AlarmNet

A MySQL database serves as a back-end data store for the entire system.  It is located in a PC connected to the backbone of the system. It stores all the information coming from the infrastructure for longitudinal studies and offline analysis.

Back-end analysis programs monitor behavioral deviations over long periods of time, helping to identify changes which might signal the advance of degenerative disease and the need for increased care frequency.  An example is Alzheimers disease, in which a person may spend as much as 40% of night-time awake, and sleep frequently during the day (McCurry et al. 2000).

Circadian Activity Rhythms

We have developed a pattern mining and analysis program for the CAR called SAMCAD (software for automatic measurement of circadian activity deviation) that measures the rhythmic behavioral activity of patients and detects any behavioral changes within these patterns. The CAR algorithm embedded in SAMCAD is statistical and predictive. As applied to motion sensors, it enables us to continuously track resident movements, and to learn his/her life habits (presence-based CAR) and vivacity (activity-level based CAR).

Circadian Activity Rhythm Analysis GUI.

These presence and activity-level based CAR are respectively based on the distribution of the probability of user presence and the average number of motion events per room. CAR runs on the back-end of the system on a PC, and polls a database where patient activity is stored. CAR supports a GUI, shown at left, which displays various information related to the activity analysis, such as the number of abnormal time periods (under-presence or over-presence) or abnormal activity (hypo- or hyper-activity) that occurred per hour, room and day during day or night, and the length and dates of the stay of the resident.

Other graphs of the GUI display the main results of the CAR analysis. Data shown in the figure is from presence-based CAR in a real clinical case study for a healthy resident who stayed 25 days in an assisted-living facility. The first graph on the left displays the average time the user spends in every room each hour, calculated over the number of days of the stay of the resident. The second graph, on the right, indicates that after 18 days the presence based CAR for this patient has been learned. These graphs can provide a wealth of information about activity patterns such as the sleep/wake cycle, or some medical hints to the physician about some activities of daily living (ADLs) of the resident such as eating, hygiene and sleeping.

To use CAR for health monitoring, after the learning period, any statistically significant anomalies that are detected will alert physicians who can investigate the source of the trouble (sleep/wake period longer, one meal less, etc.) by focusing on the region of the anomaly as identified by CAR. The hypothesis is that behavioral changes could, in the long-term, reveal health decline or pathologies. This hypothesis was clinically validated in collaboration with the medical research group MARC (Medical Automation Research Center) at UVA. To validate this hypothesis, clinical behavioral patterns of older adults in assisted-living facilities were extracted from real data sets, and behavioral changes were studied by consulting the medical notebooks of the caregivers in charge of the monitored residents. See (VG03, VND02, and VAD 06) for details about the construction of the CAR (modeling) and clinical validation.