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Health care platforms rapidly shift toward the ICT-based solutions. In this context, a wide range of consumer electronics technologies come into play ranging from robotics, embedded systems, sensors, and communication infrastructures. Driven by such observations, the H2020 RADIO project set forward a service-oriented, easily expandable design paradigm that emphasizes on how heterogenous commercial off-the-shelf (COTS) ICT technologies can be used as enablers of existing and new health-care services. Such services focus on activities of daily life (ADL) monitoring algorithms, on facilitating the indoor everyday life activities of the end user emphasizing on energy multifaceted conservation and security provision. All software components produced during the RADIO project with relative guidelines are freely available through dedicated GitHub repository (https://github.com/RADIO-PROJECT-EU).

Medical care, supported by modern technology and methods, will play an important role in our global society. An increasing average age and the need to keep standards in care lead to the introduction of several solutions often described as ambient assisted living (AAL). However, most people feel uncomfortable with these solutions, describing a sense of secret observation. To alleviate this sense of secret observation, the sensor platform is placed in clear view of the end user. This is accomplished through the involvement of robotics in this domain, and both caregivers and the patient are offered support for several tasks. The Robots in Assisted Living Environments: Unobtrusive, Efficient, Reliable, and Modular Solutions for Independent Ageing (RADIO) project targets this topic but differs from other solutions by its specific concepts, which are especially the unobtrusiveness and inclusion into home automation. This article presents the RADIO approach with its solutions for combining robotics and home automation for use in AAL environments.

Explanation of a new resampling scheme which uses virtual particles.Analysis of computational performance of the particle filter in general.Implementation of parallel particle filter with proposed resampling scheme. Particle filters are able to represent multi-modal beliefs but require a large number of particles in order to do so. The particle filter consists of three sequential steps: the sampling, the importance factor, and the resampling step. Each step processes every particle in oder to acquire the final state estimation. A high number of particles leads to a high processing time, thus reducing the particle filters usefulness for real-time embedded systems. Through parallelization, the processing time can be significantly reduced. However, the resampling step is not easily parallelizable since it requires the importance factor of each particle. In this work, a resampling scheme is proposed which uses virtual particles to solve the parallelization problem of the resampling component. Besides evaluating its performance against the multinomial resampling scheme, it is also implemented on a Xilinx Zynq-7000 FPGA.

In industrial robotics, most robotic arms are designed to enable an analytic solution for motion control tasks. This design restricts the movement flexibility of robotic arms. Numerical methods are able to fulfill the motion control tasks without requiring design restrictions on the robotic arm. However, they require more processing time to acquire a solution. In this paper, we analyze and accelerate the inverse Jacobian, pseudo inverse Jacobian, gradient descent, and the Newton method through an FPGA design using high-level synthesis (HLS). The final hardware implementations were designed in order to provide a good balance between high performance and acceptable FPGA resource utilization.

Localization is one of the four pillars of the autonomous robotic control loop. In order to work in complete unknown indoor environments, the robot needs to map its surroundings. This is done via the simultaneous localization and mapping (SLAM) algorithm. However, the SLAM algorithm does not provide additional context to the generated map. If this information is required, it needs to be provided by the operator. With Bluetooth Low Energy (BLE) technology, position dependent information can be annotated to the generated map without operator input. BLE beacons need to be positioned at points of interest for the robot and then need to be localized. Because the BLE beacon broadcasts an ID, localization is based on the Received Signal Strength Indication (RSSI). This paper presents an approach to localize BLE beacons in the RADIO indoor environment. The robot has one BLE receiver which must be used cleverly in order to triangulate the BLE beacons position.

A system that localizes and identifies trolleys in the entry area of a distribution center is presented in this paper. The area is monitored by an RFID reading system (consisting of four antennae and a fixed RFID reader) and an inexpensive pair of webcams. The trolleys are equipped with a passive RFID tag and an optical marker. The system uses a Particle Filter to combine Received Signal Strength Indication (RSSI) data from the RFID system and image data from the webcams. We propose procedures to improve RFID and camera localization accuracy. The resulting system achieves an accuracy of less than 30 cm in static and the most likely dynamic scenarios.