Monitoring and identification of trends and abnormal behaviors using an AAL systems

Transcription

1 Monitoring and identification of trends and abnormal behaviors using an AAL systems A. Losardo 1, F. Grossi 2, G. Matrella 3, I. De Munari 4 and P. Ciampolini 5 Abstract Studies aimed at highlighting a key role of ICTs in Ambient Assisted Living environment are becoming more frequent. In such a context, by means of systems and non-invasive sensors, an improvement of safety and quality of life for elderly and disable is achieved, enabling to maintain a more independent lifestyle as long as possible. AAL systems may be exploited to provide useful tools for the recognition and the management of environmental anomalies, and can also extract behavioral information correlated to the users habits. CARDEA system [1], developed by TAU-Labs (Assistive Technology Lab at University of Parma) allows for elaborating a large amount of raw data, related to the Daily Living Activities. Through the data assessment, CARDEA is able to discriminate abnormal behavioral profile to provide feedback and warning to caregivers. Introduction To cope with demographic changes (due to well-known causes, like low nativity, increase in life expectancy, a different family approach to care issues) new models of welfare are needed. Despite a limited diffusion of AAL systems, living-labs and pilot sites experimental installations are spreading, aiming at assessing the suitability of an AAL approach in fostering an autonomous lifestyle for elderly people and people with disabilities, as well as at the sustainability of future welfare and care models. 1 Agostino Losardo, Università degli Studi di Parma, Viale G.P. Usberti 181/A, agostino.losardo@unipr.it 2 Ferdinando Grossi, Università degli Studi di Parma, Viale G.P. Usberti 181/A, ferdinando.grossi@unipr.it 3 Guido Matrella, Università degli Studi di Parma, Viale G.P. Usberti 181/A, guido.matrella@unipr.it 4 Ilaria De Munari, Università degli Studi di Parma, Viale G.P. Usberti 181/A, ilaria.demunari@unipr.it 5 Paolo Ciampolini, Università degli Studi di Parma, Viale G.P. Usberti 181/A, paolo.ciampolini@unipr.it

2 2 A. Losardo, F. Grossi, G. Matrella, I. De Munari, P. Ciampolini Among main difficulties in the diffusion of such AAL systems is the difficult users perception of the cost-benefit ratio. CARDEA system aims to minimize the cost and the intrusiveness of the installation providing, at the same time, a set of assistive functions possibly straightforwardly perceived as useful by the users and caregivers. A valuable feature to the caregiver is that of generating appropriate automatic warning to improve the quality of monitoring [2], presenting them in a form adequate to actual caregiver skills. By using environmental and wearable sensor, CARDEA can make an home smarter, at the same time providing caregivers with more deep insights and control capabilities. In this paper, examples of analysis carried out with CARDEA system are shown: exploiting raw data stored in the system database, algorithms have been developed to recognize change of habits which can be useful to point out to caregivers. Methods CARDEA is a system which encompasses home automation and assistive purposes within the same framework. By means of environmental and personal sensors, it allows for advanced control and monitoring functions, accessible either from local and remote (using a simple web interface) [3]. CARDEA system is installed, since several years, in a number of sheltered house located in the Parma Apennines. In these site, a large amount of raw data concerning event detected by the system are acquired and stored in a MySQL database. Data representation is fully abstracted, making the information completely independent of the actual hardware details of the sensors. The database content is then analyzed, in order to infer behavioral information. Significant changes in behavioral trend can be used as indicators of abnormal situations, calling for the caregiver s attention. For instance, by monitoring a person with a bed occupancy sensor, it is possible to assess the regularity of the wake-sleep (circadian) rhythm. Likewise, a Daily Living Activities profile can be worked out by exploiting environmental sensors and smart appliances. Below, an example of results carried out with CARDEA system are presented. They refer to a real settlement, in Neviano degli Ardini, occupied by a 90 years old lady. The analysis was based on the response of a simple PIR (Presence Infra-Red) sensor installed in the bathroom, already used to turn on the light automatically when a person enter in the bathroom.

