• Corpus ID: 11078060

Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation

@article{Amaral2017BagofWordsMA,
  title={Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation},
  author={Kevin Michael Amaral and Ping Chen and Scott E. Crouter and Wei Ding},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.01574}
}
Accelerometer measurements are the prime type of sensor information most think of when seeking to measure physical activity. On the market, there are many fitness measuring devices which aim to track calories burned and steps counted through the use of accelerometers. These measurements, though good enough for the average consumer, are noisy and unreliable in terms of the precision of measurement needed in a scientific setting. The contribution of this paper is an innovative and highly accurate… 

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References

SHOWING 1-7 OF 7 REFERENCES

Automatic identification of physical activity types and sedentary behaviors from triaxial accelerometer: laboratory-based calibrations are not enough.

An automatic classification algorithm initially developed using laboratory-acquired data to discriminate between 8 activity classes was applied to data collected in the field, and recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful for the detection of sedentary behaviors while in transports.

Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living.

A single accelerometer placed on the thigh provided the highest accuracy for EE prediction, although monitors worn on the wrists or hip can also be used with high measurement accuracy.

Artificial neural networks to predict activity type and energy expenditure in youth.

ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

A novel method for using accelerometer data to predict energy expenditure.

A new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities is developed and is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer.

An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.

This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that it can successfully estimate activity METs and identify activity type using ANN analytic procedures.

Pattern Recognition and Machine Learning

This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.

Bipart: Learning Block Structure for Activity Detection

A general distance metric technique to use this block representation to first predict activity type, and then uses the predicted activity to estimate energy expenditure within a novel framework, dubbed Bipart.