@inproceedings{Elvira2014,

title = {A Novel Feature Extraction Technique for Human Activity Recognition},

author = {V\'{i}ctor Elvira and Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://edas.info/p15153#S1569490857},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},

address = {Gold Coast},

abstract = {This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.},

keywords = {Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Florentino-Liano2012b,

title = {Long Term Human Activity Recognition with Automatic Orientation Estimation},

author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349789},

issn = {1551-2541},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},

pages = {1--6},

publisher = {IEEE},

address = {Santander},

abstract = {This work deals with the elimination of sensitivity to sensor orientation in the task of human daily activity recognition using a single miniature inertial sensor. The proposed method detects time intervals of walking, automatically estimating the orientation in these intervals and transforming the observed signals to a “virtual” sensor orientation. Classification results show that excellent performance, in terms of both precision and recall (up to 100%), is achieved, for long-term recordings in real-life settings.},

keywords = {Acceleration, Activity recognition, automatic orientation estimation, biomedical equipment, Estimation, Gravity, Hidden Markov models, human daily activity recognition, Humans, Legged locomotion, long term human activity recognition, medical signal processing, object recognition, orientation estimation, sensors, single miniature inertial sensor, time intervals, Vectors, virtual sensor orientation, wearable sensors},

pubstate = {published},

tppubtype = {inproceedings}

}