Sensor-based Systems Research Articles

On the topology design of large wireless sensor networks for distributed consensus with low power consumption

Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera.

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

Application of O2 sensor technology to monitor performance of industrial beef samples packaged on three different vacuum packaging machines

Two optical oxygen sensor systems we applied to monitor the evolution of meat spoilage in vacuum packaged (VP) samples of raw beef meat, which were produced at a large meat processing factory in Ireland under standard industrial settings on three different vacuum packaging machines. After packaging, the samples were put on storage at +1 °C for 90 days, while analysing their quality parameters. Residual oxygen levels and their time profiles, were measured non-destructively with disposable O2 sensors placed in each pack and interrogated with a handheld reader Optech Platinum (Mocon). Total aerobic viable counts (TVC) were measured by destructive sampling on a sensor based platform Greenlight (Agilent). Furthermore, sensory attributes of meat samples were analysed by internal sensory testing panel. The two commercial oxygen sensor-based systems allowed us to assess the performance of the three VP machines and correlate it with quality and safety characteristics of packaged meat products. They provide convenient and versatile tools for simple, rapid and in situ assessment of various packaged products and packaging systems.

Validity and Reliability of an Inertial Sensor-Based Knee Proprioception Test in Younger vs. Older Adults.

Diminishing proprioception caused by aging effects is associated with a higher risk to fall. However, existing measurement systems of proprioception are often expensive, time-consuming, or insufficient regarding reliability evaluation. Inertial sensor-based systems could address these issues. Consequently, this study sought to develop and evaluate an inertial sensor-based joint position sense test. Thereto, intra-session and inter-day test-retest reliability were investigated in a cross-over design. Twenty healthy younger (age: 22 ± 3 years) and 20 healthy older adults (age: 65 ± 5 years) participated in the study. We calculated the mean of the absolute error, the signed error, and the standard deviation of the signed error. Test-retest reliability was quantified by using the intraclass correlation coefficient as well as the bias and limits of agreement. To evaluate the possibility of capturing aging effects, and correspondingly a validation of the system, we calculated Cohen's d. For the intra-session reliability, fair to good agreements were achieved for the absolute and relative error in all target ranges. Compared to younger adults, we registered a declined joint position sense in older adults with high effects observed for the absolute error in a target range of 15–25 and 35–45° as well as for the variable error in the target ranges of 35–45 and 55–65°. We suggest that inertial sensor-based joint position sense tests are reliable and capable to measure aging effects on proprioception, and are therefore a low-cost and mobile alternative to existing methods.

Comparison of algorithms and classifiers for stride detection using wearables

Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min–max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson’s disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown.

Smartphone-Based Context Flow Recognition for Outdoor Parking System with Machine Learning Approaches

Outdoor parking systems are one of the most crucial needs in a smart city to find vacant parking spaces in outdoor environments, such as roadsides, university campuses, and so on. In a typical outdoor parking system, the detection of a vehicle entering and leaving the parking zone is a major step. At present, there are numerous external sensor-based and camera-based parking systems available to detect the entrance and leaving of vehicles. Camera-based parking systems rely on sophisticated camera set-ups, while sensor-based parking systems require the installation of sensors at the parking spots or vehicles’ sides. Due to such complication, the deployment and maintenance costs of the existing parking systems are very high. Furthermore, the need for additional hardware and network capacity increases the cost and complexity, which makes it difficult to use for large deployment. This paper proposes an approach for outdoor parking utilizing only smartphone integrated sensors that do not require manpower support nor additional sensor installation. The proposed algorithm first receives sensor signals from the driver’s phone, performs pre-processing to recognize the context of drivers, which is followed by context flow recognition. The final result is obtained from context flow recognition which provides the output of whether the driver is parking or unparking. The proposed approach is validated with a set of comprehensive experiments. The performance of the proposed method is favorable as it uses only the smartphone’s internal sensors to recognize whether the cars are entering or leaving the parking area.

