Representing temporal dependencies in human activity recognition.

Forbes, Glenn; Massie, Stewart; Craw, Susan; Fraser, Lucy; Hamilton, Graeme

Abstract

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier.

Citation

FORBES, G., MASSIE, S., CRAW, S., FRASER, L. and HAMILTON, G. 2019. Representing temporal dependencies in human activity recognition. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of the 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings, 2567. Aachen: CEUR-WS [online], pages 29-38. Available from: http://ceur-ws.org/Vol-2567/paper3.pdf

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19)
• Start Date: Sep 8, 2019
• End Date: Sep 12, 2019
• Acceptance Date: Jul 23, 2019
• Online Publication Date: Mar 4, 2020
• Publication Date: Mar 4, 2020
• Deposit Date: Apr 7, 2020
• Publicly Available Date: Apr 7, 2020
• Publisher: CEUR-WS
• Peer Reviewed: Peer Reviewed
• Pages: 29-38
• Series Title: CEUR workshop proceedings
• Series Number: 2567
• Series ISSN: 1613-0073
• Keywords: Human activity recognition; Temporal dependency; Smart homes; Sensors; Time-series data
• Public URL: https://rgu-repository.worktribe.com/output/891591
• Publisher URL: http://ceur-ws.org/Vol-2567/paper3.pdf