ABSTRACT
Researchers have been working for a long time to recognize human activities based on sensor-based data. Despite the ongoing advancements in this field, it remains challenging to recognize complex human activities for a specific domain. To bring attention to this issue, the “Third Nurse Care Activity Recognition Challenge” gathered accelerometer data from smartphones to estimate daily nurse care activities. The main challenge was handling the noisy and inconsistent dataset, which is a persistent issue in any real-life data. Also, each activity depends on both the subject and the receiver, making its recognition more complex. Our team, Team Alkaline, used high pass and low pass filters to reduce noise, adopting an overlapping windowing technique. Then we extracted features in multiple domains to derive the necessary information required for classifying the human activities more accurately. Later on, we used a feature selection method to select the most significant features. We applied Random Forest (RF) classifier for training and achieved 99.0% accuracy on the validation set.
- Sayeda Shamma Alia, Paula Lago, Kohei Adachi, Tahera Hossain, Hiroki Goto, Tsuyoshi Okita, and Sozo Inoue. 2020. Summary of the 2 nd nurse care activity recognition challenge using lab and field data. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 378–383.Google ScholarDigital Library
- Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones.. In Esann, Vol. 3. 3.Google Scholar
- Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md Mamun Sheikh, AHM Nazmus Sakib, Sriman Bidhan Baray, and MAR Ahad. 2020. Complex nurse care activity recognition using statistical features. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 384–389.Google ScholarDigital Library
- Sayeda Shamma Alia; Kohei Adachi; Nhat Tan Le; Haru Kaneko; Paula Lago; Sozo Inoue. 2021. Third Nurse Care Activity Recognition Challenge. https://doi.org/10.21227/hj46-zs46Google Scholar
- Mohammad Sabik Irbaz, Abir Azad, Tanjila Alam Sathi, and Lutfun Nahar Lota. 2020. Nurse care activity recognition based on machine learning techniques using accelerometer data. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 402–407.Google ScholarDigital Library
- Md Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali, and Mohammad Shoyaib. 2019. Can a simple approach identify complex nurse care activity?. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. 736–740.Google ScholarDigital Library
- Jennifer R Kwapisz, Gary M Weiss, and Samuel A Moore. 2011. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter 12, 2 (2011), 74–82.Google ScholarDigital Library
- Oscar D Lara and Miguel A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15, 3 (2012), 1192–1209.Google Scholar
- Sozo Inoue; Paula Lago; Shingo Takeda; Alia Shamma ; Farina Faiz; Nattaya Mairittha; Tittaya Mairittha. 2019. Nurse Care Activity Recognition Challenge. https://doi.org/10.21227/2cvj-bs21Google Scholar
- Hitoshi Matsuyama, Takuto Yoshida, Nozomi Hayashida, Yuto Fukushima, Takuro Yonezawa, and Nobuo Kawaguchi. 2020. Nurse care activity recognition challenge: a comparative verification of multiple preprocessing approaches. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 414–418.Google ScholarDigital Library
- Dionicio Neira-Rodado, Chris Nugent, Ian Cleland, Javier Velasquez, and Amelec Viloria. 2020. Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition. Sensors 20, 7 (2020), 1858.Google ScholarCross Ref
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