The Experts below are selected from a list of 10293 Experts worldwide ranked by ideXlab platform
Hiroo Tamagawa - One of the best experts on this subject based on the ideXlab platform.
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evaluating Tooth Brushing performance with smartphone sound data
Ubiquitous Computing, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
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UbiComp - Evaluating Tooth Brushing performance with smartphone sound data
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '15, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
Joseph Korpela - One of the best experts on this subject based on the ideXlab platform.
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evaluating Tooth Brushing performance with smartphone sound data
Ubiquitous Computing, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
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UbiComp - Evaluating Tooth Brushing performance with smartphone sound data
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '15, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
Kazunori Nozaki - One of the best experts on this subject based on the ideXlab platform.
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evaluating Tooth Brushing performance with smartphone sound data
Ubiquitous Computing, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
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UbiComp - Evaluating Tooth Brushing performance with smartphone sound data
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '15, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
Ryosuke Miyaji - One of the best experts on this subject based on the ideXlab platform.
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evaluating Tooth Brushing performance with smartphone sound data
Ubiquitous Computing, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
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UbiComp - Evaluating Tooth Brushing performance with smartphone sound data
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '15, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
Takuya Maekawa - One of the best experts on this subject based on the ideXlab platform.
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evaluating Tooth Brushing performance with smartphone sound data
Ubiquitous Computing, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
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UbiComp - Evaluating Tooth Brushing performance with smartphone sound data
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '15, 2015Co-Authors: Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo TamagawaAbstract:This paper presents a new method for evaluating Tooth Brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of Tooth Brushing actions, such as Brushing the outer surface of the front teeth and Brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate Tooth Brushing performance scores, such as stroke quality of Brushing for the back inner teeth and duration of Brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
