Simulation Tool: a key element of the measurement chain

Be able to measure physical quantities related to user life style is one of the most performant way to provide care assistance to elder people improving their quality of life and reducing health costs.

The miniaturization of the sensors, their low cost and the development of algorithm able to convert quantitative information in qualitative information make the challenge interesting and feasible. In this research work, a simplified measurement chain composed by PIR sensors and ML algorithms and derived from the real scenario proposed in eWare project is analyzed and presented. With the term measurement chain, we generally refer to the set of stages of a measuring instrument which process the information detected by the physical quantity in the study, and then present a result. The measurement itself could be represented as a quantitative or a qualitative information, [109]. Different stages composing the measurement chain can be distinguished:

41 - Sensors network implementation, related to the measurement that needs to be performed;

- Raw data acquisition;

- Data processing;

- Uncertainty analysis;

- Application.

Inside a measurement chain the key role is played by two elements: 1) the physical quantities involved (sensors, environments, etc.); 2) the huge number of possible configuration and combination of these elements together with algorithms used to provide high level information.

In smart homes and aging society scenario, the definition of the optimal configuration of the measurement chain would does not exist. It would require an enormous number of tests not feasible in terms of cost and time in the real life. In order to avoid waste, the focus is on the chance to have a simulation of the measurement chain and of its responses. The simulation tools play a key role inside this scenario. “The basic idea is to integrate the simulation of a sensorised apartment with human behaviour modelling based on constraint-based planning that produces a sequence of daily activities”, [108]. Once the simulated environments is defined in terms of type of sensors, number, configuration, the second step is given by the chance to simulate the human behavior inside the simulated environment, which will allow to generate the desired datasets and moreover, different kinds of algorithms, generally based on ML techniques, need to be implemented to extract features and quality information. Finally, the quality information obtained thanks to the simulation have to be used to correct the parameters involved in the real environment. A general idea of the measurement chain in a global scenario is reported below.

Figure 6: Measurement chain schematic view

42 Where the quantity delta, represents the uncertainty related to all quantities involved. It is possible to observe that result of the performed measurement will depend on a huge number of factors, such as:

1. The subject considered (sex, age, pathology, habits, culture, etc.);

2. The characteristics of the environment (number of rooms, rooms dimension, furniture, walk-on surface, etc.);

3. The sensors involved (typology, characteristics, disposition in the environment, number, etc.);

4. The acquisition phase of the signal;

5. The artificial intelligence used to analyze data;

6. The data interpretation.

Translating the previous diagram in the ad hoc case, the following structure can be obtained:

In Paragraph 3.3 the Figure 8 is going to be analyzed into details.

Each block affects the result of the measurement according to how it is employed inside the measurement chain. Every measurement is subject to some uncertainty. A measurement result is only complete if it is accompanied by a statement of the uncertainty. This value can come from the measuring instrument, from the environment, from the operator and/or from other sources that

Figure 7: Measurement chain block diagram

43 could directly or indirectly affects the measure. At the same time it can be augmented or reduced by a different configuration of the measurement chain considered. The first step to investigate in which measure the uncertainty of each block of the chain can affects the final output is approached in this research work in paragraph 3.4.

Inside a Health Smart Home scenario, the problem related to how a certain configuration of the measurement chain could affect the measured quantity and its uncertainty it results in the impossibility of repeating the same test in a huge number of configuration without generating an enormous growth in costs and development time.

Physical IEs are expensive to implement. This is due to sensor technology costs and to the construction of the physical environments [110, 111]. About environment construction, two aspects need to be considered: first, researchers may not be aware of the ideal configuration of sensors to reach a good level of performances without pre-testing several combinations. This testing phase would require considerable time and expense and could be not feasible in real-world implementations where significant alterations may create confusion, particularly when pathological subjects are considered, [112]. Secondly, the environment construction it is a time consuming process which requires considerable groundwork before commitment to the purchase of equipment for the environment construction [112]. These environments therefore lack of flexibility [111, 113] and could have limited scalability [111]. As a consequence of these costs and constraints, not all researchers have access to datasets produced by such environments. “Data collection from IEs is a time consuming process [110, 111] because of the nature of the monitored scenarios, which may require the collection of data for extended periods of time in order to capture events that demonstrate typical inhabitant behavior and changes in behavior over time”,

Figure 8: High cost solution

44 [108, 110, 111, 114]. Optimal testing of new approaches generally involves data collection from different scenarios under different circumstances. This could not be possible in a real smart environment due to the difficulty in recruiting suitable participants to test the different configurations [110, 111]. Additionally, there are regulatory limitations that must be adhered to during testing on human subjects [115]. Researchers are therefore looking for alternative methods of datasets generation. As already said, one of the most popular solution is offered from generation of synthetic sensor datasets through the use of simulated IEs, which allow to accelerate research in related areas [115]. These simulated environments offer the chance to facilitate the generation of a variety of sensor datasets, even larger than those carried out from physical IEs [115]. This allows researchers to quickly test or/and evaluate new algorithms and cost effectively [110,115] providing in the same time an increased level of control over data and environment itself. “The physical layout of environments including walls, doors and objects within the environment can be modified to test a range of use case scenarios”, [119]. The arrangement of sensors including type, number and position can be adjusted as often as required with no cost and little time and effort [116]. Researchers would have complete control over the environment and generated datasets [110], experiments could be re-run many times with small adjustments to the environment, testing algorithms under development [115]. The experiments can be restarted quickly and easily with minimal set up time [117]. The simulations allow control over environments which is not feasible in the real life, such as the manipulation of time though with easily generate months, years of datasets, [117]. Simulations may represent conceptual or already existent environments, indicating the impact of some adjustments to the environment and highlighting optimizations in sensor placement with no invasiveness or expense [112]. In this context, the simulation tool plays a key role to simulate how the measurement chain react under certain conditions and to know in advance which is the best physical configuration to perform the measurements in a real environment. The purpose to find a relationship between environments, sensors and ADLS can so be reached.

Environment

Simulation Phase ML and Data Processing Data Analysis and Results Interpretation

45