Name
Abstract | ||
|---|---|---|
A challenging key aspect of recognising and modelling human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL). This paper provides a new method based on Fuzzy Finite State Machine (FFSM) and Long Short-Term Memory (LSTM) neural network for modelling and recognising human activities. The learning capability in the LSTM allows the system to learn the relations in the temporal data to identify the parameters of the rule-based system through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system's states. Experimental results are presented to demonstrate the effectiveness of the proposed approach.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3316782.3322781 | Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments |
Keywords | Field | DocType |
ADL, LSTM, activity of daily living, deep learning, fuzzy finite state machine, human activity modelling, long short-term memory | Fuzzy finite state machine,Computer science,Fuzzy logic,Long short term memory,Human–computer interaction,Temporal database,Artificial intelligence,Deep learning,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-4503-6232-0 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gadelhag Mohmed | 1 | 1 | 0.36 |
| Ahmad Lotfi | 2 | 88 | 20.21 |
| Amir Pourabdollah | 3 | 46 | 13.27 |