Labiba Gillani Fahad

TL;DR: Cognitive Assessment of Smart Home Resident (CA-SHR) is proposed to measure the ability of smart home residents in executing simple to complex activities of daily living using pre-defined scores assigned by a neuropsychologist and improves the reliability of the CA- SHR through the correct assignment of labels to the smart home resident compared with existing techniques.

TL;DR: This paper proposes a two level classification approach for activity recognition by utilizing the information obtained from the sensors deployed in a smart home, and applies a computationally less expensive learning algorithm Evidence Theoretic K-Nearest Neighbor.

TL;DR: A comprehensive evaluation of the proposed approach on two publicly available CASAS smart home datasets demonstrates its ability in the activity recognition and the correct identification of anomalies.

TL;DR: This work proposes a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments for multi-class activity classification, and demonstrates a better performance of the proposed approach compared to the state of the art.

TL;DR: An approach to monitor the change in the daily routine of a person living in a smart home using the long term analysis of the activities performed, where daily routine is the group of activities that can be performed in a single day and are repeated over a period of time.