Ontology-based sensor fusion for activity recognition

Context-aware activity recognition systems are dealing with heterogeneous sensors and these sensors are providing data at different sampling rate and output forms. Wearable sensors such as accelerometers and gyroscopes provide fast and real-time raw data which has to be interpreted before being useful to the application. Whereas ambient sensors such as temperature, humidity or object-interaction…

Adaptive Sliding Window for Physical Activity Recognition

A sliding window with a fixed size is not an effective approach for activity recognition system. Misclassifications could still happen especially for transitional activities. This is due to the fact that the length of transitional activity signals varies depending on the time to complete the activity [1], [2]. To overcome the problem, the window size…

Enhancing ontological reasoning with uncertainty handling for activity recognition

Handling uncertainty is a challenge in activity recognition. Uncertainty can be due to sensor errors (e.g. run out of batteries, imprecise outputs, missing activations etc.) and communication failures. and variability in human activities. These issues may significantly influence the accuracy of activity recognition. Data-driven approaches use machine learning techniques such as Decision Tree, naïve Bayes…

Artificial Neural Network

Artificial Neural Network is a machine learning algorithm that is modeled loosely after the human brain. A neural network is a regression or classification model, represented by a network as shown in Fig. 1. The network composes of layers of artificial neurons or nodes. Each layer’s neurons are connected to adjacent or next layer’s neurons.…

Unsupervised Learning

In contrast to supervised learning, unsupervised learning deals with inputs only . This dataset is called unlabelled dataset. Since, we are dealing with inputs only the aim of unsupervised learning is to uncover the latent structure in data. For example, we want to know how many groups (classes) can we make out of the data?…

Supervised Learning

In supervised learning, we are given a training set consists of a set of input-output pairs, where is the number of samples. The inputs is a set of attributes or features which are stored in an matrix. In classification problems, the output where denotes the number of outputs. If , it is called binary classification…