Proceedings ArticleDOI
Energy-Efficient Activity Recognition Using Prediction
Dawud Gordon,Jurgen Czerny,Takashi Miyaki,Michael Beigl +3 more
- pp 29-36
TLDR
A novel method for activity recognition which leverages the predictability of human behavior to conserve energy is presented, which accomplishes this by quantifying activity-sensor dependencies, and using prediction methods to identify likely future activities.Abstract:
Energy storage is quickly becoming the limiting factor in mobile pervasive technology. For intelligent wearable applications to be practical, methods for low power activity recognition must be embedded in mobile devices. We present a novel method for activity recognition which leverages the predictability of human behavior to conserve energy. The novel algorithm accomplishes this by quantifying activity-sensor dependencies, and using prediction methods to identify likely future activities. Sensors are then identified which can be temporarily turned off at little or no recognition cost. The approach is implemented and simulated using an activity recognition data set, revealing that large savings in energy are possible at very low cost (e.g. 84% energy savings for a loss of 1.2 pp in recognition).read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Proceedings ArticleDOI
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks
Wenchao Jiang,Zhaozheng Yin +1 more
TL;DR: This work assembles signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activity image for the activity recognition task.
Journal ArticleDOI
A Survey of Wearable Devices and Challenges
Suranga Seneviratne,Yining Hu,Tham Nguyen,Guohao Lan,Sara Khalifa,Kanchana Thilakarathna,Mahbub Hassan,Aruna Seneviratne +7 more
TL;DR: The communication security issues facing the popular wearables is examined followed by a survey of solutions studied in the literature, and the techniques for improving the power efficiency of wearables are explained.
Proceedings ArticleDOI
CrowdRecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint
TL;DR: The results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.
Journal ArticleDOI
HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices
TL;DR: The aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE) and demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity
TL;DR: Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity.
Proceedings ArticleDOI
Trading off prediction accuracy and power consumption for context-aware wearable computing
Andreas Krause,M. Ihmig,E. Rankin,Derek Leong,Smriti Gupta,Daniel P. Siewiorek,Asim Smailagic,Michael E. Deisher,U. Sengupta +8 more
TL;DR: The trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform is analyzed and optimized sampling schemes are proposed.