Activity and Location Recognition Using Wearable Sensors

By Seon-Woo Lee, K. Mase. IEEE pervasive computing, 2002

581 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7909f29e6bdec236edbf58f357df7783dbde3402

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. Billur Barshan
2. Agata Brajdic
3. Diane Joyce Cook
4. Robert K. Harle
5. Stephen J. Preece
6. David Howard
7. John Yannis Goulermas
8. Laurence P. J. Kenney

In Computer Vision and Pattern Recognition

1. Diane Joyce Cook
2. Bernt Schiele
3. Billur Barshan
4. Narayanan Chatapuram Krishnan
5. Agata Brajdic
6. Luc Van Gool
7. Robert K. Harle
8. Stephen J. Preece

In Electrical and Electronic Engineering

1. Diane Joyce Cook
2. Agata Brajdic
3. Narayanan Chatapuram Krishnan
4. Stephen S. Intille
5. Ling Bao
6. Tâm Huynh
7. Robert K. Harle
8. Stephen J. Preece

Citations in the corpus

• An Activity Recognition System For Mobile Phones (2009, background): “Lee and Mase [ 14 ] developed an activity and location recognition system using a combination of a biaxial accelerometer, compass, and gyroscope.”
• Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors (2005, methodology): “Wearable sensors have been used for health monitoring [ 43 ], the facilitation of group interactions [32], and memory augmentation [64].”
• A Survey on Ambient Intelligence in Health Care (2013, unknown): “, walking, running, sitting, climbing, and falling) [40], [41], posture [42], and gestures [43]–[46].”
• Walk detection and step counting on unconstrained smartphones (2013, methodology): “Commonly used features include mean [4, 27, 29, 45, 49, 52, 57], variance or standard deviation [29, 31, 42, 45, 49, 52, 57], energy [4, 49, 52, 57], entropy [4, 42], correlation between axes [4, 45, 49], and FFT coefficients [26, 27].”
• Activity Recognition from User-Annotated Acceleration Data (2004, background): “Activity recognition has been performed on acceleration data collected from the hip (e.g. [17, 19]) and from multiple locations on the body (e.g. [5, 14]).”
• Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “By using a set of rules based around the min operation (the fuzzy equivalent of AND), Lee and Mase (2002) were able distinguish between different gaits with good accuracy (>90%).”
• Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges (2020, methodology): “In this section, sensors suitable for smart farming are explored and discussed [20]: 1) Location-based Sensors: Location sensors are used for locating different areas and spots in the agriculture fields [21], [22].”
• Multi-sensor Activity Context Detection for Wearable Computing (2003, background): “Golding and Lesh [12] and Lee and Mase [13] both use a variety of different sensors for location tracking.”
• Analyzing features for activity recognition (2005, methodology): “) Popular features computed from the acceleration signal are mean [1, 3, 2, 4, 9], variance or standard deviation [3, 2, 6, 9], energy [1, 9], entropy [1], correlation between axes [1, 9] or discrete FFT coefficients [4].”
• Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, methodology): “For example, Lee and Mase [20] used the acceleration and angular velocity sensor data measured by inexpensive wearable devices to determine the users’ location information and identify their sitting, standing, and walking behaviors.”
• Activity recognition using cell phone accelerometers (2011, background): “Lee and Mase [25] created a system to recognize a user’s location and activities, including sitting, standing, walking on level ground, walking upstairs, and walking downstairs using a sensor module that consisted of a biaxial accelerometer and an angular velocity sensor worn in the pocket combined ”
• Activity Recognition in the Home Using Simple and Ubiquitous Sensors (2004, unknown): “[19,11,18]).”
• Sensor-Based Activity Recognition (2012, methodology): “and gyroscopes, Lee and Mase [61] propose a dead-reckoning method to determine a user’s location and recognizing sitting, standing, and walking behaviors.”
• A Survey on Activity Detection and Classification Using Wearable Sensors (2017, methodology): “Some other earlier works did not even use a classification algorithm, but rather employed empirically determined thresholds for classification [38], [39], [173].”
• Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers (2010, unknown): “[45] 1 2D acc 1 gyro 1 compass Raw data Standard deviation Derivative Threshold-based 5 8 92.”