What is happening right now?

Information on occupancy is significant in constructing intelligent buildings for smart and automated energy optimized for air-conditioning, ventilation, heating systems, and also smart lighting system. Therefore, countless experimentation are being done in order to supply automatic solutions for managing energy usage by syncing the information on occupancy to the control system [1]. By controlling the load applied to the system, a significant total of energy can be conserved. However, occupant sensing is still a highly-cost and complicated system [2]. It is still overflowed with problems such as easily intruded sensors that will cause false detection and constrained by a limited energy supply when powered by batteries [3].

Passive Infrared (PIR) sensor, radio-frequency identification (RFID) tag, ultrasonic sensor and microwave sensor are the most typical devices used for occupant sensing [4]. PIR sensors measure infrared (IR) light radiating from objects, which enable users to detect movement, that is why it is always used to detect whether an occupant has moved within the range of the sensor [5]. Next, radio-frequency identification (RFID) technology has been used to read and capture information stored on a tag attached to the occupant.

Each time an occupant passes through the system, it will calculate and recognize occupants in real-time precisely [6]. Lastly, ultrasonic and microwave sensor emit sound waves and microwaves, respectively and detect occupancy by analyzing the frequency of the received waves [7].

What is the problem?

Nowadays, most of the occupant sensing system uses PIR motion sensor because it is cheap and readily available. It is very good at detecting moving objects. But there are some cases which they cannot detect the users’ presence if they sit still or not moving at all, for instance, sleeping [8]. It might cause discomfort to the users when the lighting system suddenly turned off even though the users is still in the range of the sensor. Not to mention, most of ready-made sensors are powered by batteries, which can affect the sensor’s performance as the batteries going low in power [9].

Most of the time, users tend to buy the current conventional system because it is easy to install in a room. However, problems occurred when they’re trying to install throughout the whole house where they need to deploy lots of sensors to extend the sensing range. In some cases, they need to install several sensors just for the entrance of the house, not including windows and other doors inside of the house [10].

Right now, the problem is the current occupant sensing system is not really optimized so that the energy usage data acquisition and analysis can be done and shown to the users for monitoring purpose [11]. Even though the current system can control the electrical appliances automatically, it still has some disadvantages and lacks some element that involves the activity of the users towards the system [12].

What am I going to do?

Thus, I am going to invent an affordable electric potential sensor that can be used for non-intrusive occupant sensing for energy smart monitoring system. The developed sensor is made up of a signal antenna creating the head of the sensor, amplifying signal, and signal filtration. It detects the displacement current between the antenna and human.

The sensor’s head is a flat antenna with a thin rectangular flat, black surface. The sensor utilizes the transimpedance amplifier (TIA), a current-to-voltage converter, to amplify the current output and converts it into the voltage output. For the rest of the control system, an Arduino Nano V3.0, an Espresso Lite V2.0, a current sensor, a dual channel relay, and a USB-UART converter are being used to control the electricity passing through the system.

Furthermore, the results recorded indicate the amazing capability of the sensor in computing the equivalent signals of the human’s heart at distance up to 120 cm with no obstacle in between. In conclusion, the sensor is proven can be an alternative to the typically used sensor to detect moving and static occupants in the human sensing system.

The figure above shows the experimental setup of control system and the presence sensor installed within.

Shown in the above table is the readings in mV of recorded signals of single subject at various distance.

Shown in the above table is the readings in mV of recorded signals at fixed distance with various numbers of subjects present in the room.

Shown in the above table is the readings in Ampere of recorded current with various conditions of load.

Whom did I refer to?

Well you can refer to any of the list down here for more information. Goodluck!

[1]  A. E. Mahdi, L. Faggion, “New displacement current sensor for contactless detection of bio-activity related signals,” in Sensors and Actuators A: Physical. Elsevier, 2015, pp. 176-183.

[2]  E. Arens, C. Federspiel, D. Wang, and C. Huizenga, “How ambient intelligence will improve habitability and energy efficiency in buildings,” in Ambient intelligence. Springer, 2005, pp. 63–80.

[3]  J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse, “The smart thermostat: using occupancy sensors to save energy in homes,” in Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. ACM, 2010, pp. 211–224.

[4]  J. Scott, A. Bernheim Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, and N. Villar, “Preheat: controlling home heating using occupancy prediction,” in Proceedings of the 13th international conference on Ubiquitous computing. ACM, 2011, pp. 281–290.

[5]  L. P´erez-Lombard, J. Ortiz, and C. Pout, “A review on buildings energy consumption information,” Energy and buildings, vol. 40, no. 3, pp. 394–398, 2008.

[6]  M. Gupta, S. S. Intille, and K. Larson, “Adding gps-control to traditional thermostats: An exploration of potential energy savings and design challenges,” in Pervasive Computing. Springer, 2009, pp. 95–114.

[7]  M. Moghavvemi and L. C. Seng, “Pyroelectric infrared sensor for intruder detection,” in TENCON 2004. 2004 IEEE Region 10 Conference, vol. 500. IEEE, 2004, pp. 656–659.

[8] M. Nati, A. Gluhak, H. Abangar, and W. Headley, “Smartcampus: A user-centric testbed for internet of things experimentation,” in Wireless Personal Multimedia Communications (WPMC), 2013 16th International Symposium on. IEEE, 2013, pp. 1–6.

[9]  N. Murtagh, M. Nati, W. R. Headley, B. Gatersleben, A. Gluhak, M. A. Imran, and D. Uzzell, “Individual energy use and feedback in an office setting: A field trial,” Energy Policy, vol. 62, pp. 717–728, 2013.

[10]  T. A. Nguyen and M. Aiello, “Energy intelligent buildings based on user activity: A survey,” Energy and buildings, vol. 56, pp. 244–257, 2013.

[11]  W. Kleiminger, C. Beckel, and S. Santini, “Opportunistic sensing for efficient energy usage in private households,” in Proceedings of the Smart Energy Strategies Conference, vol. 2011, 2011.

[12]  W. Kleiminger, C. Beckel, T. Staake, and S. Santini, “Occupancy detection from electricity consumption data,” in Proceedings of the 5^th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, 2013, pp. 1–8.

[13]  X. Guo, D. Tiller, G. Henze, and C. Waters, “The performance of occupancy-based lighting control systems: A review,” Lighting Research and Technology, vol. 42, no. 4, pp. 415–431, 2010.

[14]  Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, and T. Weng, “Occupancy-driven energy management for smart building automation,” in Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building. ACM, 2010, pp. 1–6.