Abstract
In order to reduce the burden on family caregivers, machine-assisted estimation of situations (called “caregiving contexts”) necessary for the care of elderly people at home is becoming increasingly important. Older adults at home usually express their feelings through nonverbal information such as facial expressions, movements, and postures, except in daily conversation. This study aims to examine a method for estimating the caregiving context based on extracting nonverbal information from older adults at home. Our key idea is to input real-time image data captured by a USB camera into a pre-trained model that can be run in an edge environment and subject the results to analysis that aggregates a set of features for each location in the home. We expect that the results of this research will allow us to build a classifier of caregiving contexts unique to each household and to analyze better and infer the caregiving needs of the elderly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-khafajiy, M., et al.: Remote health monitoring of elderly through wearable sensors. Multimedia Tools Appl. 78(17), 24681–24706 (2019). https://doi.org/10.1007/s11042-018-7134-7
Ansor, A., Ritzkal, R., Afrianto, Y.: Mask detection using framework tensorflow and pre-trained CNN model based on raspberry pi. Jurnal Mantik 4(3), 1539–1545 (2020)
Chen, S., Nakamura, M.: Designing an elderly virtual caregiver using dialogue agents and WebRTC. In: 2021 4th International Conference on Signal Processing and Information Security (ICSPIS), pp. 53–56. IEEE (2021)
Chen, S., Nakamura, M.: Developing a facial identification system using pre-trained model and spoken dialogue agent. In: 2022 International Balkan Conference on Communications and Networking (BalkanCom), pp. 62–67. IEEE (2022)
Chen, S., Ozono, H., Nakamura, M.: Integration analysis of heterogeneous data on mind externalization of elderly people at home. In: Gao, Q., Zhou, J. (eds.) HCII 2022. LNCS, vol. 13331, pp. 197–209. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05654-3_13
Chen, S., Saiki, S., Nakamura, M.: Evaluating feasibility of image-based cognitive APIs for home context sensing. In: 2018 International Conference on Signal Processing and Information Security (ICSPIS), pp. 1–4. IEEE (2018)
Chen, S., Saiki, S., Nakamura, M.: Proposal of home context recognition method using feature values of cognitive API. In: 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 533–538. IEEE (2019)
Chen, S., Saiki, S., Nakamura, M.: Integrating multiple models using image-as-documents approach for recognizing fine-grained home contexts. Sensors 20(3), 666 (2020)
Chen, S., Saiki, S., Nakamura, M.: Nonintrusive fine-grained home care monitoring: characterizing quality of in-home postural changes using bone-based human sensing. Sensors 20(20), 5894 (2020)
Chen, S., Saiki, S., Nakamura, M.: Toward flexible and efficient home context sensing: capability evaluation and verification of image-based cognitive APIs. Sensors 20(5), 1442 (2020)
Gatt, T., Seychell, D., Dingli, A.: Detecting human abnormal behaviour through a video generated model. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 264–270. IEEE (2019)
Ozono, H., Chen, S., Nakamura, M.: Study of microservice execution framework using spoken dialogue agents. In: 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 273–278. IEEE (2021)
Ozono, H., Chen, S., Nakamura, M.: Encouraging elderly self-care by integrating speech dialogue agent and wearable device. In: Gao, Q., Zhou, J. (eds.) HCII 2022. LNCS, vol. 13331, pp. 52–70. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05654-3_4
Sanchez, S., Romero, H., Morales, A.: A review: comparison of performance metrics of pretrained models for object detection using the tensorflow framework. In: IOP Conference Series: Materials Science and Engineering, vol. 844, p. 012024. IOP Publishing (2020)
Tamamizu, K., Sakakibara, S., Saiki, S., Nakamura, M., Yasuda, K.: Capturing activities of daily living for elderly at home based on environment change and speech dialog. In: Duffy, V.G. (ed.) DHM 2017. LNCS, vol. 10287, pp. 183–194. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58466-9_18
Wang, W., Hasabnis, N.: Distributed MLPerf ResNet50 training on Intel Xeon architectures with tensorflow. In: The International Conference on High Performance Computing in Asia-Pacific Region Companion, pp. 29–35 (2021)
Yang, L., Chen, S., Yao, A.: Semihand: semi-supervised hand pose estimation with consistency. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11364–11373 (2021)
Zeng, H.: An off-line handwriting recognition employing tensorflow. In: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 158–161. IEEE (2020)
Acknowledgements
This research was partially supported by JSPS KAKENHI Grant Numbers JP19H01138, JP20H05706, JP20H04014, JP20K11059, JP22H03699, JP19K02973, Grant-in-Aid for JSPS Research Fellow (No. 22J13217), and Tateishi Science and Technology Foundation (C) (No. 2207004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, S., Nakamura, M., Yasuda, K. (2023). A Study for Estimating Caregiving Contexts Based on Extracting Nonverbal Information from Elderly People at Home. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. HCII 2023. Lecture Notes in Computer Science, vol 14043. Springer, Cham. https://doi.org/10.1007/978-3-031-34917-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-34917-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34916-4
Online ISBN: 978-3-031-34917-1
eBook Packages: Computer ScienceComputer Science (R0)