{ "id": 582542, "title": "Related Papers : Literature Review", "url": "/competitions/cmi-detect-behavior-with-sensor-data/discussion/582542", "author_name": "", "post_date": "2025-06-01T03:46:02.684834100Z", "votes": 45, "comment_count": 4, "views": 0, "content": "
    \n

  1. [EnsemblesLSTM] Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (IMWUT/ubicomp) paper Tensorflow

    2. \n

  2. Deep Learning for Sensor-based Activity Recognition: A Survey (Pattern Recognition Letters) paper

    3. \n

  3. Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation (IMWUT/ubicomp) paper

    4. \n

  4. Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems (IMWUT/ubicomp) paper

    5. \n

  5. CNN-based sensor fusion techniques for multimodal human activity recognition (ISWC/ubicomp) paper

    6. \n

  6. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble (ubicomp) paper

    7. \n

  7. [AROMA] AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors (IMWUT/ubicomp) paper

    8. \n

  8. A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition (IJCAI) papercode

    9. \n

  9. [AttnSense] AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition (IJCAI) paper

    10. \n

  10. Multi-agent Attentional Activity Recognition (IJCAI) papercode

    11. \n

  11. Distribution-based Semi-Supervised Learning for Activity Recognition (AAAI) papercode

    12. \n
", "messages": [ { "id": "3214765", "postDate": "06/01/2025 03:46:02", "content": "
    \n

  1. [EnsemblesLSTM] Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (IMWUT/ubicomp) paper Tensorflow

    2. \n

  2. Deep Learning for Sensor-based Activity Recognition: A Survey (Pattern Recognition Letters) paper

    3. \n

  3. Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation (IMWUT/ubicomp) paper

    4. \n

  4. Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems (IMWUT/ubicomp) paper

    5. \n

  5. CNN-based sensor fusion techniques for multimodal human activity recognition (ISWC/ubicomp) paper

    6. \n

  6. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble (ubicomp) paper

    7. \n

  7. [AROMA] AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors (IMWUT/ubicomp) paper

    8. \n

  8. A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition (IJCAI) papercode

    9. \n

  9. [AttnSense] AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition (IJCAI) paper

    10. \n

  10. Multi-agent Attentional Activity Recognition (IJCAI) papercode

    11. \n

  11. Distribution-based Semi-Supervised Learning for Activity Recognition (AAAI) papercode

    12. \n
", "rawMarkdown": "1. [EnsemblesLSTM] Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (IMWUT/ubicomp) [paper](https://arxiv.org/pdf/1703.09370) [Tensorflow](https://github.com/tploetz/LSTMEnsemble4HAR)\n\n2. Deep Learning for Sensor-based Activity Recognition: A Survey (Pattern Recognition Letters) [paper](https://arxiv.org/pdf/1707.03502)\n\n3. Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation (IMWUT/ubicomp) [paper](https://dl.acm.org/doi/10.1145/3130927)\n\n4. Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems (IMWUT/ubicomp) [paper](https://dl.acm.org/doi/10.1145/3130959)\n \n5. CNN-based sensor fusion techniques for multimodal human activity recognition (ISWC/ubicomp) [paper](https://dl.acm.org/doi/abs/10.1145/3123021.3123046)\n\n6. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble (ubicomp) [paper](https://dl.acm.org/doi/10.1145/2971648.2971708)\n\n7. [AROMA] AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors (IMWUT/ubicomp) [paper](https://dl.acm.org/doi/abs/10.1145/3214277?download=true)\n\n8. A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition (IJCAI) [paper](https://www.ijcai.org/Proceedings/2019/0779.pdf)[code](https://github.com/Hangwei12358/IJCAI2019_DDNN)\n\n9. [AttnSense] AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition (IJCAI) [paper](https://www.ijcai.org/Proceedings/2019/0431.pdf)\n\n10. Multi-agent Attentional Activity Recognition (IJCAI) [paper](https://arxiv.org/pdf/1905.08948)[code](https://github.com/KaixuanChen/Multi-agent-Attentional-Activity-Recognition)\n\n11. Distribution-based Semi-Supervised Learning for Activity Recognition (AAAI) [paper](https://ojs.aaai.org/index.php/AAAI/article/view/4765)[code](https://github.com/Hangwei12358/AAAI2019_DSSL)", "votes": null }, { "id": "3215123", "postDate": "06/01/2025 16:06:59", "content": "

@ahsuna123 Well done. Thank you for sharing!

", "rawMarkdown": "ahsuna123 Well done. Thank you for sharing!", "votes": null }, { "id": "3215880", "postDate": "06/02/2025 18:57:17", "content": "

@ahsuna123 Thanks for sharing.

