DeepSense
University of Illinois Urbana-Champaign · IBM (United States)
Abstract
Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as…
Citation impact
- FWCI
- 24.58
- Percentile
- 100%
- References
- 47
Authors
5Topics & keywords
- Computer science
- Artificial intelligence
- Activity recognition
- Noise (video)
- Accelerometer
- Biometrics
- Convolutional neural network
- Gyroscope
- Affordable and clean energy