Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

By Paul Lukowicz, Jamie A. Ward, Holger Junker, Mathias Stäger, Gerhard Tröster, Amin Atrash, Thad Starner. International Conference on Pervasive Computing, 2004

324 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Human-Computer Interaction, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5588d480e4f820cd14a2d9ec3b0c698af697f656

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. Diane Joyce Cook
2. Qiang Yang
3. Stephen J. Preece
4. David Howard
5. John Yannis Goulermas
6. Laurence P. J. Kenney
7. William L. Haskell
8. C. Nugent

In Human-Computer Interaction

1. Diane Joyce Cook
2. Samer Mohammed
3. Qiang Yang
4. Stephen S. Intille
5. Yacine Amirat
6. Andreas Bulling
7. Narayanan Chatapuram Krishnan

In Signal Processing

1. Diane Joyce Cook
2. Qiang Yang
3. Stephen J. Preece
4. Jorge Luis Reyes-Ortiz
5. David Howard
6. Xavier Parra
7. Luca Oneto
8. Davide Anguita

Citations in the corpus

• Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “Lukowicz et al (2004) and Ward et al (2006) used HMMs to classify a range of workshop activities, such as using a saw or screw driver, from body-worn accelerometers and microphones.”
• Sensor-Based Activity Recognition (2012, background): “As a result of the wave of intensive investigation, there have seen a plethora of impressive works on sensor-based activity recognition in the past several years [57], [58], [62], [64], [76]–[80], [82], [83], [85], [87], [97].”
• Sensor-Based Abnormal Human-Activity Detection (2008, methodology): “Moreover, body-worn microphones and accelerometers were also used to track users’ daily activities in [18].”
• Physical Human Activity Recognition Using Wearable Sensors (2015, methodology): “HMMs have also been used as a part of unsupervised learning algorithms for human activity recognition studies [78–80].”
• Gesture spotting with body-worn inertial sensors to detect user activities (2008, methodology): “Lukowicz et al. [22] concentrated on workshop activities including sawing, hammering, drilling and filing.”
• Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor (2007, background): “Furthermore, most work with accelerometers has either used cumbersome wired sensors [3] or sensors that store data locally for off-line processing [1, 4, 5].”
• Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, methodology): “Lukowicz et al. [25] used gyroscopes and accelerometers to measure the acceleration and angular velocity.”
• A Public Domain Dataset for Human Activity Recognition using Smartphones (2013, background): “Recognition can be accomplished by exploiting the information retrieved from various sources such as environmental [1] or body-worn sensors [2, 3].”
• A Survey on Ambient Intelligence in Health Care (2013, unknown): “Other modalities that have been researched for activity recognition include video cameras [58]–[61], microphones [62], [63], and GPS locators [64], [65].”