Turning Homes into Low-Cost Ambient Assisted Living Environments

Turning Homes into Low-Cost Ambient Assisted Living Environments

Alexiei Dingli (University of Malta, Malta), Daniel Attard (University of Malta, Malta) and Ruben Mamo (University of Malta, Malta)
Copyright: © 2012 |Pages: 23
DOI: 10.4018/jaci.2012040101
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Abstract

Today motion recognition has become more popular in areas like health care. In real-time environments, the amount of information and data required to compute the user’s motion is substantial, while the time to collect and process this information are crucial parameters in the performance of a motion recognition system. The nature of the data determines the design of the system. One important aspect of this system is reducing the delay between sensing and recognising a motion, while achieving acceptable levels of accuracy. The detection of humans in images is a challenging problem. In this paper, the authors present a solution using the Kinect, a motion sensing input device by Microsoft designed for the Xbox 360 console, to create an Ambient Assisted Living (AAL) application which monitors a person’s position, labels objects around a room, takes voice input, and raises alerts in case of falls. The authors present a number of modules like converting Kinect Skeletal Data to allow mouse control via hand movement, building a Finite State Machine (FSM), obtaining pose information, voice commands to allow interaction with the application, and face detection and recognition. The authors use different algorithms to achieve the required outcome.
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1. Literature Review

Today the aging population is rising every year (United Nations, 2002) and the need to assist elder people in a better life style using ambient assisted living (AAL) devices is also increasing and is being achieved by means of advanced technology. Recently a number of such devices have been designed and developed, based on video cameras, computer audio and vision systems with quite promising results. However, for the field to reach maturity, a number of challenges need to be tackled including the development of robust systems in the real-world which are easy to use and accepted by the society, users and carers (Salah, Gevers, Sebe, & Vinciarelli, 2011). Various AAL products are available on the market, which can be described as information and communication technology based products, systems and services that provide elder and vulnerable people with a better life style and a secure environment. As outlined by the EU commission, the stakeholders’ needs are categorised in three categories; AAL for persons (home and mobile), AAL in community and AAL at work. The technologies on which the applications and functionalities are based on

  • Sensing – exist in almost all AAL applications from wearable products to build in the environment sensors (such as home, vehicles, public areas, etc.)

  • Reasoning – is the knowledge about the daily activities of the user, or an abnormal activity such as in an emergent situation.

  • Acting – can be considered as the systems and services, which proactively act to assist and monitor the user in the assisted environment.

  • Interacting – Intelligent interfaces supported by networks and computers will be surrounding humans and machines.

  • Communicating – AAL systems can also be able to communicate between each other and being able to communicate within centralised services (AALIANCE, http://www.aaliance.eu).

AAL technologies include the use of home embedded sensors and networks, body worn sensors, robots and implants. Recently the industry is showing a growing interest in video and computer vision based solutions and this because of the fact that such products are always getting cheaper to be produced with cameras and sensors are being integrated on the semiconductor itself. In this paper we will be discussing the personal living in the home. The major risks faced by older people are falls. It is estimated that about one third of the elderly living in the community experience at least one fall each year (James, Eldemire, Gouldbourne, & Morris, 2007), with a thirty percent of the people aged 65 or older falling each year (Bell & Talbot-Stern, 2000; Gillespie, Gillespie, Robertson, Lamb, Cumming, & Rowe, 2003; Fabien, Deepayan, Charith, & Mark, 2011). A suitable definition of a fall according to Gibson et al. is “Unintentionally coming to the ground or some lower level and other than as a consequence of sustaining a violent blow, loss of consciousness, sudden onset of paralysis as in stroke or an epileptic seizure” (Gibson, Andres, Isaacs, Radebaugh, & Worm-Petersen, 1987).

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