@inproceedings{c40eed6ab4b749e3b8af7f59643073aa,
title = "Sit-to-stand intention recognition",
abstract = "Sit-to-stand (STS) difficulties are common among elderly because of the decline of their cognitive capabilities and motor functions. The way to help is to encourage them to practice their own functions and to assist only at the point where they need during STS processes. The provision of such support requires the elderly{\textquoteright}s intention of standing up to be recognised and the amount of support as well as the moment when the support would be needed to be predicted. The research presented in this paper focuses on intention recognition as it is difficult due to uncertainties existing in STS processes and differences in individual{\textquoteright}s biomechanical features. This paper presents fuzzy logic based self-adaptive approach to the recognition of standing up intention from sensor signals that contain the uncertainties.",
keywords = "Intention recognition, Neural Networks, Robot, STS, Uncertainty handling, prediction, Fuzzy logic, Neural networks, Prediction",
author = "Dayou Li and Hang Lu and Renxi Qiu and Carsten Maple and Zuobin Wang",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",
year = "2021",
month = jan,
day = "23",
doi = "10.1007/978-981-33-6318-2\_8",
language = "English",
isbn = "9789813363175",
volume = "737",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "65--72",
editor = "Yi Wang and Kristian Martinsen and Tao Yu and Kesheng Wang",
booktitle = "Advanced Manufacturing and Automation X. IWAMA 2020.",
address = "Germany",
}