Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that effective sound activity detection can produce significant gains in classification accuracy and that further gains can be made using source separation methods based on independent component analysis. Encouragingly, the results show that recognition accuracies in the range 70%–100% can be consistently obtained using different microphone-pair positions, under all but the most severe noise conditions. 1. Introduction 1.1. Home Telemonitoring The devotion to one’s parents has been displayed in various cultures throughout time and, as such, has usually included the care of one’s parents in old age. In colonial times, the care of frail aged persons was primarily the responsibility of the family. Today in the West, elderly people are primarily cared for in hospitals, nursing homes, or by their families. The United Nations predicts that by 2100, 28.1% of the world population will be aged 65 years or older, compared with 10.0% in 2000 and 6.9% in 1990 [1]. The resulting increased demand on the health system, coupled with decreasing taxpayer support and fewer younger people to care for the elderly, will introduce significant pressure on aged care services. The main needs of these services are monitoring and supporting elderly people. For example, the fifth highest cause of death for elderly people is falls [2], and while the falls themselves may not be easily preventable, in many circumstances the deaths following them are, with appropriate monitoring and support. Consequently, significant research interest has been focused towards home-telecare solutions allowing elderly people to live safely and independently in their homes. In recent years, it has been suggested that sound “signatures” are well suited to automated telemonitoring of elderly people and superior to video cameras from the perspective of privacy [3]. Telemonitoring using sound signatures is a relatively less explored area in the literature, in comparison with other techniques such as gait parameters and
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