A Behaviour Monitoring System (BMS) for Ambient Assisted Living

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.

UWB/BLE Tracking System for Elderly People Monitoring

“…Therefore, development of usable tracking system should be preceded by identification of the aim of tracking, the object to be tracked, and the place where the system would operate.Trace information can be used for different purposes. The most obvious is activity monitoring [2]. The collected data allows for determination of physical activity periods.…”

“…The influence of various diseases on gait disorders is discussed in detail in Reference [4]. If monitoring concerns persons with more advanced cognitive impairment stadia, the systems might be used to detect wandering patterns [5][6][7].Post processing of the collected data allows for comparing the person's behaviour with the assumed behaviour model [2]. The difference between the model and the current data may trigger alerts that are usually sent to monitored person's caregivers.…”

“…Trace information can be used for different purposes. The most obvious is activity monitoring [2]. The collected data allows for determination of physical activity periods.…”

“…Post processing of the collected data allows for comparing the person's behaviour with the assumed behaviour model [2]. The difference between the model and the current data may trigger alerts that are usually sent to monitored person's caregivers.…”

From AAL to ambient assisted rehabilitation: a research pilot protocol based on smart objects and biofeedback

“…AAL and IoT healthcare related researches as well seems to be more focused on the assessment of the health conditions of the people under monitoring, their behavior (Eisa and Moreira 2017), the adherence to medical treatments (Roy et al 2017), patients' empowerment toward a more conscious healthy lifestyle (Grace et al 2017), or improving their self-sufficiency (Poncela et al 2019). Moreover, most of the studies reported in literature are targeted to the adult and elderly population (Azimi et al 2017), while very few studies foster the effects of smart environments or smart objects on children.…”