In this paper, we identify the spectrum opportunities for radio frequency (RF) energy harvesting through power density measurements from 350 MHz to 3 GHz. The field trials have been performed in Covilhâ by using the NAKDA-SMR spectrum analyser with a measuring antenna. Based on the identification of the most promising opportunities, a dual-band band printed antenna operating at GSM bands (900/1800) is proposed, with gains of the order 1.8-2.06 dBi and efficiency 77.6-84%. Guidelines for the design of RF energy harvesting circuits and choice of textile materials for a wearable antenna are also discussed. Besides, we address the guidelines for designing circuits to harvest energy in a scenario where a wireless body area network (WBAN) is being sustained by a TX91501 Powercasf® RF dedicated transmitter and a five-stage Dickson voltage multiplier responsible for harvesting the RF energy. The IRIS motes, considered for our WBAN scenario, can perpetually operate if the RF received power attains at least -10 dBm.

Field trials have been performed in Covilhã to identify the spectrum opportunities for radio frequency (RF) energy harvesting through power density measurements from 350 MHz to 3 GHz. Based on the identification of the most promising opportunities, a dual-band printed antenna was conceived, operating at GSM bands (900/1800), with gains of 1.8 and 2.06 dBi, and efficiency varying from 77.6 to 82%, for the highest and lowest operating frequency bands, respectively. In this paper, guidelines for the design of RF energy harvesting circuits and choice of textile materials for a wearable antenna are briefly discussed. Besides, we address the development and experimental characterization of three different prototypes of a five-stage Dickson voltage multiplier (with and without impedance matching circuit) responsible for RF energy harvesting. All the three prototypes (1, 2 and 3) can power supply the sensor node for RF received powers of 2 dBm, -3 dBm and -4 dBm, and conversion efficiencies of 6, 18 and 20%, respectively.

The low average birth rate in developed countries and the increase in life expectancy have lead society to face for the first time an ageing situation. This situation associated with the World’s economic crisis (which started in 2008) forces the need of equating better and more efficient ways of providing more quality of life for the elderly. In this context, the solution presented in this work proposes to tackle the problem of monitoring the elderly in a way that is not restrictive for the life of the monitored, avoiding the need for premature nursing home admissions. To this end, the system uses the fusion of sensory data provided by a network of wireless sensors placed on the periphery of the user. Our approach was also designed with a low-cost deployment in mind, so that the target group may be as wide as possible. Regarding the detection of long-term problems, the tests conducted showed that the precision of the system in identifying and discerning body postures and body movements allows for a valid monitorization and rehabilitation of the user. Moreover, concerning the detection of accidents, while the proposed solution presented a near 100% precision at detecting normal falls, the detection of more complex falls (i.e., hampered falls) will require further study.

The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users’ quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour. In this paper we present a wireless body area network architecture to recognize human movement, identify human postures and detect harmful activities in order to prevent risk situations. The WBAN was created using tiny, cheap and low-power nodes with inertial and physiological sensors, strategically placed on the human body. Doing so, in an as ubiquitous as possible way, ensures that its impact on the users’ daily actions is minimum. The information collected by these sensors is transmitted to a central server capable of analysing and processing their data. The proposed system creates movement profiles based on the data sent by the WBAN’s nodes, and is able to detect in real time any abnormal movement and allows for a monitored rehabilitation of the user.