Minimum Set Of Sensors Research Articles

G.A.I.T: gait analysis interactive tool a pipeline for automatic detection of gait events across different motor impairments

We introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.

Neural network-based estimation of lower limb joint kinematics: A minimally intrusive approach for gait analysis

The establishment of a quantitative gait analysis system holds paramount importance, particularly in the context of functional rehabilitation of the lower limbs. Clinicians emphasize the imperative for sensors to be portable, compact, integrated, and non-intrusive, crucial characteristics in the rehabilitation field to facilitate their use and ensure optimal integration into care protocols. This study investigates an innovative approach aimed at reducing the reliance on body-fixed sensors by harnessing their data within a neural network, thus concentrating on the joint kinematics of the lower limbs. The primary objective is to estimate the flexion-extension angles of the hip, knee, and ankle during walking, utilizing data collected by two sensors positioned on the subject's legs. Initially, the neural network undergoes training with calculated data (leg tilt angles and angular velocities) sourced from the OpenSim database, followed by further refinement with experimental data obtained from a subject walking on a treadmill, wherein leg tilt angles and angular velocities are measured. The significance of this research is underscored by the demonstrated capability, through conducted tests, of the implemented networks to efficiently fuse data from a minimal set of sensors. Consequently, the proposed approach emerges as both practical and minimally intrusive, facilitating a robust evaluation of gait kinematic parameters.

Robust minimal strong reconstructibility problem of Boolean control networks

In this paper, the problem of robust minimal strong reconstructibility (RMSR) of Boolean control networks (BCNs) with multi-bit perturbations is studied by using graph theory. The problem takes the premise that BCNs are strongly reconstructible before function perturbation. We analyze the effect of the multi-bit perturbation on the strong reconstructibility of BCNs and present two valid criteria for the robust strong reconstructibility of BCNs. Then, to avoid the effect of function perturbation on the strong reconstructibility of BCNs, the problem of adding a minimal sensor set is considered, thereby making the BCNs robust minimal strong reconstructible after function perturbation. Finally, the results are applied to a biological example.

Agricultural Autonomous Robotic Platform Motion Control

A model of the movement of a robotic platform adapted to the conditions of an industrial orchard is proposed. (Research purpose) Development of a motion control system for an autonomous robotic wheeled platform based on inertial and satellite navigation and traversed path calculation, which will allow it to move in an apple orchard and automatically perform various technological operations, such as fertilization, growth diseases control of, fruit harvesting. (Materials and methods) A mathematical model was developed to control the movement of a robotic platform, taking into account the turning radii of three types, the length of the arc of the performed circle, the speed of movement in the garden plantation rows using a garden electronic map. The method used allows implementing a program for the robotic platform automatic movement around a typical orchard using a minimum set of sensors, significantly reducing the load on the onboard computer processor and memory. Software, developed in the Python programming language, enables plotting the robotic platform route, displaying the movement trajectory, and indicating the positioning accuracy at each point in relation to the trees in the garden plantation rows, the movement speed and the wheel rotation angle. (Results and discussion) The robotic platform managed to autonomously pass the preset routes, while the interaction of the software and the robotic platform hardware was provided. A field testing of the developed software was performed. (Conclusions) The specified accuracy of the robotic platform positioning was confirmed for the 3.5-meter aisles of intensive orchards. The maximum deviation from the task map using satellite and inertial navigation system was 164 millimeters, which complies with the agrotechnical requirements for mechanized fruit harvesting.

Emotions and Perceived Productivity of Software Developers at the Workplace

Emotions are known to impact cognitive skills, thus influencing job performance. This is also true for software development, which requires creativity and problem-solving abilities. In this paper, we report the results of a field study involving professional developers from five different companies. We provide empirical evidence that a link exists between emotions and perceived productivity at the workplace. Furthermore, we present a taxonomy of triggers for developers’ positive and negative emotions, based on the qualitative analysis of participants’ self-reported answers collected through daily experience sampling. Finally, we experiment with a minimal set of non-invasive biometric sensors that we use as input for emotion detection. We found that positive emotional valence, neutral arousal, and high dominance are prevalent. We also found a positive correlation between emotional valence and perceived productivity, with a stronger correlation in the afternoon. Both social and individual breaks emerge as useful for restoring a positive mood. Furthermore, we found that a minimum set of non-invasive biometric sensors can be used as a predictor for emotions, provided that training is performed on an individual basis. While promising, our classifier performance is not yet robust enough for practical usage. Further data collection is required to strengthen the classifier, by also implementing individual fine-tuning of emotion models.

Visual Servoing Approach to Autonomous UAV Landing on a Moving Vehicle.

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided.

