标题
Human activity recognition adapted to the type of movement
作者
关键词
Human movements characteristics, Type of movements, Deep learning, Convolutional neural networks, Fast Fourier Transform, Intra-window LSTM
出版物
COMPUTERS & ELECTRICAL ENGINEERING
Volume 88, Issue -, Pages 106822
出版商
Elsevier BV
发表日期
2020-09-02
DOI
10.1016/j.compeleceng.2020.106822
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Human motion recognition exploiting radar with stacked recurrent neural network
- (2019) Mingyang Wang et al. DIGITAL SIGNAL PROCESSING
- Design and assessment of the data analysis process for a wrist-worn smart object to detect atomic activities in the smart home
- (2019) Qin Ni et al. Pervasive and Mobile Computing
- Modelling of interactions for the recognition of activities in groups of people
- (2018) Kyle Stephens et al. DIGITAL SIGNAL PROCESSING
- HuMAn: Complex Activity Recognition with Multi-modal Multi-positional Body Sensing
- (2018) Pratool Bharti et al. IEEE TRANSACTIONS ON MOBILE COMPUTING
- Deep learning for sensor-based activity recognition: A Survey
- (2018) Jindong Wang et al. PATTERN RECOGNITION LETTERS
- Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor
- (2018) Uriel Martinez-Hernandez et al. PATTERN RECOGNITION LETTERS
- Deep Recurrent Neural Networks for Human Activity Recognition
- (2017) Abdulmajid Murad et al. SENSORS
- Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer
- (2017) Mohd Halim Mohd Noor et al. Pervasive and Mobile Computing
- Human activity recognition with smartphone sensors using deep learning neural networks
- (2016) Charissa Ann Ronao et al. EXPERT SYSTEMS WITH APPLICATIONS
- Frequency features and GMM-UBM approach for gait-based person identification using smartphone inertial signals
- (2016) Rubén San-Segundo et al. PATTERN RECOGNITION LETTERS
- Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
- (2016) Francisco Ordóñez et al. SENSORS
- Segmenting human activities based on HMMs using smartphone inertial sensors
- (2016) Rubén San-Segundo et al. Pervasive and Mobile Computing
- High dimensional low sample size activity recognition using geometric classifiers
- (2015) Muhammad Shahzad Cheema et al. DIGITAL SIGNAL PROCESSING
- A tutorial on human activity recognition using body-worn inertial sensors
- (2014) Andreas Bulling et al. ACM COMPUTING SURVEYS
- Window Size Impact in Human Activity Recognition
- (2014) Oresti Banos et al. SENSORS
- Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units
- (2013) B. Barshan et al. COMPUTER JOURNAL
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now