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Activity Recognition of non periodic activites



The Next CEO of Stack Overflow
2019 Community Moderator ElectionCNN for phoneme recognitionhow to retrain model with periodic new features?ANN on Pattern RecognitionSuitable Autoencoder for Activity Recognition dataset Feature Extractionk-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsHow to approach Peak picking with a wide range of peak shapes, sizes, varying noise level, and occasionally shifting baseline?ML Algorithm for anomaly detection in paired time-seriesActivity recognition with binary sensorsActivity Classification using accelerometer data?Action Recognition for multiple objects and localization










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$begingroup$


The data consits of single time series instances of a complex activity that can be divided into single non-periodic activities. The data was collected using mobile phones, holding information about acceleration and motion rotation rate on 3 axis, as well as speed & other sensor data.
I want to detect the single atomic activties. I've already labeled the data completely. However, there are some single activities that are non periodic, while some are periodic (like sitdown/close a door vs. walking). As the time variance of these activities are also very different I dont know which features/approach is suitable for this problem. I expect the classic windowing approach not to work well on here because the windows could miss/split the non-periodic activites.



Do you have any ideas / discussions on this problem? I thought about a 'template matching' approach.










share|improve this question









$endgroup$
















    0












    $begingroup$


    The data consits of single time series instances of a complex activity that can be divided into single non-periodic activities. The data was collected using mobile phones, holding information about acceleration and motion rotation rate on 3 axis, as well as speed & other sensor data.
    I want to detect the single atomic activties. I've already labeled the data completely. However, there are some single activities that are non periodic, while some are periodic (like sitdown/close a door vs. walking). As the time variance of these activities are also very different I dont know which features/approach is suitable for this problem. I expect the classic windowing approach not to work well on here because the windows could miss/split the non-periodic activites.



    Do you have any ideas / discussions on this problem? I thought about a 'template matching' approach.










    share|improve this question









    $endgroup$














      0












      0








      0





      $begingroup$


      The data consits of single time series instances of a complex activity that can be divided into single non-periodic activities. The data was collected using mobile phones, holding information about acceleration and motion rotation rate on 3 axis, as well as speed & other sensor data.
      I want to detect the single atomic activties. I've already labeled the data completely. However, there are some single activities that are non periodic, while some are periodic (like sitdown/close a door vs. walking). As the time variance of these activities are also very different I dont know which features/approach is suitable for this problem. I expect the classic windowing approach not to work well on here because the windows could miss/split the non-periodic activites.



      Do you have any ideas / discussions on this problem? I thought about a 'template matching' approach.










      share|improve this question









      $endgroup$




      The data consits of single time series instances of a complex activity that can be divided into single non-periodic activities. The data was collected using mobile phones, holding information about acceleration and motion rotation rate on 3 axis, as well as speed & other sensor data.
      I want to detect the single atomic activties. I've already labeled the data completely. However, there are some single activities that are non periodic, while some are periodic (like sitdown/close a door vs. walking). As the time variance of these activities are also very different I dont know which features/approach is suitable for this problem. I expect the classic windowing approach not to work well on here because the windows could miss/split the non-periodic activites.



      Do you have any ideas / discussions on this problem? I thought about a 'template matching' approach.







      classification time-series activity-recognition






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 25 at 9:51









      dataddicteddataddicted

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