Twin Meander Coil

Energy-efficient and unconstrained wearable sensing platforms are essential for ubiquitous healthcare and activity monitoring applications. This paper presents Twin Meander Coil for wirelessly connecting battery-free on-body sensors to a textile-based reader knitted into clothing. This connection is based on passive inductive telemetry (PIT), wherein an external reader coil collects data from passive sensor coils via the magnetic field. In contrast to standard active sensing techniques, PIT does not require the reader to power up the sensors. Thus, the reader can be fabricated using a lossy conductive thread and industrial knitting machines. Furthermore, the sensors can superimpose information such as ID, touch, rotation, and pressure on its frequency response. However, conventional PIT technology needs a strong coupling between the reader and the sensor, requiring the reader to be small to the same extent as the sensors' size. Thus, applying this technology to body-scale sensing systems is challenging. To enable body-scale readout, Twin Meander Coil enhances the sensitivity of PIT technology by dividing the body-scale meander-shaped reader coils into two parts and integrating them so that they support the readout of each other. To demonstrate its feasibility, we built a prototype with a knitting machine, evaluated its sensing ability, and demonstrated several applications.

Ubiquitous healthcare on smart environments: A systematic mapping study

The number of elderly is increasing in recent years. According to the United Nations (UN), in 2050 there will be 2.1 billion people above 60 years of age worldwide. In addition, the World Health Organization (WHO) reported that more than 1 billion people live with some form of disability, the leading causes of which are chronic diseases and accidents. Thus, many opportunities for the application of smart environments to support ubiquitous healthcare are emerging, the benefits of which may be reflected in reduced medical costs and increased convenience of patients and families. This systematic mapping study aims to identify how smart environments have been applied to support ubiquitous healthcare, what techniques and technologies are being used, and what research gaps are still left unexplored. Eight scientific repositories were used to search for papers in the area of ubiquitous healthcare, and a filtering process was used to remove bias. Of an initial sample of 1706 studies, 49 were reviewed entirely, analyzed, and categorized. Among these, we highlight those oriented to monitoring, detection, notification, and action on situations that may cause illnesses or promote the improvement of people’s health and wellness. Technologies to support ubiquitous healthcare were categorized into three groups: ambient sensors, wearables, and social robotics. These technologies have been applied most frequently to support the elderly and disabled. The diseases most commonly cited were dementia, diabetes, Alzheimer’s, autism, obesity, mental stress, sleep disorders, asthma, epilepsy and chronic diseases. We found only three papers that used prediction models. Finally, we observed a trend of using social robotics to improve the intelligence of ambient, aggregating mobility, and acting.