Journal of Ambient Intelligence and Smart Environments

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.

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.

With a rapidly aging population worldwide, developing alternatives to enhance current cognitive screening practices is becoming increasingly important. The Clock Drawing Test, a paper-and-pencil test, has been used as one of the most popular cognitive screening tools for dementia. In this paper, we present our approach to developing a home-based computerized dementia screening tool, the ClockMe System, which we developed based on our observational study of the current practice of dementia screening at a clinic. The ClockMe System has two main parts: The ClockReader Application and the ClockAnalyzer Application. By using the ClockReader Application, older adults can self-administer dementia screening at home. The ClockAnalyzer Application enables medical practitioners to review and monitor the screening results of their patients. We conclude our paper with preliminary user evaluation results and suggestions for future implementation. The study shows the potential of computing technologies that can advance the current practice of dementia screening.

This paper presents a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user's state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin, resume, or end tasks. The objective of the system is to help the user maintain a daily schedule of activities while minimizing interruptions from questions or prompts. The model is built upon an option-based hierarchical POMDP. Options can be programmed and customized to specify complex routines for prompting or questioning.

The paper proposes a heuristic approach to solving the POMDP based on a dual control algorithm using selective-inquiry that can appeal for help from the user explicitly when the sensor data is ambiguous. The dual control algorithm is working effectively in the unified control model which features the adaptive option and robust state estimation. Simulation results show that the unified dual control model achieves the best performance and efficiency comparing with various alternatives. To further demonstrate the system's performance, lab experiments have been carried out with volunteer actors performing a series of carefully designed scenarios with different kinds of interruption cases. The results show that the system is able to successfully guide the agent through the sample schedule by delivering correct prompts while efficiently dealing with ambiguous situations.

This paper presents an approach to structuring knowledge and reasoning for high-level interpretation of sensor data in e.g. independent living applications. The main contribution is to use generalized events, described in terms of ‘space-time chunks’, as a unifying and simplifying structuring principle. We use reasoning with ontologies and rules in combination with a database system, and also incorporate numerical computation. We show that an easy to use modeling formalism is obtained, and that reasoning is feasible at the time of service request, by using R-entailment, which enables efficient exploitation of ontologies and rules in the presence of RDF data. Two applications were built and evaluated using the approach described in this paper, both of which are related to monitoring well-being of elderly people, and both of which use simple, low-cost sensors.