Flexible dual-diversity wearable wireless node integrated on a dual-polarised textile patch antenna

A new textile wearable wireless node, for operation in the 2.45 GHz industrial, scientific and medical (ISM) band, is proposed. It consists of a dual-polarised textile patch antenna with integrated microcontroller, sensor, memory and transceiver with receive diversity. Integrated into a garment, the flexible unit may serve for fall detection, as well as for patient or rescue-worker monitoring. Fragile and lossy interconnections are eliminated. They are replaced by very short radiofrequency signal paths in the antenna feed plane, reducing electromagnetic compatibility and signal integrity problems. The compact and flexible module combines sensing and wireless channel monitoring functionality with reliable and energy-efficient off-body wireless communication capability, by fully exploiting dual polarisation diversity. By integrating a battery, a fully autonomous and flexible system is obtained. This novel textile wireless node was validated, both in flat and bent state, in the anechoic chamber, assessing the characteristics of the integrated system in free-space conditions. Moreover, its performance was verified in various real-world conditions, integrated into a firefighter garment, and used as an autonomous body-centric measurement device

Compact personal distributed wearable exposimeter

A compact wearable personal distributed exposimeter (PDE) is proposed, sensing the power density of incident radio frequency (RF) fields on the body of a human. In contrast to current commercial exposimeters, our PDE, being composed of multiple compact personal wearable RF exposimeter sensor modules, minimizes uncertainties caused by the proximity of the body, the specific antenna used, and the exact position of the exposimeter. For unobtrusive deployment inside a jacket, each individual exposimeter sensor module is specifically implemented on the feedplane of a textile patch antenna. The new wearable sensor module's high-resolution logarithmic detector logs RF signal levels. Next, on-board flash memory records minimum, maximum, and average exposure data over a time span of more than two weeks, at a one-second sample period. Sample-level synchronization of each individual exposimeter sensor module enables combining of measurements collected by different nodes. The system is first calibrated in an anechoic chamber, and then compared with a commercially available single-unit exposimeter. Next, the PDE is validated in realistic conditions, by measuring the average RF power density on a human during a walk in an urban environment and comparing the results to spectrum analyzer measurements with a calibrated antenna

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks

Synchronous wearable wireless body sensor network composed of autonomous textile nodes

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system

Wireless Health Monitoring using Passive WiFi Sensing

This paper presents a two-dimensional phase extraction system using passive WiFi sensing to monitor three basic elderly care activities including breathing rate, essential tremor and falls. Specifically, a WiFi signal is acquired through two channels where the first channel is the reference one, whereas the other signal is acquired by a passive receiver after reflection from the human target. Using signal processing of cross-ambiguity function, various features in the signal are extracted. The entire implementations are performed using software defined radios having directional antennas. We report the accuracy of our system in different conditions and environments and show that breathing rate can be measured with an accuracy of 87% when there are no obstacles. We also show a 98% accuracy in detecting falls and 93% accuracy in classifying tremor. The results indicate that passive WiFi systems show great promise in replacing typical invasive health devices as standard tools for health care.Comment: 6 pages, 8 figures, conference pape