Summary of the 2
            <sup>
              <i>nd</i>
            </sup>
            nurse care activity recognition challenge using lab and field data

Alia, Sayeda Shamma; Lago, Paula; Adachi, Keiichiro; Hossain, Tahera; Goto, Hiroki; Okita, Tsuyoshi; Inoue, Sozo

doi:10.1145/3410530.3414611

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…• Data labelling problems 14.5% [127], [103], [105], [128], [129], [110], [112], [113], [114], [130], [115], [116], [131], [132], [117], [133], [134], [120], [135], [122], [136], [125] Noise/Imbalanced data set…”

Section: Annotationsmentioning

confidence: 99%

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Alam,

McChesney,

Nicholl

et al. 2023

IEEE Sensors J.

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Huge amounts of data is generated with the emergence of new sensors technologies. Human Activity Recognition (HAR) data sets are generated from cameras, such as video or still images, capturing human behaviour through sensors like gyroscope, Bluetooth, sound sensors, and accelerometers. These generated data sources are collected by the researchers and formed into open data sets. However, these data sets often show issues during data set construction, sharing, searching, that could produce further challenges for reuse of the data by others. The main objective of this research is to explore the current issues and challenges faced by researchers in the HAR domain. A detail literature review was conducted to extract information from the published literature. Similarly, a questionnaire survey was sent to selected researchers having expertise in the HAR domain and who work with open data sets. The main issues and challenges were identified and classified into a hierarchical structure. This research will help HAR researchers to be aware of the current issues and challenges in the field of HAR open data sets. It will help to promote important attributes applicable to many open data sets, such as privacy, anonymity, platform maintenance, data sets descriptions, metadata, environmental conditions, resources, and training, while constructing and sharing new data sets.

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Section: Annotationsmentioning

confidence: 99%

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Alam,

McChesney,

Nicholl

et al. 2023

IEEE Sensors J.

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“…The second nurse care activity recognition challenge [8] used both lab and realworld data for activity recognition. This partially solved the weakness of the first challenge as it reduces the gap between lab simulation and the real world.…”

Section: Related Workmentioning

confidence: 99%

Predicting User-specific Future Activities using LSTM-based Multi-label Classification

Irbaz¹,

Ahsan²,

Lota³

2022

Preprint

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User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses. It is challenging because, unlike other domains, activities in healthcare involve both nurses and patients, and they also vary from hour to hour. In this paper, we employ various data processing techniques to organize and modify the data structure and an LSTM-based multi-label classifier for a novel 2-stage training approach (user-agnostic pre-training and user-specific fine-tuning). Our experiment achieves a validation accuracy of 31.58%, precision 57.94%, recall 68.31%, and F1 score 60.38%. We concluded that proper data pre-processing and a 2-stage training process resulted in better performance. This experiment is a part of the "Fourth Nurse Care Activity Recognition Challenge" by our team "Not A Fan of Local Minima".

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“…In addition, such activities often have imbalanced data such as the number of data between classes and the duration of actions. Due to this background, there is a difference between activity recognition using data collected in real nursing facilities [18] and activity recognition using data collected in an environmentally controlled laboratory [3,13].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Feature Importances for Automatic Generation of Care Records

Kaneko

Hossain

Inoue

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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The aging society is becoming a more and more serious problem in developed countries. In Japan, the aging society is also becoming a serious problem same as other developed countries. As a result, the shortage of caregivers is expected to be a severe problem. For this reason, Information and Communication Technology (ICT) and other technologies are expected to be used to reduce the workload of caregivers. In our laboratory, we have developed a nursing care record application which used for recording the nursing care activities and nursing care records. In this paper, to improve the estimation accuracy of nursing care records, we analyze the nursing care record data and machine learning model using feature importance and data visualization. We proposed 10 new features e.g., previous amount of breakfast, previous amount of lunch, previous amount of dinner, snacking, level of requiring long-term care, sleep time on the previous day, amount of exercise on the day, physical condition on the day, weekday, and weather. Afterwards, we analyze the trends of the care record data and estimation results based on these new features. The evaluation of the average accuracy was 77.3% and the average F1 score of 49.8%. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.

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Summary of the 2 ^nd nurse care activity recognition challenge using lab and field data

Cited by 10 publications

References 4 publications

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Predicting User-specific Future Activities using LSTM-based Multi-label Classification

Analysis of Feature Importances for Automatic Generation of Care Records

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Summary of the 2 nd nurse care activity recognition challenge using lab and field data

Cited by 10 publications

References 4 publications

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Predicting User-specific Future Activities using LSTM-based Multi-label Classification

Analysis of Feature Importances for Automatic Generation of Care Records

Contact Info

Product

Resources

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Summary of the 2 ^nd nurse care activity recognition challenge using lab and field data