From action to activity: Sensor-based activity recognition

By Ye Liu, Liqiang Nie, Li Liu, David S. Rosenblum. Neurocomputing, 2016

487 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c084b0fc86749ec824388bbe5b14c92d538aae53

How researchers see this paper

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

In Artificial Intelligence

1. Graziano Pravadelli
2. Florenc Demrozi
3. Parisa Rashidi
4. Azra Bihorac
5. Daniel Preotiuc-Pietro
6. Lyle Ungar
7. Daniel Hopkins
8. Liqiang Nie

In Computer Vision and Pattern Recognition

1. Parisa Rashidi
2. Graziano Pravadelli
3. Azra Bihorac
4. Florenc Demrozi
5. Daniel Preotiuc-Pietro
6. Lyle Ungar
7. Liqiang Nie
8. David S. Rosenblum

Citations in the corpus

• Beyond Binary Labels: Political Ideology Prediction of Twitter Users (2017, background): “…et al., 2010; Flekova et al., 2016b), gender (Burger et al., 2011; Sap et al., 2014), personality (Schwartz et al., 2013; Preoţiuc-Pietro et al., 2016), socioeconomic status (Preoţiuc-Pietro et al., 2015a,b; Liu et al., 2016c), popularity (Lampos et al., 2014) or location (Cheng et al., 2010).”
• Recent trends in machine learning for human activity recognition—A survey (2018, background): “The authors Liu et al. (2016) have presented an algorithm to identify the complex high-level activities from simple low-level actions in the sensor domain.”
• Action2Activity: Recognizing Complex Activities from Sensor Data (2015, background): “A set of approaches have been proposed to explore the simple relations among actions [Yang, 2009; Liu et al., 2016; Ryoo and Aggarwal, 2006].”
• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “In unsupervised learning, the goal is to identify patterns in input data without any knowledge on the output [105].”