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E. Principi, P. Olivetti, S. Squartini, R. Bonfigli, and F. Piazza, "A Floor Acoustic Sensor for Fall Classification," Paper 9329, (2015 May.). doi: E. Principi, P. Olivetti, S. Squartini, R. Bonfigli, and F. Piazza, "A Floor Acoustic Sensor for Fall Classification," Paper 9329, (2015 May.). doi:
Abstract: The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. In this preliminary work, the audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors, and Support Vector Machines to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of persons and of common objects. The results show the effectiveness of the approach.

@article{principi2015a,
author={principi, emanuele and olivetti, paolo and squartini, stefano and bonfigli, roberto and piazza, francesco},
journal={journal of the audio engineering society},
title={a floor acoustic sensor for fall classification},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},} @article{principi2015a,
author={principi, emanuele and olivetti, paolo and squartini, stefano and bonfigli, roberto and piazza, francesco},
journal={journal of the audio engineering society},
title={a floor acoustic sensor for fall classification},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={the interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. in this context the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. the sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. in this preliminary work, the audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on mel-frequency cepstral coefficients, supervectors, and support vector machines to discriminate among different types of fall events. the performance of the algorithm has been evaluated against a specific audio corpus comprising falls of persons and of common objects. the results show the effectiveness of the approach.},}

TY - paper
TI - A Floor Acoustic Sensor for Fall Classification
SP - EP -
AU - Principi, Emanuele
AU - Olivetti, Paolo
AU - Squartini, Stefano
AU - Bonfigli, Roberto
AU - Piazza, Francesco
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015 TY - paper
TI - A Floor Acoustic Sensor for Fall Classification
SP - EP -
AU - Principi, Emanuele
AU - Olivetti, Paolo
AU - Squartini, Stefano
AU - Bonfigli, Roberto
AU - Piazza, Francesco
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015
AB - The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. In this preliminary work, the audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors, and Support Vector Machines to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of persons and of common objects. The results show the effectiveness of the approach.