AirCode: Unobtrusive Physical Tags for Digital Fabrication

We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper

Real-time Gesture Recognition Using RFID Technology

This paper presents a real-time gesture recognition technique based on RFID technology. Inexpensive and unintrusive passive RFID tags can be easily attached to or interweaved into user clothes. The tag readings in an RFID-enabled environment can then be used to recognize the user gestures in order to enable intuitive human-computer interaction. People can interact with large public displays without the need to carry a dedicated device, which can improve interactive advertisement in public places. In this paper, multiple hypotheses tracking is used to track the motion patterns of passive RFID tags. Despite the reading uncertainties inherent in passive RFID technology, the experiments show that the presented online gesture recognition technique has an accuracy of up to 96%

Serious game for physical rehabilitation: measuring the effectiveness of virtual and real training environments

Recent advances in low-cost natural user interfaces such as Microsoft Kinect and Leap Motion controller allow the Virtual Reality implementation of 3D serious games for, posture, upper limb and lower limb rehabilitation purposes. However, it is very important to compare the results obtained by the users that train in virtual and real environments. This paper presents a virtual reality serious game for upper limb rehabilitation using a natural user interface expressed by Leap Motion controller. One of the developed virtual reality serious game for rehabilitation is converted to a real scenario with the same elements and rules and the same aims of physical rehabilitation. In order to extract appropriate information from the serious game based on real objects a RFID technology was used together with software components developed in LabVIEW. The evaluation of hand muscles' activity during the training session is based on the usage of thermography that permits to measure in an unobtrusive way the distribution of the temperature on the hands' level. Based on analysis of thermographic images obtained before and after serious game practice, the level of activity of specific muscles associated with training for virtual and real scenario is extracted. Experimental results that are also included in the paper underline the effectiveness of the proposed method for the comparison of the training in virtual and real scenarios.info:eu-repo/semantics/acceptedVersio

Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces

Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service (S2aaS) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications

Real-time Gesture Recognition Using RFID Technology

This paper presents a real-time gesture recognition technique based on RFID technology. Inexpensive and unintrusive passive RFID tags can be easily attached to or interweaved into user clothes. The tag readings in an RFID-enabled environment can then be used to recognize the user gestures in order to enable intuitive human-computer interaction. People can interact with large public displays without the need to carry a dedicated device, which can improve interactive advertisement in public places. In this paper, multiple hypotheses tracking is used to track the motion patterns of passive RFID tags. Despite the reading uncertainties inherent in passive RFID technology, the experiments show that the presented online gesture recognition technique has an accuracy of up to 96%

Cognizance of Vehicle Position and Moving using UHF RFID Tags

The cognizance means to detect the moving position of a robot at the particular point. In this method, the detection is to be done with the help of radio-frequency identification (RFID) tags. RFID tags are used in this method are of ultrahigh frequency (UHF). The indoor environmental area where different goods are distributed this method would be useful there. The RFID reader with identical configuration has been attached to a robot which is used to identify the location with the help of RFID tags. The signal received from RFID reader is used to acknowledge the accurate location and to give the direction to robot to move further at end point. This method proves the effectiveness in accurately estimating the vehicle position and giving the direction up to the last point. DOI: 10.17762/ijritcc2321-8169.15070

RFID Label Tag Design for Metallic Surface Environments

This paper describes a metal mount RFID tag that works reliably on metallic surfaces. The method proposes the use of commercial label type RFID tags with 2.5 mm thick Styrofoam103.7 with a relative permittivity of 1.03 attached on the back of the tag. In order to verify the performance of the proposed method, we performed experiments on an electric transformer supply chain system. The experimental results showed that the proposed tags can communicate with readers from a distance of 2 m. The recognition rates are comparable to those of commercial metallic mountable tags

Wearable sensors for respiration monitoring: a review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version