A Behaviour Monitoring System (BMS) for Ambient Assisted Living
Detecting abnormal behavior
Science & Technology
in-house monitoring of older adults; Ambient Assisted Living; learning mobility routine at home; detecting abnormal behaviour
learning mobility routine at home
Chemical technology
Ambient Assisted Living
Arrhythmias, Cardiac
TP1-1185
02 engineering and technology
Learning mobility routine at home
Article
3. Good health
in-house monitoring of older adults
Activities of Daily Living
0202 electrical engineering, electronic engineering, information engineering
Humans
detecting abnormal behaviour
Detecting abnormal behaviour
In-house monitoring of older adults
Algorithms
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Monitoring, Physiologic
DOI:
10.3390/s17091946
Publication Date:
2017-08-24T12:44:54Z
AUTHORS (2)
ABSTRACT
Unusual changes in the regular daily mobility routine of an elderly person at home can be an indicator or early symptom of developing health problems. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and permanence habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensors’ data streams and compute sensor-driven features that describe the daily mobility routine of the elderly as part of the developed Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different user’s mobility profiles at home, and also with a real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder’s care.
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