Mixing
Deep learning on tri-axial data
I have a series of tri-axial accelerometer data of dimension (N, 1000, 3), where N is the number of instances, 1000 is the length of the acceleration data (i.e. 10 seconds sampled at 100 Hz) and 3 are the axes X, Y and Z. The data is also divided into two classes, A and B, where A accounts for 95% of the data. In total I have just under 3000 instances of class B. The aim of my project is to create model to detect class B.
I have been creating a number of machine learning models (decision trees, boosted modes etc) with features obtained via signal processing and statsitics (e.g. standard deviation, mean, magnitude, area under curve etc). These models perform well, but they seem to be missing a number of events in the real world, that by eye I can distinguish. This led me to believe that my features are missing key components of the classes. I've been going down into the rabbit hole of signal processing, but to date none has been that Eureka moment.
Now I am no expert in Deep learning, but by combining the data into a single axis (i.e. taking the magnitude) gave promising results (i.e. just as good as the current models). However, again taking the magnitude removes information. So I was wondering if there is a way to use deep learning to 1. select features from the individual axes and 2. use these as input for another deep learner to perform the classification. Something like this:
My simple view of multiple axis deep learner. Here the individual axes (i.e. X, Y and Z) are fed into seperate deep learners and the outputs are then fed into a single deep learner.
Apologies for the lots of text and lack of examples, as I'm not allowed to share the data, and only looking for guidance on whether deep learning can be of help. Thanks for taking the time read my post.
machine-learning deep-learning accelerometer
asked Jan 2 at 9:54
halien69halien69
21
add a comment |
I have a series of tri-axial accelerometer data of dimension (N, 1000, 3), where N is the number of instances, 1000 is the length of the acceleration data (i.e. 10 seconds sampled at 100 Hz) and 3 are the axes X, Y and Z. The data is also divided into two classes, A and B, where A accounts for 95% of the data. In total I have just under 3000 instances of class B. The aim of my project is to create model to detect class B.
I have been creating a number of machine learning models (decision trees, boosted modes etc) with features obtained via signal processing and statsitics (e.g. standard deviation, mean, magnitude, area under curve etc). These models perform well, but they seem to be missing a number of events in the real world, that by eye I can distinguish. This led me to believe that my features are missing key components of the classes. I've been going down into the rabbit hole of signal processing, but to date none has been that Eureka moment.
Now I am no expert in Deep learning, but by combining the data into a single axis (i.e. taking the magnitude) gave promising results (i.e. just as good as the current models). However, again taking the magnitude removes information. So I was wondering if there is a way to use deep learning to 1. select features from the individual axes and 2. use these as input for another deep learner to perform the classification. Something like this:
My simple view of multiple axis deep learner. Here the individual axes (i.e. X, Y and Z) are fed into seperate deep learners and the outputs are then fed into a single deep learner.
Apologies for the lots of text and lack of examples, as I'm not allowed to share the data, and only looking for guidance on whether deep learning can be of help. Thanks for taking the time read my post.
machine-learning deep-learning accelerometer
asked Jan 2 at 9:54
halien69halien69
21
add a comment |
I have a series of tri-axial accelerometer data of dimension (N, 1000, 3), where N is the number of instances, 1000 is the length of the acceleration data (i.e. 10 seconds sampled at 100 Hz) and 3 are the axes X, Y and Z. The data is also divided into two classes, A and B, where A accounts for 95% of the data. In total I have just under 3000 instances of class B. The aim of my project is to create model to detect class B.
I have been creating a number of machine learning models (decision trees, boosted modes etc) with features obtained via signal processing and statsitics (e.g. standard deviation, mean, magnitude, area under curve etc). These models perform well, but they seem to be missing a number of events in the real world, that by eye I can distinguish. This led me to believe that my features are missing key components of the classes. I've been going down into the rabbit hole of signal processing, but to date none has been that Eureka moment.
