Row 2137

Row ID: 2137 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 2137 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Hello everyone,

For the last couple of hours I've been trying to solve a problem of which I'm unsure if it can be fixed, or if I'm trying something that just can't work.

I have collected data from test participants for an emotional analysis, this includes heart rate, galvanic skin response and their facial expression. I have data of 11 participants, with 1Hz sampling, so 480 datapoints per participant. I also have labels that I want to use for training for every datapoint, for every participant, these are unique values (We are calculating their emotional change, so I have a slope value that indicates a positive/negative shift).

We want to train a neural network to be able to determine this slope. My problem is that I have data from 11 participants, in separate csv files. I want the neural network to take each of these 11 files, train on that and update the values, since the relation needs to be assessed within each test participant. Currently I have made 2 networks using LSTM layers, and a CNN for the facial recognition. I use a fusion layer at the end to combine everything. My question is: Is this a good approach and is this doable, and secondly how do I correctly set this up, especially in regards to reading the data from the different csv files and how to handle the labels (which are also in individual csv files for each participant). Also considering that the end result of the network should be a slope value again.

Thank you very much!

FieldValue
text Hello everyone, For the last couple of hours I've been trying to solve a problem of which I'm unsure if it can be fixed, or if I'm trying something that just can't work. I have collected data from test participants for an emotional analysis, this includes heart rate, galvanic skin response and their facial expression. I have data of 11 participants, with 1Hz sampling, so 480 datapoints per participant. I also have labels that I want to use for training for every datapoint, for every participan…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-06-29
username_encoded Z0FBQUFBQm5LakwwUTVHN1VNN1hjeDU4X01UdG9ZanpvbjMyRkVsbDAtXzg0SGtzT1hxeGJ2Z2JqZkkxcDQ0YjByNjZJSGNqMlBmanZESU56ek45VXBBSUR4VUt2NGpaWmc9PQ==
url_encoded Z0FBQUFBQm5Lak9FOVpaQXlyV2ZjSi10aEVQemZPNHBHdVZrQTJtR3RpZGNyZzQ2Q3ZxYUVYbDV2QjZ0WmwwRHVWRkhUcjVjYTJRWFlEdzFWSGRleGNwc0pfYzU5WHVyN3d0ZHRVQjBKdkVMNzlWU0dLaTNjODg0N3VnakhsMm1ldmludU1oUXV0NkJBUUFEdTFQTThTUFZpNWdIcjBOc1dBbnVmSjRvRXkwdklVR3ZVRllpVDJaRTR0WE4yOER2YXJ3S3BvTGI4Rnh5

Raw Record

{
  "text": "Hello everyone,\n\nFor the last couple of hours I've been trying to solve a problem of which I'm unsure if it can be fixed, or if I'm trying something that just can't work.\n\nI have collected data from test participants for an emotional analysis, this includes heart rate, galvanic skin response and their facial expression. I have data of 11 participants, with 1Hz sampling, so 480 datapoints per participant. I also have labels that I want to use for training for every datapoint, for every participant, these are unique values (We are calculating their emotional change, so I have a slope value that indicates a positive/negative shift). \n\nWe want to train a neural network to be able to determine this slope. My problem is that I have data from 11 participants, in separate csv files. I want the neural network to take each of these 11 files, train on that and update the values, since the relation needs to be assessed within each test participant. Currently I have made 2 networks using LSTM layers, and a CNN for the facial recognition. I use a fusion layer at the end to combine everything.   \nMy question is: Is this a good approach and is this doable, and secondly how do I correctly set this up, especially in regards to reading the data from the different csv files and how to handle the labels (which are also in individual csv files for each participant). Also considering that the end result of the network should be a slope value again.\n\nThank you very much!",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2023-06-29",
  "username_encoded": "Z0FBQUFBQm5LakwwUTVHN1VNN1hjeDU4X01UdG9ZanpvbjMyRkVsbDAtXzg0SGtzT1hxeGJ2Z2JqZkkxcDQ0YjByNjZJSGNqMlBmanZESU56ek45VXBBSUR4VUt2NGpaWmc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9FOVpaQXlyV2ZjSi10aEVQemZPNHBHdVZrQTJtR3RpZGNyZzQ2Q3ZxYUVYbDV2QjZ0WmwwRHVWRkhUcjVjYTJRWFlEdzFWSGRleGNwc0pfYzU5WHVyN3d0ZHRVQjBKdkVMNzlWU0dLaTNjODg0N3VnakhsMm1ldmludU1oUXV0NkJBUUFEdTFQTThTUFZpNWdIcjBOc1dBbnVmSjRvRXkwdklVR3ZVRllpVDJaRTR0WE4yOER2YXJ3S3BvTGI4Rnh5"
}
Explore Dataset Explore Row

Entry Information