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

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


Glenn Forbes

Lucy Fraser

Graeme Hamilton


Michael T. Cox

Peter Funk

Shahina Begum


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.


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:

Conference Name 26th International conference on case-based reasoning (ICCBR 2018)
Conference Location Stockholm, Sweden
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 9, 2018
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer
Pages 249-263
Series Title Lecture notes in computer science
Series Number 11156
Series ISSN 1611-3349
ISBN 9783030010805
Keywords Human activity recognition; Smart homes; Sensors
Public URL


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