This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about $0.02$ m.

This article is concerned with collecting stored sensor data from a static Wireless Sensor Network utilizing a group of mobile robots that act as data mules. In this scenario, the static sensor nodes’ locations are known a priori, and the overall optimization problem is formulated as a variation of the Traveling Salesman Subset-tour Problem (TSSP). The constraints that are taken into account include: (a) the pairwise distance between static nodes, (b) the maximum time interval between consecutive visits of each static node, (c) the service time that is required for the collection of the sensor data from the mobile element that visits this sensor node, and (d) the energy efficiency for the mobile nodes. The optimal mobile robot paths are computed using an enhanced Mobile Element Scheduling scheme. This scheme extracts the sequential paths of the mobile elements in an attempt to eliminate any sensor data loss.

This article focuses on the utilization of a mobile robot as data mule for collecting and transferring data from a wireless sensor system (WSN). Each static node within the WSN has its data generation rate resulting in an imposed inter-visit duration due to its hardware limitations. The mobile element/robot approaches the nodes, collects their stored data, and transfers these to a depot station. In the adopted scenario, the mobile robot assumes prior knowledge of the nodes' locations and the corresponding trajectories are extracted by solving a combinatorial optimization problem that resembles that of Travelling Salesman Subset-tour Problem (TSSP). The resulting Mobile Element Scheduling (MES) scheme accounts for: the traveling distances between the static nodes, the maximum inter-visit duration for each node to avoid buffer overflow, the visiting/service time at each node and the energy consumption of the mobile robot. The presented simulation studies indicate the effectiveness of the overall optimization concept.

This article gives a comprehensive introduction to the field of Wireless Sensors and Controllers Network in industry by means of a laboratory-based course developed at the University of Patras - Greece. The parallel enrollment with theoretical and experimental tasks is the main direction of the corresponding educational process. Therefore, a series of lectures outline theoretical issues about networking in industry, emphasizing the integration of wireless technology. Furthermore, students' training over the concept of a hybrid network between a Wireless Sensor Network and a PLC one fulfills the objective of the course. The philosophy of this educational implementation is first pointed out, followed by the lectures' content and the laboratory practice.

This article focuses on the utilization of a mobile robot as data mule for collecting and transferring data from a ground wireless sensor system (WSN). For the case, where the nodes' communication is impeded either due to a non-connected node, or due to excessive retransmissions caused by significant packet losses, the effective transmission rate is reduced and the energy consumption for the RF-communication increases. In this case, the mobile element/robot approaches the nodes and collects data with a reduced packet loss rate resulted from the use of the proposed technique. The devised algorithm is implemented in the operating system Contiki. Then, the mobile element transfers the stored data to the desired destination. In the adopted scenario, the mobile element assumes prior knowledge of the nodes' locations and its trajectory is extracted by solving a combinatorial optimization problem that resembles that of Traveling Salesman Subset-tour Problem (TSSP). Furthermore, the system is capable of transferring large volumes of data between source nodes and destination one, taking into account: a) the reliable data transmission, b) the traveling distances between the static nodes, c) the visiting/service time at each node and, d) the energy consumption of the mobile robot. Finally, experimental studies indicate the efficiency in terms of reliable data transmission and transmission time among pairs of static and a mobile node. Subsequently, the effectiveness of the overall concept is validated by means of simulations.

This article presents the development of a custom designed motion capture system for assisting physicians in upper limb rehabilitation. The scope of the implementation is the reduction in costs and hospitalization time related with existing systems. The system architecture revolves around motion sensing nodes, each utilizing an Inertial Measurement Unit (IMU), mounted on the upper limb of a patient. A physical working prototype is implemented and evaluated using vision-based passive marker detection techniques. Additionally, a generic upper limb kinematic model, derived from robotic kinematic theory is developed. The parameterization of the model allows for modularity, since a variety of sensing modalities can be rapidly integrated. The combination of the theoretical and physical implementation are evaluated in a preliminary motion sensing scenario for the upper limb of a patient, to depict the feasibility of this approach.