Abstract
The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.
Highlights
Life expectancy is becoming higher and higher every year in most developed countries
This paper presents a novel approach for activity monitoring of the elderly to detect deviations from their daily routines
Many health problems and welfare issues are directly related to these deviations, it is a powerful tool for experts, caregivers and relatives
Summary
Life expectancy is becoming higher and higher every year in most developed countries. To the best of our knowledge, the first use of disaggregation of smart meter data for health monitoring purposes was carried out in [35] They performed an iterative time-dependent hidden Markov model to disaggregate appliances based on a priori knowledge of inhabitant’s activities. Instead of directly tracking ADLs by mapping event sequences into main activities, as in previous works [36,39], the indices of usage patterns are used to evaluate the performance and to detect whether the subject has deviated from their routine. In contrast to [23,24,36,39], the routine evaluation is completely based on NILM, the usage pattern is extracted from multiple appliances and uncertainty is modeled using the Dempster-Shafer Theory, which is commonly used in fusion sensor data.
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