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project : SIMBAD

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SIMBAD (Smart Inactivity Monitor using Array Based Detectors)

Oct 1999 - May 2001

This project was a collaborative effort involving the University of Manchester, British Telecom, Infrared Integrated System Ltd. (IRISYS), and the Institute of Human Ageing at the University of Liverpool. Its aim was to construct a human fall detector for use in sheltered accommodation units. The system used a low-resolution infrared sensor produced by IRISYS to monitor a room. When an occupant of the room fell to the ground, the system would automatically summon medical assistance. British Telecom provided the experience in telecare systems needed to implement the system commercially. The University of Liverpool provided sites for field-testing, and information on the dynamics of falls in the elderly. The role of the University of Manchester was to design algorithms capable of discriminating between falls and normal movements.

Fractures, particularly of the hip, due to falls at home are a siginificant cause of death in the elderly, leading to a clear requirement for a system that can summon medical assistance to such incidents. With the over-60 population of the UK expected to grow by as much as 65% over the coming 30 years, the world market for this type of device is expected to exceed 10 million units within 7 years. Current systems used in this application area include CCTV cameras, which are highly intrusive and requires expensive 24hr monitoring, or personal panic buttons, which rely on the user retaining concsiousness. The SIMBAD system would be completely autonomous, and would avoid intrusiveness by using a low-resolution sensor.

The role of the University of Manchester was to design the algorithms required to discriminate falls from non-fall movements. An actress was recruited to perform a series of falls and non-falls, such as sitting or lying down, which were recorded using both the infra-red detector provided by IRISYS and a colour video camera. A colour segmentation alogrithm was developed to detect the movements of the actress in the colour video sequences, and to calculate the velocity of her centre of mass. A separate algorithm was developed to determine the velocity of her centre of mass from the infrared images. The colour video data was then used as a gold-standard in order to train a neural network to discriminate between the fall and non-fall movements. The performance of the system was evaluated using a novel implementation of ROC curves. The final system was then delivered to IRISYS for field trials.

In addition, early work during this project led to the development of the non-parametric image subtraction algorithm, and to further research into the statistical applications of uniform probabiltiy distributions.


Developers Paul A. Bromiley email: paul.bromiley@man.ac.uk
Neil Thacker email: neil.thacker@man.ac.uk


This project was funded by the MEDLINK program, grant no. P169, supported by the UK Departments of Health, the Engineering and Physical Sciences Research Council, and the Medical Research Council.


This colour segmentation and non-parametric image subtraction algorithms produced during this project are now available in the TINA machine vision software.


Several Tina Memos describe aspects of this work:

2001-015 Colour Image Segmentation by Non-parametric Density Estimation in Color Space.

2002-004 A Novel Method for Non-Parametric Image Subtraction: Identification of Enhancing Lesions in Multiple Sclerosis from MR Images.

Publications resulting from this work

Bromiley, P.A., Thacker, N.A., and Courtney, P.
Non-parametric Image Subtraction using Grey Level Scattergrams.
Image and Vision Computing, 20, pp 609-617, 2002.

Bromiley, P.A., Pokric, M., and Thacker, N.A.
Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction.
MIUA 2002, Portsmouth, 2002.

Bromiley, P.A., Pokric, M., Thacker, N.A., and Jackson, A.
Detection of MS Lesions in MRI Scans using Non-Parametric Image Subtraction,
ISMRM 2002, Honolulu, Hawaii, 2002.

Bromiley, P.A., Courtney, P., and Thacker, N.A.,
Design of a Visual System for Detecting Natural Events by the use of an Independent Visual Estimate: a Human Fall Detector.
In Series on Machine Perception and Artificial Intelligence Vol. 50: Empirical Evaluation Methods in Computer Vision.
H.I. Christensen and P.J. Philips (eds.).
World Scientific Publishing, 2002, ISBN 981-02-9453-5.

Bromiley, P.A., Thacker, N.A., and Courtney, P.
Colour Image Segmentation by Non-Parametric Density Estimation in Feature Space.
Proc. BMVC 2001, Manchester, 2001.

Bromiley, P.A., Courtney, P., and Thacker, N.A.
A Case Study in the use of ROC curves for Algorithm Design.
Proc. BMVC 2001, Manchester, 2001.

Bromiley, P.A., Thacker, N.A., and Courtney, P.
Non-parametric Image Subtraction for MRI.
Proc. MIUA 2001, Birmingham, 2001.

Bromiley, P.A., Thacker, N.A., and Courtney, P.
Non-parametric Image Subtraction using Grey-Level Scattergrams.
Proc. British Machine Vision Conference, Bristol, 2000.

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