A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer

By Adil Mehmood Khan, Young-Koo Lee, Sungyoung Lee, Tae-Seong Kim. IEEE Transactions on Information Technology in Biomedicine, 2010

566 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a646902191a27b77497b421bc0a529bb92f4ab16

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Nasser Kehtarnavaz
2. Roozbeh Jafari
3. Cheng Chen
4. Oscar D. Lara
5. Miguel A. Labrador
6. Murat Cihan Yüksek
7. Li Fei-Fei
8. Alexandre Alahi

In Computer Vision and Pattern Recognition

1. Nasser Kehtarnavaz
2. Li Fei-Fei
3. Luc Van Gool
4. Roozbeh Jafari
5. Ming-Hsuan Yang
6. Trevor Darrell
7. Xiaogang Wang
8. Dacheng Tao

Citations in the corpus

• The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC-2014) A Study on Human Activity Recognition Using Accelerometer Data from Smartphones (2014, methodology): “Four different settings are considered for the dataset: (1) data collected for a single subject over different days, (2) data collected for multiple subjects over different days, (3) data collected for a single subject on one day as training data and further data collected for the same subject on an”
• Activity recognition with smartphone sensors (2014, methodology): “SMA is calculated as the sum of the magnitude of the three axes acceleration within the segment window[23].”
• Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals (2017, background): “[65, 32] use recurrent networks for single triaxial accelerometer data, [41] for a heart rate controller, and [50] for multimodal wearable sensors.”
• A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “In addition, the amplitude of the accelerometers varies from ±2g [24], up to ±6g [38] yet ±2g was shown to be sufficient to recognize ambulation activities [24].”
• A robust human activity recognition system using smartphone sensors and deep learning (2018, background): “Many HAR systems over the past used accelerometers to recognize a big range of daily activities such as standing, walking, sitting, running, and lying [17]-[23].”
• Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors (2015, methodology): “In [12], a hierarch icalrecognition scheme was proposed to extract features based o n linear discriminant analysis from a single tri-axial accel erometer.”
• Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, background): “Backpropagation neural networks are used in human activity recognition (walking, running, sitting, etc.) with wearable sensors [139], [140].”
• Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, background): “[52] 3D acc (×1) 15 pos, mot, trans 3M 3F Hierarchical recognizer (LDA+ANN): 97.”
• Window Size Impact in Human Activity Recognition (2014, unknown): “(2010) [34], Hong et al.”