Minimum Set Of Sensors Research Articles

A Minimal Set of Sensors in a Smart-Shirt to Obtain Respiratory Parameters

Smart-Shirts (and other wearable technologies) that provide vital medical data are becoming increasingly prevalent. However, obtaining accurate measurement of respiratory parameters via a Smart-Shirt requires ongoing research. In this study, various respiratory parameters have been captured via an optoelectronic plethysmograph and a body plethysmograph using an optimal set of spatial sensors. Sixty-four reflective markers were fixed on a compression shirt and different respiratory manoeuvres were performed by the subjects. In this analysis, Singular Value Decomposition was used to determine the minimum marker set required to yield accurate predictions of respiratory mechanics. Sufficient accuracy and precision for most clinical applications was able to be determined using positional data from nine markers. Using motions of nine sensors, the tidal volume can be predicted with a mean error of less than 139 ml and an adjusted R2 higher than 0.96. A subsequent linear regression analysis provides the location of the nine markers. These outcomes reduce the computational complexity of analysing optical based wearable technology, reducing barriers to further uptake.

Cost-effective routing as a service in sensor-cloud

Sensor-cloud is a collaborative platform which allows multiple wireless sensor networks (WSN) to pull their resources together for better utilisation and efficiency. In this work, we consider a set of applications, each characterised by the set of targets it intends to cover and our aim is to provide routing as a service (RaaS) to all these applications in the sensor-cloud. We should be able to run data gathering for each application in parallel with the minimum use of sensor-cloud resources. This is achieved by creating a collection of data gathering trees. We have proposed an algorithm which selects a minimal set of sensors to cover the required targets and obtains a spanning forest connecting them so that, the total energy cost of all the trees in the forest is minimised. Experimental results demonstrate the advantages of serving the applications by a sensor-cloud over standalone WSNs in terms of the usage of resources.

MakeSense: Automated Sensor Design for Proprioceptive Soft Robots.

Soft robots have applications in safe human-robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially.

Algorithms for joint sensor and control nodes selection in dynamic networks

The problem of placing or selecting sensors and control nodes plays a pivotal role in the operation of dynamic networks. This paper proposes optimal algorithms and heuristics to solve the Simultaneous Sensor and Actuator Selection Problem (SSASP) in linear dynamic networks. In particular, a sufficiency condition of static output feedback stabilizability is used to obtain the minimal set of sensors and control nodes needed to stabilize an unstable network. We then show that SSASP can be written as a mixed-integer nonconvex problem. To solve this nonconvex combinatorial problem, three methods based on (i) mixed-integer nonlinear programming, (ii) binary search algorithms, and (iii) simple heuristics are proposed. The first method yields optimal solutions to SSASP—given that some constants are appropriately selected. The second method requires a database of binary sensor/actuator combinations, returns optimal solutions, and necessitates no tuning parameters. The third approach is a heuristic that yields suboptimal solutions but is computationally attractive. The theoretical properties of these methods are discussed and numerical tests on dynamic networks showcase the trade-off between optimality and computational time.

Tele-monitoring and tele-rehabilitation of the shoulder muscular-skeletal diseases through wearable systems.

In this work we present the development and preliminary testing of a wearable-technology-enabled platform for the remote rehabilitation of a large number of shoulder muscular-skeletal diseases. The presented system (Shoulphy) is conceived to lead and assess the patient, wearing a minimal set of inertial sensors, through personalized physical rehabilitation programs under the remote supervision of the physician/therapist. We have introduced a minimal inertial sensor set and an associated biomechanical reconstruction method based on a bi-articular model of the shoulder. We have tested the system in classical shoulder rehabilitation exercises and we have demonstrated that the system is able to discriminate between correct and compensatory movement strategies.

Output Feedback Image-Based Visual Servoing of Rotorcrafts

This paper presents an improved output feedback based image-based visual servoing (IBVS) law for rotorcraft unmanned aerial vehicles (RUAVs). The control law enables a RUAV with a minimal set of sensors, i.e. an inertial measurement unit (IMU) and a single downward facing camera, to regulate its position and heading relative to a planar visual target consisting of multiple points. As compared to our previous work, twofold improvement is made. First, the desired value of the image feature of controlling the vertical motion of the RUAV is a function of other image features instead of a constant. This modification helps to keep the visual target stay in the camera’s field of view by indirectly adjusting the height of the vehicle. Second, the proposed approach simplifies our previous output feedback law by reducing the dimension of the observer filter state space while the same asymptotic stability result is kept. Both simulation and experimental results are presented to demonstrate the performance of the proposed controller.

