Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

Abstract

Elders who live alone are vulnerable population at risk. Despite telemedicine and clinical home monitoring are effective to provide health care, accidents at home are still one of the major risk factors. Important efforts have been done in home monitoring systems to detect potential scenarios of risk. However, the complex technological interaction has a negative impact on the user and intrusive or wearable devices are not recommended. Sensorized houses solve this problem, recording the behaviour of elders at home without a direct interaction. However, the detection of health-risk scenarios is a complex issue, since the large amount temporal data registered by sensors must be correctly interpreted. In such a case, Artificial Intelligence techniques have been used to automatically identify abnormal behaviors. Due to the number false positives of these techniques, the human intervention is still necessary. We consider that a semiautomatic approach, abstracting and visualizing the recorded temporal information, is a more suitable approach. In this work, we propose a specialised visualization model that helps system supervisors to identify through visual mining techniques most common scenarios of risk. We illustrate our proposal in a specific home monitoring system.

Spatiotemporal data visualisation for homecare monitoring of elderly people

“…This work is an extension of a previous paper appeared in [9] that stated the formal aspects of the MTA model. This time we analyse the applicability of the model in a home monitoring system.…”

Engaging older adults in the visualization of sensor data facilitated by an open platform for connected devices

“…Le and colleagues used streamgraphs and radial plots to visualize longitudinal motion sensor activity while maintaining spatial location [11]. Juarez et al presented an alternative approach to visualizing activities in different locations of the house by using a model of multiple temporal axes [12]. Though the preceding literature provides insights into different approaches towards visualizing sensor data, they often presented the visualizations with a focus on clinical assessment.…”

Smart Health and Wellbeing

“…For example, fall detection, using radar signals and video monitoring, has been studied widely [Dai et al 2013]. Recent research has also investigated how to utilize robots, sensors, the Internet, and mobile technologies for personalized care [Juarez et al 2013;Rashidi and Cook 2013]. Most of these projects aim to assist the elderly and chronic patients so that they can take care of themselves more independently and increase their quality of life, while reducing healthcare costs at the same time.…”