Validation of magneto-inertial measuring units for measuring hip joint angles

Camera-based motion capture systems are the current gold standard for motion analysis. However, the use of wireless inertial sensor-based systems is increasing in popularity, largely due to convenient portability. The purpose of this study was to validate the use of wireless inertial sensors for measuring hip joint motion with a functional calibration requiring only one motion (walking) and neutral standing. Data were concurrently collected using a 10-camera motion capture system and a wireless inertial sensor-based system. Hip joint angles were measured for 10 participants during walking, jumping jack, and bilateral squat tasks and for a subset (n = 5) a jump turn task. Camera-based system hip joint angles were calculated from retro-reflective marker positions and sensor-based system angles were calculated in MATLAB using the sensor output quaternions. Most hip joint angles measured with the sensor-based system were within 6° of angles measured with the camera motion capture system. Accurate measurement of motion outside of a laboratory setting has broad implications for diagnosing movement abnormalities, monitoring sports performance, and assessing rehabilitation progress.

Remote Robot-Sensor Calibration Service

Cyber physical systems (CPSs) are integrating computation, networking, and physical processes. CPSs facilitate improvements especially for asset management. The authors have contributed to robot automation with CPS technologies by a pilot system to support setup and maintenance of sensor-based robot systems with remote calibration services. This paper has focused to support remotely-located technology specialists as well as local field personnel by appropriate user interface design. A design example is given for a remote robot-sensor calibration procedure. In the user interface design, the authors follow loosely the Ecological Interface Design (EID) approach.

Sensor-based and Cognitive Assistance Systems in Industry 4.0: Big Data Analytics, Smart Production, and Sustainable Value Creation

Sensor-based and Cognitive Assistance Systems in Industry 4.0: Big Data Analytics, Smart Production, and Sustainable Value Creation

Automatic Detection of Compensatory Movement Patterns by a Pressure Distribution Mattress Using Machine Learning Methods: A Pilot Study

For stroke patients with hemiplegia, reaching with the paretic arm is often associated with compensatory movements due to limited active arm movement and a loss of interjoint coordination. Detecting common compensatory movement patterns, such as excessive trunk displacement and scapular elevation, is critical for improving the motor function of the paretic arm. Existing compensatory movement pattern detection methods, including sensor-based systems and camera-based systems, suffer from object obstruction and require complex setups. In this paper, a compensatory movement pattern detection system using a pressure distribution mattress is presented. This method is a novel approach to detect compensations and has observed advantages; it is simple, unobtrusive and low cost. Fifteen healthy participants with no motor impairments performed three reaching tasks (back-and-forth, side-to-side, and up-and-down reaching) in a normal pattern and in compensatory movement patterns (trunk rotation and lean-forward, and scapular elevation). Pressure distribution data of all motions were recorded and processed to generate a group of features (average sensor values, the lateral center of pressure, longitudinal center of pressure, the ratio of left-side to right-side pressure, and the ratio of front-side to back-side pressure) reflecting the information of each predefined pattern. Four machine learning methods were implemented to detect compensatory movement patterns and showed good reliability and precision. Both k-nearest neighbor (kNN) and support vector machine (SVM) classifiers have achieved an excellent classification performance (F1-score = 0.934) in detecting compensation during all reaching tasks. For the multiclass classification of compensatory movement patterns, the SVM classifier exhibited a good classification performance for trunk lean-forward (F1-score = 0.933), scapular elevation (F1-score = 0.881), and trunk rotation (F1-score = 0.854) and outperformed previous reports. The study results provide initial evidence of a pressure distribution mattress for detecting compensatory movement patterns. Future work needs to test the approach on stroke survivors to verified the feasibility and validity.

Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems.

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

Making Sense of the World: Framing Models for Trustworthy Sensor-Driven Systems

Sensor-driven systems provide data and information that facilitate real-time decision-making and autonomous actuation, as well as enable informed policy choices. However, can we be sure that these systems work as expected? Can we model them in a way that captures all the key issues? We define two concepts: frames of reference and frames of function that help us organise models of sensor-based systems and their purpose. Examples from a smart water distribution network illustrate how frames offer a lens through which to organise and balance multiple views of the system. Frames aid communication between modellers, analysts and stakeholders, and distinguish the purpose of each model, which contributes towards our trust that the system fulfils its purpose.

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

Background: Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity. Objectives: We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness. Methods: We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone. Results: During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning. Conclusions: This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.

Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty

BackgroundDespite the general success of total knee arthroplasty (TKA) regarding patient-reported outcome measures, studies investigating gait function have shown diverse functional outcomes. Mobile sensor-based systems have recently been employed for accurate clinical gait assessments, as they allow a better integration of gait analysis into clinical routines as compared to laboratory based systems. Research questionIn this study, we sought to examine whether an accurate assessment of gait function of knee osteoarthritis patients with respect to surgery outcome evaluation after TKA using a mobile sensor-based gait analysis system is possible. MethodsA foot-worn sensor-based system was used to assess spatio-temporal gait parameters of 24 knee osteoarthritis patients one day before and one year after TKA, and in comparison to matched control participants. Patients were clustered into positive and negative responder groups using a heuristic approach regarding improvements in gait function. Machine learning was used to predict surgery outcome based on pre-operative gait parameters. ResultsGait function differed significantly between controls and patients. Patient-reported outcome measures improved significantly after surgery, but no significant global gait parameter difference was observed between pre- and post-operative status. However, the responder groups could be correctly predicted with an accuracy of up to 89% using pre-operative gait parameters. Patients exhibiting high pre-operative gait function were more likely to experience a functional decrease after surgery. Important gait parameters for the discrimination were stride time and stride length. SignificanceThe early identification of post-surgical functional outcomes of patients is of great importance to better inform patients pre-operatively regarding surgery success and to improve post-surgical management.

Open-source 3D printed sensors for hand strength assessment: Validation of low-cost load cell and fabric sensor-based systems.

Pinch and grip strength assessment is commonly performed in occupational therapy practice. However, the typically utilised methods are limited to pinch or grip dynamometers and do not readily translate to handling everyday objects. With the advent of consumer-grade 3D printing and low-cost sensor systems, the possibilities for creating customised assessment devices are expanding. As a first step in determining the validity of low-cost sensor systems, their data must be compared to a gold standard. Consequently, this study examined the criterion validity of two such systems for measuring pinch strength, specifically a small load cell and pressure-sensing fabric, with a mechanical pinch gauge. A total of 33 participants performed strength tests using a mechanical pinch grip device, which had a plastic 3D printed cover with a pressure-sensing fabric overlaid on it to allow for simultaneous criterion validation, and a small load cell with a plastic 3D printed casing designed for comfortable pinch grip assessment. The simultaneously assessed fabric sensor and mechanical pinch grip device showed excellent absolute (ICC2,k =0.94) and relative (Pearson's R=0.90) agreement. Both devices showed similar excellent relative (R>0.75) agreement with the load cell despite non-simultaneous assessment. These findings indicate that 3D printed sensors incorporating a load cell and a pressure-sensing fabric can be used to replicate a pinch grip assessment performed with a mechanical pinch gauge. This study lays the foundation for these sensor systems to be modified for use as assessment tools during the performance of functional tasks using everyday objects. Additionally, because both systems generate real-time force data they could be used for biofeedback as part of rehabilitation and strengthening programs. To aid uptake and future research using these systems, the 3D print models, step-by-step hardware design and software programs used are provided in an open-source format at www.rehabtools.org/otsensors.html.

Animal behavior management by energy-efficient wireless sensor networks

In recent years, studies have been conducted on systems based on wireless sensor networks for the detection of animal behaviors. These systems can offer significant information on how to increase the production and animals’ health. They consist of low-cost sensor nodes with limited resources. One of the critical resources is the power unit. These systems have an energy problem especially in outdoor environments same as other wireless sensor-based systems. ZigBee-based RF modules are used for data transmissions in these systems. Hence, they have limited battery and memory. To fulfill the requirements of power reduction, this paper proposes a technique based on time-driven systems for controlling the transmission rate of ZigBee-based RF modules based on data aggregation and sleep/wake-up methods. Data aggregation is done in many cases. However, the sleep/wake-up method is related to animal behavior because it may cause changes in the network topology. The proposed system is implemented on cows and the results show that the proposed system can help to achieve a significant reduction in power consumption of wireless sensor networks and improve the lifetime. In additional, it can offer and help to liable of animals in monitoring the health of their animals.

