Integration of discriminative and generative models for activity recognition in smart homes

Activity recognition in smart homes enables the remote monitoring of elderly and patients. In healthcare systems, reliability of a recognition model is of high importance. Limited amount of training data and imbalanced number of activity instances result in over-fitting thus making recognition models inconsistent. In this paper, we propose an activity recognition approach that integrates the distance minimization (DM) and probability estimation (PE) approaches to improve the reliability of recognitions. DM uses distances of instances from the mean representation of each activity class for label assignment. DM is useful in avoiding decision biasing towards the activity class with majority instances; however, DM can result in over-fitting. PE on the other hand has good generalization abilities. PE measures the probability of correct assignments from the obtained distances, while it requires a large amount of data for training. We apply data oversampling to improve the representation of classes with less number of instances. Support vector machine (SVM) is applied to combine the outputs of both DM and PE, since SVM performs better with imbalanced data and further improves the generalization ability of the approach. The proposed approach is evaluated using five publicly available smart home datasets. The results demonstrate better performance of the proposed approach compared to the state-of-the-art activity recognition approaches

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Exploring semantics in activity recognition using context lattices

Studying human activities has significant implication in human beneficial applications such as personal healthcare. This research has been facilitated by the development of sensor technologies in pervasive computing with a large quantity of observational data collected about environments and user actions. By mining these data, traditional machine learning techniques have made great progress in recognising activities, but due to the increasing number of sensors and complexity of activities, they are subject to feasibility and scalability. These techniques may benefit from the inclusion of semantic information about the nature and relationships of sensor data and activities being observed. We introduce a new data structure, the context lattice, which allows designers to capture and explore this sort of knowledge. We demonstrate how context lattices can be used to infer human activities with the inclusion of such knowledge. We present comprehensive evaluations of the system against two third-party smart-home data sets, and demonstrate that our approach compares favourably with traditional analytic techniques in many circumstances. We conclude with a discussion of the strengths and weaknesses of context lattices in activity recognition

Exploring semantics in activity recognition using context lattices

Studying human activities has significant implication in human beneficial applications such as personal healthcare. This research has been facilitated by the development of sensor technologies in pervasive computing with a large quantity of observational data collected about environments and user actions. By mining these data, traditional machine learning techniques have made great progress in recognising activities, but due to the increasing number of sensors and complexity of activities, they are subject to feasibility and scalability. These techniques may benefit from the inclusion of semantic information about the nature and relationships of sensor data and activities being observed. We introduce a new data structure, the context lattice, which allows designers to capture and explore this sort of knowledge. We demonstrate how context lattices can be used to infer human activities with the inclusion of such knowledge. We present comprehensive evaluations of the system against two third-party smart-home data sets, and demonstrate that our approach compares favourably with traditional analytic techniques in many circumstances. We conclude with a discussion of the strengths and weaknesses of context lattices in activity recognition