Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor

By Emmanuel Munguia Tapia, Stephen S. Intille, William L. Haskell, Kent Larson, Julie A. Wright, Abby C. King, Robert H. Friedman. International Semantic Web Conference, 2007

560 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Surgery

View Paper: https://www.semanticscholar.org/paper/ab65fe78557de26caed284a6dd996a340896e595

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. Gary M. Weiss
2. Diane Joyce Cook
3. Narayanan Chatapuram Krishnan
4. Samuel Moore
5. Jennifer R. Kwapisz
6. E. Ramirez
7. Gregory J. Norman
8. Fernando De la Torre

In Computer Vision and Pattern Recognition

1. Narayanan Chatapuram Krishnan
2. Diane Joyce Cook
3. Andrew T. Campbell
4. Gary M. Weiss
5. Omer Berat Sezer
6. Nicholas Donald Lane
7. Yi Wang
8. A. Ozbayoglu

Citations in the corpus

• Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “In many studies the recognition setup involved cumbersome devices that had to be worn in a specific way [5], [12], [13], [14], [15], [17], [22], [23], [25], [28], [29].”
• Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, methodology): “BNs and NB classifiers are used in human activity recognition [151] and health monitoring areas, such as monitoring and classifying respiration rate, step count [160], and heart rate data [152], [153].”
• Activity recognition using cell phone accelerometers (2011, methodology): “Additional studies have similarly focused on how one can use a variety of accelerometerbased devices to identify a range of user activities [4-7, 9-16, 21].”
• BEST PRACTICES FOR USING PHYSICAL ACTIVITY MONITORS IN POPULATION-BASED RESEARCH (2012, unknown): “First, will single-unit (single placement site) devices perform as well as systems that use sensors in multiple sites? Will the use of physiological signals, such as body heat or HR, provide additional useful information above and beyond the movement information? Preliminary evidence suggests that b”
• Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application (2008, methodology): “Our classification algorithm is based on a decision tree technique [32][33].”
• Simple and Complex Activity Recognition through Smart Phones (2012, background): “[7] collected data from five accelerometers placed on various body locations for implementing a real-time system to recognize thirty gymnasium activities.”
• Sequential Max-Margin Event Detectors (2014, background): “Many problems in surveillance [14], activity analysis [1], clinical monitoring [20] and human computer interaction [12] can be posed as detecting events in time series.”
• Classification Accuracies of Physical Activities Using Smartphone Motion Sensors (2012, methodology): “In the literature, different features have been employed for acceleration-based activity recognition, such as mean [4,5,9,12], variance [11,12], spectral entropy [4, 5], and fast Fourier transform coefficients [5,9,11,18].”
• An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. (2009, methodology): “Other groups have also had success identifying specific activities by applying different types of pattern recognition algorithms to accelerometer data (5, 14, 15, 21).”