Adaptive Visual Servoing of Unmanned Aerial Vehicles in GPS-Denied Environments

This paper presents an adaptive output feedback-based visual servoing law for a quadrotor unmanned aerial vehicle equipped with a single downward facing camera. The objective is to regulate the relative position and yaw of the vehicle to a planar target consisting of multiple points using a minimal sensor set, i.e., an inertial measurement unit and a vision sensor. A set of first order image moment features, defined in the image plane of a virtual camera with zero roll and pitch motion, is used for visual servoing. It has been observed in previous work that various system uncertainties, such as aerodynamics constants and attitude estimation bias, result in steady-state errors if not being compensated. By treating those uncertainties as unknown system parameters, an adaptive backstepping controller is developed. As the given visual servoing law is an output feedback controller, the translational velocity measurement from the global position system is not required. The asymptotic stability of the error dynamics is proven. Experimental results are provided to demonstrate the controller performance.

Optimizing Full Scale Dynamic Testing of Building Components: Measurement Sensors and Monitoring Systems

Experimental work to assess the thermal performance of complex building components often requires testing under real and therefore variable conditions. The PASLINK methodology developed during many years by a European network of outdoor test centres is a dynamic methodology that provides high quality data sets and makes it possible to study the thermal behaviour of tested building components.The test methodology defines the application of a “standard” set of sensors and instruments that are fixed for all experiments carried out in this type of test cells. These minimum set of sensors are the ones described by the PASLINK network quality assurance documentation. The test procedure also includes a calibration process of the test cells. Usually the specific component to be tested is also equipped with extra sensors to obtain specific information of the thermal behaviour of the sample. An example of the sensors over a specific roof test component will be also presented. Finally the data acquisition system requirements will be presented briefly, including both data logger and the software designed for the test control and data acquisition. They must fulfil all the requirements needed for an appropriate testing strategy and a reliable data handling.It is important to note that the requirements here expressed are valid for any type of test carried out to test the thermal behaviour of a building component under real climate but well controlled conditions. Traceability of all processes and data handling is indispensable for guaranteeing a high quality of the experimental work as well as the evaluation of the collected data by means of dynamic analysis methods.

Daily-life monitoring of stroke survivors motor performance: the INTERACTION sensing system.

The objective of the INTERACTION Eu project is to develop and validate an unobtrusive and modular system for monitoring daily life activities, physical interactions with the environment and for training upper and lower extremity motor function in stroke subjects. This paper describes the development and preliminary testing of the project sensing platform made of sensing shirt, trousers, gloves and shoes. Modular prototypes were designed and built considering the minimal set of inertial, force and textile sensors that may enable an efficient monitoring of stroke patients. The single sensing elements are described and the results of their preliminary lab-level testing are reported.

Online estimation of vehicle load and mass distribution for ground vehicles

The vehicle mass and its distribution on the vehicle is an important information for several applications, from safety-oriented control systems to fleet management in public transport, courier services, garbage collection and so on. Specifically, an online estimation of such quantities must be provided and the results should be available relying on a minimum set of sensors, to reduce costs and possibly avoid an ad-hoc instrumentation. This paper proposes a frequency-based data processing scheme to address this issue, which relies on vertical acceleration and speed angular velocity measurements only. The validity of the approach is assessed both on simulations in a multibody environment and on experimental data.

An open-source navigation system for micro aerial vehicles

This paper presents an open-source indoor navigation system for quadrotor micro aerial vehicles (MAVs), implemented in the ROS framework. The system requires a minimal set of sensors including a pl...

On Perimeter Coverage in Wireless Sensor Networks

Many sensor network applications require the tracking and the surveillance of target objects. However, in current research, many studies have assumed that a target object can be sufficiently monitored by a single sensor. This assumption is invalid in some situations, especially, when the target object is so large that a single sensor can only monitor a certain portion of it. In this case, several sensors are required to ensure a 360° coverage of the target. To minimize the amount of energy required to cover the target, the minimum set of sensors should be identified. Centralized algorithms are not suitable for sensor applications. In this paper, we describe our novel distributed algorithm for finding the minimum cover. Our algorithm requires fewer messages than earlier mechanisms and we provide a formal proof of correctness and time of convergence. We further demonstrate our performance improvement through extensive simulations.

Sensor location with respect to fault tolerance properties

This paper deals with the sensor location of non-linear systems with respect to fault tolerance properties. Non-linear observability and individual observability indices are introduced and used to build minimal and redundant sensor sets. These sets are organised into an oriented graph which contains all the possible reconfiguration paths for which the estimated variables remain observable. The evaluation of fault tolerance is based on fault tolerance properties: a structural and a probabilistic criteria, for which observability properties remain satisfied. The developed methodology is applied to a steam generator installation which represents complex non-linear system widely used in process engineering.

