Erik Dovgan

An agent-based approach to care in independent living

This paper presents a multi-agent system for the care of elderly people living at home on their own, with the aim to prolong their independence. The system is composed of seven groups of agents providing a reliable, robust and flexible monitoring by sensing the user in the environment, reconstructing the position and posture to create the physical awareness of the user in the environment, reacting to critical situations, calling for help in the case of an emergency, and issuing warnings if unusual behavior is detected. The system has been tested during several on-line demonstrations.

Data management in the MIRABEL smart grid system

Nowadays, Renewable Energy Sources (RES) are attracting more and more interest. Thus, many countries aim to increase the share of green energy and have to face with several challenges (e.g., balancing, storage, pricing). In this paper, we address the balancing challenge and present the MIRABEL project which aims to prototype an Energy Data Management System (EDMS) which takes benefit of flexibilities to efficiently balance energy demand and supply. The EDMS consists of millions of heterogeneous nodes that each incorporates advanced components (e.g., aggregation, forecasting, scheduling, negotiation). We describe each of these components and their interaction. Preliminary experimental results confirm the feasibility of our EDMS.

Behavior Analysis Based on Coordinates of Body Tags

This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the users activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.

A Multi-Agent Care System to Support Independent Living

This paper presents a context-aware, multi-agent system called “Confidence” that helps elderly people remain independent longer by detecting falls and unusual movement, which may indicate a health problem. The system combines state-of-the-art sensor technologies and four groups of agents providing a reliable, robust, flexible monitoring system. It can call for help in case of an emergency, and issue warnings if unusual behavior is detected. The first group gathers data from the location and inertial sensors and suppresses noise. The second group reconstructs the position and activity of a person and detects the context. The third group assesses the persons condition in the environment and reacts to critical situations such as falls. The fourth group detects unusual behavior as an indicator of a potential health problem. The system was successfully tested on a scenario consisting of events that were difficult to recognize as falls, as well as in a scenario consisting of normal days and days when the perso...

Evolutionary scheduling of flexible offers for balancing electricity supply and demand

To address the needs of rapidly changing energy markets, an energy data management system capable of supporting higher utilization of renewable energy sources is being developed. The system receives flexible offers from producers and consumers of energy, aggregates them on a regional level and schedules the aggregated flexible offers to balance forecast energy supply and demand. This paper focuses on formulating and solving the optimization problem of scheduling aggregated flexible offers within such a system. Three metaheuristic scheduling algorithms (a randomized greedy search, an evolutionary algorithm and a hybrid between the two) tailored to this problem are introduced and their performance is assessed on a benchmark test problem and two realistic problems. The best results are achieved by the evolutionary algorithms, which can efficiently handle thousands of aggregated flex-offers.

Confidence: ubiquitous care system to support independent living

The Confidence system aims at helping the elderly stay independent longer by detecting falls and unusual movement which may indicate a health problem. The system uses location sensors and wearable tags to determine the coordinates of the users body parts, and an accelerometer to detect fall impact and movement. Machine learning is combined with domain knowledge in the form of rules to recognize the users activity. The fall detection employs a similar combination of machine learning and domain knowledge. It was tested on five atypical falls and events that can be easily mistaken for a fall. We show in the paper and demo that neither sensor type can correctly recognize all of these events on its own, but the combination of both sensor types yields highly accurate fall detection. In addition, the detection of unusual movement can observe both the users micro-movement and macro-movement. This makes it possible for the Confidence system to detect most types of threats to the users health and well-being manifesting in his/her movement.

Comparing a multiobjective optimization algorithm for discovering driving strategies with humans

When a person drives a vehicle along a route, he/she optimizes two objectives, the traveling time and the fuel consumption. Therefore, the task of driving can be viewed as a multiobjective optimization problem and solved with appropriate optimization algorithms. The comparison between the driving strategies obtained by humans and those obtained by the algorithms is interesting from several points of view. For example, it is interesting to see which strategies are better. To perform the human versus machine test, we compared the driving strategies obtained by the multiobjective optimization algorithm for discovering driving strategies (MODS) with those obtained by a group of volunteers operating a vehicle simulator. The test was performed using data from three real-world routes. The results show that MODS always finds better driving strategies than the volunteers, especially when the fuel consumption is to be reduced. Moreover, the results show that some volunteers always drive similarly in terms of traveling time and fuel consumption while others significantly vary their driving strategies.

A multiobjective optimization algorithm for discovering driving strategies

This paper presents a deterministic multiobjective optimization algorithm for discovering driving strategies. The goal is to find a set of nondominated driving strategies with respect to two conflicting objectives: time and fuel consumption. The presented multiobjective algorithm is based on the breadth-first search algorithm and Nondominated Sorting Genetic Algorithm (NSGA-II). Experiments on a 10-km route show that the results significantly depend on the discretization of the search space.

A Multi-Agent System for Remote Eldercare

This paper presents a case study in which a multi-agent system for care of the elderly people living at home alone is applied in order to prolong their independence. The system consists of several agents organized in groups providing robust and flexible monitoring, calling for help in the case of an emergency and issuing warnings if unusual behavior is detected. The first results and demonstrations show promising performance.

Discovering driving strategies with a multiobjective optimization algorithm

When driving a vehicle along a given route, several objectives such as the traveling time and the fuel consumption have to be considered. This can be viewed as an optimization problem and solved with the appropriate optimization algorithms. The existing optimization algorithms mostly combine objectives into a weighted-sum cost function and solve the corresponding single-objective problem. Using a multiobjective approach should be, in principle, advantageous, since it enables better exploration of the multiobjective search space, however, no results about the optimization of driving with this approach have been reported yet. To test the multiobjective approach, we designed a two-level Multiobjective Optimization algorithm for discovering Driving Strategies (MODS). It finds a set of nondominated driving strategies with respect to two conflicting objectives: the traveling time and the fuel consumption. The lower-level algorithm is based on a deterministic breadth-first search and nondominated sorting, and searches for nondominated driving strategies. The upper-level algorithm is an evolutionary algorithm that optimizes the input parameters for the lower-level algorithm. The MODS algorithm was tested on data from real-world routes and compared with the existing single-objective algorithms for discovering driving strategies. The results show that the presented algorithm, on average, significantly outperforms the existing algorithms.