activities in similar manners and share analogou作者: nephritis 時(shí)間: 2025-3-27 09:21 作者: corporate 時(shí)間: 2025-3-27 10:21
Nigeria: The State that Lost Its Futurebeen made in action recognition task for videos in normal illumination, few have studied action recognition in the dark, partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data by c作者: 門閂 時(shí)間: 2025-3-27 15:35 作者: Surgeon 時(shí)間: 2025-3-27 21:43
Development Process and Requirements,on model trained with data from a group of users may not generalize well for unseen users and its performance is likely to be different for different users. To address these issues, this paper investigates the approach of fine-tuning a global model using user-specific data locally for personalizing 作者: 犬儒主義者 時(shí)間: 2025-3-28 00:09 作者: Inculcate 時(shí)間: 2025-3-28 04:51 作者: Petechiae 時(shí)間: 2025-3-28 07:43 作者: ESO 時(shí)間: 2025-3-28 11:20 作者: 許可 時(shí)間: 2025-3-28 18:40
Toward Data Augmentation and Interpretation in Sensor-Based Fine-Grained Hand Activity Recognition,r-based datasets of whole-body activities, there are limited data available for acceler-ator-based fine-grained hand activities. In this paper, we propose a purely convolution-based Generative Adversarial Networks (GAN) approach for data augmentation on accelerator-based temporal data of fine-graine作者: 小蟲 時(shí)間: 2025-3-28 21:57 作者: 使厭惡 時(shí)間: 2025-3-28 23:53
