Sensor-based activity monitoring systems promise to prolong independent living of frail elderly people, including those affected by cognitive disorders. Different solutions are already available on the market, which use wireless sensors installed in the home to track the daily living routines of the senior. Those systems provide caregivers with statistics about detected activities; some of them may trigger real-time notifications when they identify a risk situation. Long-term monitoring of finegrained behavioral anomalies can be an important tool to support the diagnosis of neurodegenerative diseases. However, current commercial systems can only monitor high-level activity routines. For this reason, in a previous work we devised a novel method to recognize fine-grained abnormal behaviors of elderly people at home based on sensor data. Experiments in the lab showed the effectiveness of that method. In this paper we present our experience about the implementation of the system in the home of an elderly person with diagnosis of mild cognitive impairment. After illustrating the current implementation, we discuss preliminary results and outline research directions. In particular, a preliminary clinician's assessment indicates the potential utility of this system to support the diagnosis, and the benefits that would be gained by extending the system to monitor additional parameters, including neurovegetative aspects and motor behavior. We also discuss directions for addressing the encountered technological issues, for improving our reasoning algorithms with more extensive support of uncertainty, and for 'closing the loop' by making the senior an active part of the system.

From lab to life: Fine-grained behavior monitoring in the elderly's home

RIBONI, DANIELE;
2015

Abstract

Sensor-based activity monitoring systems promise to prolong independent living of frail elderly people, including those affected by cognitive disorders. Different solutions are already available on the market, which use wireless sensors installed in the home to track the daily living routines of the senior. Those systems provide caregivers with statistics about detected activities; some of them may trigger real-time notifications when they identify a risk situation. Long-term monitoring of finegrained behavioral anomalies can be an important tool to support the diagnosis of neurodegenerative diseases. However, current commercial systems can only monitor high-level activity routines. For this reason, in a previous work we devised a novel method to recognize fine-grained abnormal behaviors of elderly people at home based on sensor data. Experiments in the lab showed the effectiveness of that method. In this paper we present our experience about the implementation of the system in the home of an elderly person with diagnosis of mild cognitive impairment. After illustrating the current implementation, we discuss preliminary results and outline research directions. In particular, a preliminary clinician's assessment indicates the potential utility of this system to support the diagnosis, and the benefits that would be gained by extending the system to monitor additional parameters, including neurovegetative aspects and motor behavior. We also discuss directions for addressing the encountered technological issues, for improving our reasoning algorithms with more extensive support of uncertainty, and for 'closing the loop' by making the senior an active part of the system.
9781479984251
Computer networks and communications; Computer science applications; Computer vision and pattern recognition; Human-computer interaction; Health (social science)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/195192
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