An Algorithm for Minimal Connected Cover Set Problem in Wireless Sensor Networks

Reducing power consumption to extend network lifetime is one of the most important challenges in designing wireless sensor networks. One promising approach to conserving system energy is to keep only a minimal number of sensors active and put others into low-powered sleep mode, while the active sensors can maintain the communication connectivity and cover the target region completely. The problem of computing such minimal active sensor set is NP-hard. In this paper, a centralized Voronoi tessellation (CVT) based approximate algorithm is proposed to construct a near optimal cover set of active sensors required to cover the target region completely. The communication graph induced by the cover set computed by CVT algorithm is connected if sensor’s communication radius is at least twice of its sensing radius. In case of sensor’s communication radius is smaller than twice of its sensing radius, a minimum spanning tree (MST) based connection algorithm is proposed to ensure the communication connectivity of the cover set. Finally, the performance of the proposed algorithm is evaluated through theoretical analysis and extensive numerical experiments. Experimental results show that the proposed algorithm outperforms the greedy algorithm in terms of the runtime and the size of the constructed connected cover set.

Track and Vehicle Condition Monitoring during Normal Operation Using Reduced Sensor Sets

The Enhanced Rail Contribution by Increased Reliability (ERCIR) project aimed to enhance availability by condition monitoring of the track and vehicle suspension from in-service trains. In this project, the possibility of instrumenting an inservice train is explored, with emphasis on using a minimal sensor set for the detection and diagnosis of track and vehicle faults. The use of a bogie-mounted pitch rate gyro to observe mean vertical track geometry is novel, as is using a bogiemounted yaw rate gyro to observe mean lateral alignment as well as the more usual longer wavelength track curvature. Mathematical algorithms are developed to detect vehicle suspension faults and track irregularities. The suspension faults considered are changes in secondary lateral and anti-yaw dampers, and changes in effective conicity. Sudden changes are detected and diagnosed using a Kalman filter-based innovation approach; gradual changes in damping coefficients and effective conicity are detected by parameter estimation usin...

Dynamic Identification of a Car

This paper presents a new dynamic modelling of a car using robotic formalism. It is based on a dual approach between eulerian and lagrangian formalism to express the dynamic model. This model is then used in order to estimate the dynamic parameters of a car and more particularly the dynamic parameters of the chassis. This approach gives better results than the lagrangian formalism. The trajectories used for the identification are presented and chosen among common trajectories. Minimum set of sensors is listed. Both simulation and experimental results are given for a Peugeot 406.

Sensor network design for fault tolerant estimation

AbstractThis paper addresses the problem of fault tolerant estimation and the design of fault tolerant sensor networks. Fault tolerance is defined with respect to a given estimation objective, namely a given functional of the system state should remain observable when sensor failures occur. Redundant and minimal sensor sets are defined and organized into an automaton which contains all the subsets of sensors such that the estimation objective can be achieved. Three criteria, which evaluate the system fault tolerance with respect to sensor failures when a reconfiguration strategy is used, are introduced: (strong and weak) redundancy degrees (RD), sensor network reliability (R), and mean time to non‐observability (MTTNO). Sensor networks are designed by finding redundant sensor sets whose RD and/or R and/or MTTNO are larger than some specified values. A ship boiler example is developed for illustration. Copyright © 2004 John Wiley & Sons, Ltd.

Motion pattern and posture: correctly assessed by calibrated accelerometers.

Basic motion patterns and posture can be distinguished by multichannel accelerometry, as recently shown. A refinement of this method appeared to be desirable to further increase its effectiveness, especially to distinguish walking and climbing stairs, and body rotation during sleep. Recordings were made of 31 subjects, according to a standard protocol comprising 13 motions and postures. This recording was repeated three times with appropriate permutation. Five uni-axial sensors and three sites of placement (sternum with three axes, right and left thigh) were selected. A hierarchical classification strategy used a standard protocol (i.e., individual reference patterns) to distinguish subtypes of moving behaviors and posture. The analysis method of the actometer signals reliably detected 13 different postural and activity conditions (only 3.2% misclassifications). A minimum set of sensors can be found for a given application; for example, a two-sensor configuration would clearly suffice to differentiate between four basic classes (sitting, standing, lying, moving) in ambulatory monitoring.

Fault Tolerant Sensor Network Design using Redundancy Degrees

This paper is concerned with the design of sensor networks which keep the system observable in the presence of sensor failures. Pseudo-minimal and minimal sensor sets are organized into an oriented graph which contains all the possible reconfiguration paths for which the system remains observable. A bottom-up analysis of this graph allows to compute redundancy degrees which evaluate the robustness of the observability property with respect to sensor failures. The design of sensor networks thus resumes to finding pseudo-minimal sensor sets with given redundancy degrees.