Now I am no expert in Deep learning, but by combining the data into a single axis (i.e. taking the magnitude) gave promising results (i.e. just as good as the current models). However, again taking the magnitude removes information. So I was wondering if there is a way to use deep learning to 1. select features from the individual axes and 2. use these as input for another deep learner to perform the classification. Something like this:
My simple view of multiple axis deep learner. Here the individual axes (i.e. X, Y and Z) are fed into seperate deep learners and the outputs are then fed into a single deep learner.
Apologies for the lots of text and lack of examples, as I'm not allowed to share the data, and only looking for guidance on whether deep learning can be of help. Thanks for taking the time read my post.
machine-learning deep-learning accelerometer
asked Jan 2 at 9:54
halien69halien69
21
I have a series of tri-axial accelerometer data of dimension (N, 1000, 3), where N is the number of instances, 1000 is the length of the acceleration data (i.e. 10 seconds sampled at 100 Hz) and 3 are the axes X, Y and Z. The data is also divided into two classes, A and B, where A accounts for 95% of the data. In total I have just under 3000 instances of class B. The aim of my project is to create model to detect class B.
I have been creating a number of machine learning models (decision trees, boosted modes etc) with features obtained via signal processing and statsitics (e.g. standard deviation, mean, magnitude, area under curve etc). These models perform well, but they seem to be missing a number of events in the real world, that by eye I can distinguish. This led me to believe that my features are missing key components of the classes. I've been going down into the rabbit hole of signal processing, but to date none has been that Eureka moment.
Now I am no expert in Deep learning, but by combining the data into a single axis (i.e. taking the magnitude) gave promising results (i.e. just as good as the current models). However, again taking the magnitude removes information. So I was wondering if there is a way to use deep learning to 1. select features from the individual axes and 2. use these as input for another deep learner to perform the classification. Something like this:
My simple view of multiple axis deep learner. Here the individual axes (i.e. X, Y and Z) are fed into seperate deep learners and the outputs are then fed into a single deep learner.
Apologies for the lots of text and lack of examples, as I'm not allowed to share the data, and only looking for guidance on whether deep learning can be of help. Thanks for taking the time read my post.
machine-learning deep-learning accelerometer
machine-learning deep-learning accelerometer
asked Jan 2 at 9:54
halien69halien69
21
asked Jan 2 at 9:54
halien69halien69
21
asked Jan 2 at 9:54
halien69halien69
21
asked Jan 2 at 9:54
halien69halien69
21
asked Jan 2 at 9:54
halien69halien69
21
21
add a comment |
add a comment |
1 Answer
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Since there is no specifics in the question, the answer can only be given in general terms.
If magnitude gives good result, you can fed X, Y, Z and magnitude into a single deep learner as 4 input.
In this case, your deep learner will be able to use a) separate features of axis, b) combining the data into a single axis, c) the relationship between the axes.
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
add a comment |
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1 Answer
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1 Answer
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active
oldest
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oldest
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active
oldest
votes
Since there is no specifics in the question, the answer can only be given in general terms.
If magnitude gives good result, you can fed X, Y, Z and magnitude into a single deep learner as 4 input.
In this case, your deep learner will be able to use a) separate features of axis, b) combining the data into a single axis, c) the relationship between the axes.
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
add a comment |
Since there is no specifics in the question, the answer can only be given in general terms.
If magnitude gives good result, you can fed X, Y, Z and magnitude into a single deep learner as 4 input.
In this case, your deep learner will be able to use a) separate features of axis, b) combining the data into a single axis, c) the relationship between the axes.
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
add a comment |
Since there is no specifics in the question, the answer can only be given in general terms.
If magnitude gives good result, you can fed X, Y, Z and magnitude into a single deep learner as 4 input.
In this case, your deep learner will be able to use a) separate features of axis, b) combining the data into a single axis, c) the relationship between the axes.
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
Since there is no specifics in the question, the answer can only be given in general terms.
If magnitude gives good result, you can fed X, Y, Z and magnitude into a single deep learner as 4 input.
In this case, your deep learner will be able to use a) separate features of axis, b) combining the data into a single axis, c) the relationship between the axes.
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
answered Jan 11 at 9:47
Pavel MatreninPavel Matrenin
1613
1613
add a comment |
add a comment |
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