Visual monitoring of people in private spaces. From the “Big Brother” to the “Good Brother”Francisco Flórez-RevueltaFaculty of Science, Engineering and Computing, Kingston UniversityDigital Imaging Research CentreInterdisciplinary Hub for the Study of Health and Age-related conditions (IhSHA)

1. Introduction

Technology is gradually gaining acceptance as a means to complement the work of caregivers by monitoring and assisting persons with reduced physical or cognitive capacity in their day-to-day living. Ambient Assisted Living (AAL) environments make use of a variety of sensors that collect information from the environment or its dwellers. These sensors that monitor the persons and their interactions with other people or objects allow the recognition of activities of daily living or dangerous situations, e.g. falls.

While monitoring technology for public security is relatively mature, attention is now focussed on the use of sensing technology embedded in devices used to promote health and enhance wellbeing and safety. However, monitoring is often seen as intrusive and as violating rights to privacy. Acceptance of such technologies is low because they create a sense of Orwellian “Big Brother” surveillance.

Legal, ethical, and regulatory responsibilities require that data is protected in order to preserve privacy.

2. platform

The main objective of is to provide an ICT-based solution to support and relieve the burden experienced by informal carers (relatives, friends, neighbours…) and improve the quality of life of both the carer and the person who is being cared for. The platform is composed of two different modules, namely the AAL home system and the informal caregiver tool.

The AAL home system consists of a group of cameras and an array of sensors installed at the assisted person´s home and is responsible for collecting information on a daily basis and in an automatic way about the routine, moods, behaviour and activities of the assisted person.

The informal caregiver tool is based on a Web platform that provides, to the carer, guidance and support to improve their working conditions and enable, as a result, a better quality of care.

Activities and events to be detected:• Recognition of activities of daily living (ADL): wake-

up, going to the toilet, sleeping…• Activity level: light, moderate, intensive• Location and tracking of the persons in the home• Risky situations: fall detection, dangerous objects;

fire, flood, gas alarms… • Social activity: leaving home, receiving visits, use of

telephone…• Detection of basic and advanced activities:

cooking, watching TV…

3. Privacy by context

Privacy-Enhancing Technologies is a system of ICT measures protecting informational privacy by eliminating or minimising personal data thereby preventing unnecessary or unwanted processing of personal data, without the loss of functionality of the information system.

We propose a privacy-by-context paradigm for remote visual monitoring of assisted persons, following a level-based scheme to access video data (although it could be extended to any personal data) in order to protect privacy. Each level establishes the way in which the video stream is modified and displayed and, therefore, which protection degree is provided.

The context should provide enough information to empower the assisted persons to adapt privacy to their preferences, in such a way that they can decide who, how, and when their carers can watch them.

An individual can then select in advance a visualisation level to any situation that could be modelled by the context, i.e. giving different permissions to access the video stream to the various different carers in different ways according to what is happening at the assisted person’s home.

4. Visual privacy

We have implemented our level-based scheme considering eight visualisation models.

Original Blur Pixelating

Emboss Silhouette

Skeleton Avatar Invisilibity

This tests have been carried out using a RGB-D camera, i.e. Microsoft Kinect

5. Validation in real environments

Use of omnidirectional RGB cameras located onthe ceiling of each room

Activity Status of the assisted person

6. More information

Padilla-López, J.R.; Chaaraoui, A.A.; Gu, F.; Flórez-Revuelta, F. (2015). Visual privacy by context: proposal and evaluation of a level-based visualisation scheme. Sensors, 15(6), 12959-12982.

Padilla-López, J.R.; Chaaraoui, A.A.; Flórez-Revuelta, F. (2015). Visual privacy protection methods: A survey. Expert Systems With Applications, 42(9), 4177-4195.

Flórez-Revuelta, F.; Gu. F.; Pierscionek, B.; Remagnino, P. (2015). White paper on AAL systems and associated privacy issues. Public report, BREATHE Consortium.

Padilla-López, J.R.; Flórez-Revuelta, F.; Monekosso, D.N.; Remagnino, P. (2012). The “Good” Brother: Monitoring People Activity in Private Spaces. In Distributed Computing and Artificial Intelligence (pp. 49-56). Springer Berlin Heidelberg.

This work has been supported by the Ambient Assisted Living Joint Programme and Innovate UK under Project “BREATHE – Platform for self-assessment and efficient management for informal caregivers” (AAL-JP-2012-5-045).