Soil Moisture Sensor-based Systems are Suitable for Monitoring and Controlling Irrigation of Greenhouse Crops

Sensor-based feedback control irrigation systems have been increasingly explored for greenhouse applications. However, the relationships between microclimate variation, plant water usage, and growth are not well understood. A series of trials were conducted to investigate the microclimate variations in different greenhouses and whether a soil moisture sensor-based system can be used in monitoring and controlling irrigation in greenhouse crop productions. Ocimum basilicum ‘Genovese Gigante’ basil and Campanula portenschlagiana ‘Get Mee’ bellflowers were monitored using soil moisture sensors for an entire crop cycle at two commercial greenhouses. Significant variations in greenhouse microclimates were observed within the two commercial greenhouses and within an older research greenhouse. Evaporation rates were measured and used as an integrated indicator of greenhouse microclimate conditions. Evaporation rates varied within all three greenhouses and were almost double the lowest rates within one of the greenhouses, suggesting microclimates within a range of greenhouses. Although these microclimate variations caused large variations in the growing substrate water contents of containers within the greenhouses, the growth and quality of the plants were unaffected. For example, no significant correlations were observed between the growth of bellflower plants and the average volumetric water content (VWC), minimum VWC, or maximum VWC of the growing substrate. The change in VWC at each irrigation (ΔVWC), however, was positively correlated with the fresh weight, dry weight, and growth index (GI) of the bellflowers. For basil, no significant correlations were observed between plant growth and ΔVWC. This suggests that sensor-based feedback irrigation systems can be used for greenhouse crop production when considerations are given to factors such as the magnitude of microclimate variation, crop species and its sensitivity to water stress, and growing substrate.

Dynamic data-driven learning for self-healing avionics

In sensor-based systems, spatio-temporal data streams are often related in non-trivial ways. For example in avionics, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors such as engine inputs, angle of attack, and air density. It is therefore a challenge to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity or an incorrect airspeed. In this paper, we present a highly-declarative programming framework that facilitates the development of self-healing avionics applications, which can detect and recover from data errors. Our programming framework enables specifying expert-created failure models using error signatures, as well as learning failure models from data. To account for unanticipated failure modes, we propose a new dynamic Bayes classifier, that detects outliers and upgrades them to new modes when statistically significant. We evaluate error signatures and our dynamic Bayes classifier for accuracy, response time, and adaptability of error detection. While error signatures can be more accurate and responsive than dynamic Bayesian learning, the latter method adapts better due to its data-driven nature.

The NETLAKE Metadatabase-A Tool to Support Automatic Monitoring on Lakes in Europe and Beyond

Sharing data is a keystone of collaborative science. A fundamental barrier, however, can be a lack of knowledge on what is being collected, where, and by whom. The aim of NETLAKE (COST Action ES1201) was to build a network of sites and individuals to support development and deployment of automatic sensor-based systems on lakes and reservoirs in Europe. To support this, NETLAKE developed a metadatabase which could provide answers to questions on where lakes were monitored, details on the frequency and duration of monitoring, contact details, and which sensors were being used. Development included challenges related to time and resources, and indeed to communication between lake scientists and database experts. In total, metadata for 71 European lakes were captured. The resulting data revealed interesting facts; for example, seven sites had archives that spanned over a decade, only seven of these lakes were used as drinking water sources, and one was a large fish pond. GLEON, the Global Lake Ecological Observatory Network, and two pan-American projects are now adding their metadata and the metadatabase is developing into a tool for the global community which can promote high frequency monitoring and facilitate network science.

Automated Moving Object Classification in Wireless Multimedia Sensor Networks

The use of wireless multimedia sensor networks (WMSNs) for surveillance applications has attracted the interest of many researchers. As with traditional sensor networks, it is easy to deploy and operate WMSNs. With inclusion of multimedia devices in wireless sensor networks, it is possible to provide data to users that is more meaningful than that provided by scalar sensor-based systems alone; however, producing, storing, processing, analyzing, and transmitting multimedia data in sensor networks requires consideration of additional constraints, including energy, processing power, storage capacity, and communication. Furthermore, as multimedia sensors produce much more data than scalar sensors, more manpower is required to analyze multimedia data. To overcome these constraints and challenges, this paper aimed to propose a system architecture and a set of procedures for WMSNs that facilitate automatic classification of moving objects using scalar and multimedia sensors. Methods and standards for detecting and classifying a moving object, as well as transmission of the results, are described in detail. The hardware for each sensor node includes a built-in camera, a passive infrared motion sensor, a vibration sensor, and an acoustic sensor. An application using our proposed methods was developed and embedded in the multimedia sensor node. In addition, a sink station was set up and the data produced by the sensor network was collected by this server. The classification performance of the application was tested using video recorded by the sensor node. The effect of the proposed methods on power consumption was also tested and measured. The experimental results show that the proposed approach is sufficiently lightweight to be used for real-world surveillance applications.