3 Monitoring and identification of trends and abnormal behaviors using an AAL systems 3 Results In Fig.1, a 2-dimensional density map is presented. The color scale maps the activity intensity detected to PIR sensor during the hours of the day (on the vertical axis) along a two-years long period, beginning from February 2010 to November Cool color indicates a low activity whereas warm ones indicates a very high activity. The raw data were appropriately filtered to reduce noise and improve the quality of representation. Fig. 1: Density Activity Map (fading color means a clear decline) Behavior shown in Fig. 1 is quite easy to interpret: major intensity is observed at wake-up and bedtime. In the lower band of the figure, the color clearly fades, this indicating a marked decline in the activity. The caregiver, of course, may correlate such behavior with the actual health conditions and possibly account for that in daily care strategies. A more quantitative approach is illustrated in Fig. 2, which shows an outline of the activity intensity over time, obtained by integrating the sensor activity over each day. Also, partial sums related to the day- or night-activity are shown. Fig. 3, instead, illustrates the bathroom-access count, exhibiting, as expected, a similar trend. In the same figure, the error bar concerning measurement uncertainties and the frequency distribution are shown, supporting meaningfulness and reliability of extracted figures, despite the presence of a strong noise.

4 4 A. Losardo, F. Grossi, G. Matrella, I. De Munari, P. Ciampolini Fig. 2: Activity intensity over time (1,5 years) Frequency Access count Fig. 3: Bathroom accesses count

5 Monitoring and identification of trends and abnormal behaviors using an AAL systems 5 Given their format, however, such analytical data are hardly meaningful to formal and informal caregivers: a more expressive and intuitive way of presenting data to them is needed. To this purpose, automatic algorithms to recognize potential anomalous trend have been developed. In Fig. 4, the activity trend is shown, as worked out from the raw data in Fig. 2. The trend is estimated by evaluating the linear regression coefficient over a moving window. I.e., a first-order, averaged derivative is assumed as the trend indicator: positive values indicate an increase in activities, whereas negative values indicate a decrease. Figure 4: Activity trend estimate Eventually, three possible condition of interest have been devised: 1. Out of range condition occurs when the activity intensity figure exceeds given (customizable) thresholds, representing the bounds of a normal condition. 2. Too fast increase or decrease condition occurs when estimated speed of change exceeds given thresholds (Fig. 5) 3. Too long increase or decrease condition occurs when a steady increasing/decreasing activity trend is observed for a period exceeding a given duration. (Fig. 5)

6 6 A. Losardo, F. Grossi, G. Matrella, I. De Munari, P. Ciampolini When at least one of these conditions occurs the system may call for caregivers attention. Conclusions Figure 5: Trend warning conditions In this paper, a simple technique to extract behavioral trends from raw use data of an environmental control system is introduced. Despite the simplicity of the examples shown, the approach seems to be promising, aiming at providing caregivers with synthesis information, possibly relevant to the health status, in an accessible and intuitive format. The use of low cost and non-intrusive sensors allows for effectively exploiting behavioral analysis in live contexts, supporting the caregivers work and allowing them to appreciate behavioral changes which could remain unnoticed otherwise (night behaviors, for instance, or very slow declining trends). The approach can be straightforwardly extended to a wider set of sensors, thus enriching the system perspicacity and its inference ability. In particular, wearable sensors, besides being more invasive, may provide a much more detailed view of the user s personal activity, thus allowing for more accurate estimations. In conclusion, it is worth remarking that the proposed approach is not meant to provide automated diagnosis tools, but instead to provide physicians and caregivers with

7 Monitoring and identification of trends and abnormal behaviors using an AAL systems 7 tools supporting their diagnostic activity; close cooperation with caregiving persons is therefore needed: interpretation of warning is left to the caregivers, and the system relies on sensible configuration of threshold parameters, which need to be attained through the physician knowledge of user s health issues. Nevertheless, we believe this approach to be an inexpensive and effective way of making AAL benefits more tangible, besides the many further merits of AAL systems. Bibliography 1. F. Grossi, V. Bianchi, G. Matrella, I. De Munari, P. Ciampolini, An Assistive Home Automation and Monitoring System, ICCE 2008 Digest of Technical Papers, 341 (2008) 2. Rantz, M.J.; Skubic, M.; Koopman, R.J.; Phillips, L.; Alexander, G.L.; Miller, S.J.; Guevara, R.D., "Using sensor networks to detect urinary tract infections in older adults," e-health Networking Applications and Services (Healthcom), th IEEE International Conference on, vol., no., pp.142, A. Losardo, F. Grossi, G. Matrella, V. Bianchi, I. De Munari, P. Ciampolini, Web-enabled Home Assistive Tools, AAATE2011 Conference Proc., IOS Press, Assistive Technology Series, 448 (2011) 4. A. Losardo, F. Grossi, G. Matrella, I. De Munari, e P. Ciampolini, Monitoraggio indiretto dell attivita e del benessere di persone che vivono in ambienti intelligenti, AAL in Italia Primo libro bianco, pp , ISBN