A sensor fusion technique between an infrared thermal sensor and a capacitive sensors system was applied to an indoor location system to be used in smart homes and assisted living environments.
Analyzing the two systems separately it can be seen how their strengths and weaknesses have been perfectly balanced with the sensor fusion approach.
The capacitive sensor method applied in the localization field can bring many advantages since it is tag-less, unobtrusive and privacy-aware. The installation is easy since plates can be attached on the wall and can be hidden behind some covers and become invisible and unobtrusive for the users. Moreover, it is cheap both because of the material it is made of and because of the low power consumption during the usage. The main problem with this kind of sensor is the sensitivity that changes with the distance. This leads to a low sensitivity area in the middle of the room if only capacitive sensors are used. Moreover, they are affected by different sources of noise that cannot be easily controlled. Above all drift affects most this kind of sensor.
The infrared thermal sensor suits well the purpose of indoor human lo-calization because it acquires images of the situation in the room without violating the privacy of the user. It is tag-less and unobtrusive and does not need a big effort to install it. However, it has uneven sensitivity: when a hu-man passes through the area covered by two different pixels the temperature value sensed is less than the temperature sensed when the human occupies the area covered by a pixel alone. Moreover considering 3 meters as the stan-dard height of a room’s ceiling, the floor area covered by an infrared sensor with the same sensitivity of the one used in this work, is around 2.5 m x 2.5
m, that means the border of a standard 3 m x 3 m room is not covered. Merg-ing the data of the two sensMerg-ing systems can create a much more accurate, sensitive and robust system:
• Capacitive drift can be corrected by the use of a sensor like the infrared one that is not affected by the same problem;
• The overall sensitivity range can be extended for both the systems: the lack of sensitivity of the infrared sensor at the borders of the room are balanced by the high sensitivity of the capacitive sensors in the proximity of the walls. In the same way, the lack of sensitivity of the capacitive sensors in the middle point of the room is compensated by the field of view area of the infrared one. In the parts where both systems have good sensitivity, the infrared non-homogeneous sensitivity when crossing pixels can be corrected by the capacitive ones.
• Infrared sensor parallax errors can be corrected by the capacitive sensor.
In conclusion, the sensor fusion approach can lead to an improvement in the system. In fact, robustness and reliability are increased by adding redundancy, spatial and temporal coverage are extended and resolution and uncertainty are improved.
To check the improvement obtained, it will be necessary to analyze the data through Neural Networks.
Should be compared the localization performance using first the data-set composed by data from only capacitive sensors, then only data from infrared sensors and at last a merge of the two systems. In this way, it will be clear in terms of accuracy and error the actual improvement that the sensor fusion can give to both the systems.
It will be possible to see if the errors and drifts from one of the two sides will be actually recognized in the overall system. For example, using the data-set obtained from the Experiment three it will be possible to see if the hot water bottle placed in the room during the experiment and sensed by the infrared sensor will be recognized as the presence of a person or if the capacitive sensor contributes will avoid this error.
Further improvements can be implemented in future work:
• The capacitive sensor system could be optimized both in hardware com-ponents and in processing techniques in order to reduce drift and noise level. This could lead to more accurate measurement and could also speed up the system by allowing to make the method applicable to more
realistic and dynamic contexts. Some improvements could also be done to reduce the power consumption for long-term service using either bat-tery or wireless power.
• Regarding the infrared sensor system, the Arduino board used for col-lecting and sending data from the sensor to the computer can be replaced with a customized circuit containing a microcontroller IC and an Xbee module. This would reduce the size of the system and improve the per-formances.
• The size of the experimental room could be increased beyond 3 m x 3 m:
the number of capacitive plates and infrared sensors could be increased in order to cover a bigger space and other sensors positioning strategies could be tested.
During this work, a lot of effort has been made to make everything work in the best way and to try to have a scientific approach to the problems encountered. The expectation is that this system will be enhanced to be ready and available to improve the lives of users, especially those who need care and assistance.