FITsense: employing multi-modal sensors in smart homes to predict falls.

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

Abstract

As people live longer, the increasing average age of the population places additional strains on our health and social services. There are widely recognised benefits to both the individual and society from supporting people to live independently for longer in their own homes. However, falls in particular have been found to be a leading cause of the elderly moving into care, and yet surprisingly preventative approaches are not in place; fall detection and rehabilitation are too late. In this paper we present FITsense, which is building a Smart Home environment to identify increased risk of falls for residents, and so allow timely interventions before falls occurs. An ambient sensor network, installed in the Smart Home, identifies low level events taking place which is analysed to generate a resident’s profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident’s typical profile and to known “risky” profiles to allow evidence-driven intervention recommendations. Human activity recognition to identify ADLs from sensor data is a key challenge. Here we compare a windowing-based and a sequence-based event representation on four existing datasets. We find that windowing works well, giving consistent performance but may lack sufficient granularity for more complex multi-part activities.

Citation

MASSIE, S., FORBES, G., CRAW, S., FRASER, L. and HAMILTON, G. 2018. FITsense: employing multi-modal sensors in smart homes to predict falls. In Cox, M.T., Funk, P. and Begum, S. (eds.) Case-based reasoning research and development: proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Lecture notes in computer science, 11156. Cham: Springer [online], pages 249-263. Available from: https://doi.org/10.1007/978-3-030-01081-2_17

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 26th International conference on case-based reasoning (ICCBR 2018)
• Start Date: Jul 9, 2018
• End Date: Jul 12, 2018
• Acceptance Date: May 21, 2018
• Online Publication Date: Oct 9, 2018
• Publication Date: Nov 8, 2018
• Deposit Date: Jul 6, 2018
• Publicly Available Date: Oct 10, 2019
• Print ISSN: 0302-9743
• Electronic ISSN: 1611-3349
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 249-263
• Series Title: Lecture notes in computer science
• Series Number: 11156
• Series ISSN: 1611-3349
• ISBN: 9783030010805
• DOI: https://doi.org/10.1007/978-3-030-01081-2_17
• Keywords: Human activity recognition; Smart homes; Sensors
• Public URL: http://hdl.handle.net/10059/2994
• Contract Date: Jul 6, 2018