Two Articles Published in Journal of Ambient Intelligence and Smart Environments

Two articles were published in a recent thematic issue Home-based Health and Wellness Measurement and Monitoring in Journal of Ambient Intelligence and Smart Environments.

Analysis of Daily-Living Dynamics
Authors: B. Kaluža and M. Gams

Abstract: An analysis of daily-living behavior is an important approach to assess the wellbeing of an elderly person that lives at home alone. This paper presents an approach to monitoring an individual in the home environment by an ambient-intelligence system in order to detect anomalies in daily-living patterns. The proposed method is based on transforming the sequence of posture and spatial information using a novel matrix presentation to extract spatial-activity features. Then, an outlier-detection method is used for a classification of the individual’s usual and unusual daily patterns regardless, of the cause of the problem, be it physical or mental. Experiments indicate that the proposed algorithm successfully discriminates between the daily behavior patterns of a healthy person and those with health problems.
DOI: 10.3233/AIS-2012-0164

Home-based health monitoring of the elderly through gait recognition
Authors: B. Pogorelc and M. Gams

Abstract: In Europe, in particular, growing numbers of elderly people need sustainable elderly care, which the young are not able to provide. As an alternative, elderly care can be provided through home-based, automatic, health-monitoring systems. Here we propose data-mining algorithms in a system for the automatic recognition of health problems, activities and falls through the analysis of gait. The gait of the elderly is captured using a motion-capture system and the resulting time series of position coordinates are analyzed with a data-mining approach in order to classify it into five health states: 1) normal, 2) with hemiplegia, 3) with Parkinson’s disease, 4) with pain in the back and 5) with pain in the leg, or into five activities/falls: 1) accidental fall, 2) unconscious fall, 3) walking, 4) standing/sitting, 5) lying down/lying. We propose and analyze four data-mining approaches: 1) CML – Classical machine-learning approach with raw sensor data, 2) SCML – Classical machine-learning approach with semantic attributes, 3) MDTW – Multidimensional dynamic time-warping approach with raw sensor data and 4) SMDTW – Multidimensional dynamic time-warping approach with semantic attributes. According to the results of the experiments, SMDTW achieved the highest classification accuracy of the four proposed approaches, and transforming the raw data into the semantic attributes significantly improved the performance of the approaches. Since the observed health problems are related also to postural instability and danger of falling, their early detection helps to prevent elderly people from falling.
DOI: 10.3233/AIS-2012-0166