", "rawMarkdown": "ahsuna123 Thanks for sharing.", "votes": null }, { "id": "3244294", "postDate": "07/08/2025 04:14:43", "content": "

Thanks for sharing!
\nI’ve recently been exploring Google’s Large Sensor Model and am wondering whether its ideas might be useful for this competition as well. Here’s the link to the latest paper:

\n

Large Sensor Model
\nhttps://arxiv.org/html/2410.13638v1

\n

Large Sensor Model 2
\nhttps://arxiv.org/pdf/2506.05321

", "rawMarkdown": "Thanks for sharing!\nI’ve recently been exploring Google’s Large Sensor Model and am wondering whether its ideas might be useful for this competition as well. Here’s the link to the latest paper:\n\nLarge Sensor Model\nhttps://arxiv.org/html/2410.13638v1\n\nLarge Sensor Model 2\nhttps://arxiv.org/pdf/2506.05321", "votes": null }, { "id": "3244299", "postDate": "07/08/2025 04:20:06", "content": "

Thank you for providing good resource when I stuck.

", "rawMarkdown": "Thank you for providing good resource when I stuck.", "votes": null } ], "comments": [ { "id": 3215123, "author_name": "chumajin", "author_url": "", "post_date": "06/01/2025 16:06:59", "content": "

@ahsuna123 Well done. Thank you for sharing!

", "votes": null, "replies": [] }, { "id": 3215880, "author_name": "samithsachidanandan", "author_url": "", "post_date": "06/02/2025 18:57:17", "content": "

@ahsuna123 Thanks for sharing.

", "votes": null, "replies": [] }, { "id": 3244294, "author_name": "kmatsu01", "author_url": "", "post_date": "07/08/2025 04:14:43", "content": "

Thanks for sharing!
\nI’ve recently been exploring Google’s Large Sensor Model and am wondering whether its ideas might be useful for this competition as well. Here’s the link to the latest paper:

\n

Large Sensor Model
\nhttps://arxiv.org/html/2410.13638v1

\n

Large Sensor Model 2
\nhttps://arxiv.org/pdf/2506.05321

", "votes": null, "replies": [] }, { "id": 3244299, "author_name": "tom99763", "author_url": "", "post_date": "07/08/2025 04:20:06", "content": "

Thank you for providing good resource when I stuck.

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3214765": "1. [EnsemblesLSTM] Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (IMWUT/ubicomp) [paper](https://arxiv.org/pdf/1703.09370) [Tensorflow](https://github.com/tploetz/LSTMEnsemble4HAR)\n\n2. Deep Learning for Sensor-based Activity Recognition: A Survey (Pattern Recognition Letters) [paper](https://arxiv.org/pdf/1707.03502)\n\n3. Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation (IMWUT/ubicomp) [paper](https://dl.acm.org/doi/10.1145/3130927)\n\n4. Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems (IMWUT/ubicomp) [paper](https://dl.acm.org/doi/10.1145/3130959)\n \n5. CNN-based sensor fusion techniques for multimodal human activity recognition (ISWC/ubicomp) [paper](https://dl.acm.org/doi/abs/10.1145/3123021.3123046)\n\n6. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble (ubicomp) [paper](https://dl.acm.org/doi/10.1145/2971648.2971708)\n\n7. [AROMA] AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors (IMWUT/ubicomp) [paper](https://dl.acm.org/doi/abs/10.1145/3214277?download=true)\n\n8. A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition (IJCAI) [paper](https://www.ijcai.org/Proceedings/2019/0779.pdf)[code](https://github.com/Hangwei12358/IJCAI2019_DDNN)\n\n9. [AttnSense] AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition (IJCAI) [paper](https://www.ijcai.org/Proceedings/2019/0431.pdf)\n\n10. Multi-agent Attentional Activity Recognition (IJCAI) [paper](https://arxiv.org/pdf/1905.08948)[code](https://github.com/KaixuanChen/Multi-agent-Attentional-Activity-Recognition)\n\n11. Distribution-based Semi-Supervised Learning for Activity Recognition (AAAI) [paper](https://ojs.aaai.org/index.php/AAAI/article/view/4765)[code](https://github.com/Hangwei12358/AAAI2019_DSSL)", "3215123": "ahsuna123 Well done. Thank you for sharing!", "3215880": "ahsuna123 Thanks for sharing.", "3244294": "Thanks for sharing!\nI’ve recently been exploring Google’s Large Sensor Model and am wondering whether its ideas might be useful for this competition as well. Here’s the link to the latest paper:\n\nLarge Sensor Model\nhttps://arxiv.org/html/2410.13638v1\n\nLarge Sensor Model 2\nhttps://arxiv.org/pdf/2506.05321", "3244299": "Thank you for providing good resource when I stuck." }, "source": "meta" }