Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition

There has been an increased interest in the development of models to identify and predict human activities. However, the sparsity of the data gathered from the sensory devices in an ambient living environment creates the challenge of representing activities accurately. Also, such data usually comprise arbitrary lengths of dimensions. Recurrent Neural Networks (RNNs) are one of the widely used algorithms in sequential modelling due to their ability to handle the arbitrary lengths of data. In an attempt to address the above challenges, this paper proposes a method of fuzzy feature representation with Bidirectional Long Short-Term Memory (Bi-LSTM) for human activities modelling and recognition. To obtain optimal feature representation, sensor data are fuzzified and the membership degrees represent the selected features which are then applied to the Bi-LSTM model for activity modelling and recognition. The learning capability of the Bi-LSTM allows the model to learn the temporal relationship in sequential data which is used to identify human activities pattern. The learned pattern is then utilised in the prediction of further activities. The proposed method is tested and evaluated using dataset representing Activity of Daily Living (ADL) for a single user in a smart home environment. The obtained results are also compared with existing approaches that are used for modelling and recognising human activities.