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography.

This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24–70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

Identification of strategic sensor locations for intrusion detection and classification in smart grid networks

The dependence of smart grid (SG) on advanced communication technology has increased its vulnerability to both cyber and physical attacks. Unlike, a physical attack which can be easily detected by the state estimation (SE) task performed at control center, cyberattack detection is accomplished using the information obtained through the entire set of secured sensors. However, the economic constraints hinder securing the entire monitoring system. In this regard, the present work proposes a cost-effective framework for detecting physical, cyber and cyber–physical(CP) attacks by imparting security to a minimal set of sensors. The attack detection scheme involves identifying a minimal sensor set using a graph theory-based approach for topological observability. The attack detection task is executed by solving AC state estimation (AC-SE) for varying types of attacks based on the secured sensor information at strategic locations. Prioritizing strategic locations allows for maximizing detection accuracy under different levels of budget for security. The formulation does not assume any restriction on the accessibility of sensor information to the intruder to launch an attack. Following attack detection, the attack type is determined based on sensor measurements. The scheme has been extensively validated for different systems in terms of attack detection and classification accuracy under different levels of budget availability and intruder accessibility.

A Spacecraft Attitude Determination and Control Algorithm for Solar Arrays Pointing Leveraging Sun Angle and Angular Rates Measurements

The capability to orient the solar arrays of a spacecraft toward the Sun is an ultimate asset for any attitude determination and control subsystem (ADCS). This ability should be maintained in any operative circumstance, either nominal or off-nominal, to avoid the loss of the entire space-borne system. The safe mode implementation should guarantee a positive power generation from the solar arrays, regardless of the health status of the satellite platform. This paper presents a solar array pointing algorithm, to be executed on-board, with a minimal set of sensors and actuators. In fact, the sensors are limited to the solar arrays, exploiting the current/voltage sensing capacity of the electrical power subsystem to measure the Sun angle with respect to the arrays normal, and to the angular rates sensors. The actuators are required to provide a torque only along two axes and, thus, a reduced actuation capacity is still manageable by the proposed algorithm. The paper describes the algorithm, both in the Sun direction determination and in the Sun pointing control capacity. The achieved performance is outlined, considering either an ideal system or a realistic one, being the latter affected by sensors and actuators limitations. The actuation by means of momentum exchange devices or magnetic torquers is discussed, with the purpose to prove the wide applicability range of the presented algorithm, which is capable to guarantee solar array orientation with a minimal hardware set.

Multicriteria assessment of optimal forecasting models in decision support systems to ensure the navigation safety

When developing the Decision Support System (DSS), the operability of the internal motion and maneuvering control systems of the vessel is characterized by a large number of parameters, which should be monitored in order to achieve the expected results. The task is to develop an appropriate methodology for automatic monitoring of these systems, which, using a minimum set of sensors, makes it possible to predict the state of the vessel and change the dangerous state to the safe one. To implement the method of automatic monitoring, a set of statistical control tools is selected depending on the alleged violations and the level of correlation of parameters. The uncorrelated parameters are monitored by instruments based on the Shewhart map [1], the correlated parameters are monitored on the basis of Hotelling statistics [2]. This approach makes it possible to diagnose the pre-emergency and emergency states of ship control systems in on-line mode. The method used for multi-level integrated monitoring of the technical state of control systems in on-line mode can improve the reliability of of identification of the technical state of vessel subsystems and expandthe scope of application of monitoring and diagnostics tools. The data obtained can become the basis for the development of rational decisions in the DSS at the level of control subsystems for the vessel transfer from the dangerous to safe state.

Providing music service in Ambient Intelligence: experiments with gym users

Ambient Intelligence (AmI) is an interdisciplinary research area of ICT which has evolved since the 90s, taking great advantage from the advent of the Internet of Things (IoT). AmI creates, by using Artificial Intelligence (AI), an intelligent ecosystem in which computers, sensors, lighting, music, personal devices, and distributed services, work together to improve the user experience through the support of natural and intuitive user interfaces. Nowadays, AmI is used in various contexts, e.g., for building smart homes and smart cities, providing healthcare, and creating an adequate atmosphere in retail and public environments.In this paper, we propose a novel AmI system for gym environments, named Gym Intelligence, able to provide adequate music atmosphere, according to the users’ physical effort during the training. The music is taken from Spotify and is classified according to some music features, as provided by Spotify itself. The system is based on a multi-agent computational intelligence model built on two main components: (i) machine learning methods that forecast appropriate values for the Spotify music features, and (ii) a multi-objective dynamic genetic algorithm that selects a specific Spotify music track, according to such values. Gym Intelligence is built by sensing the ambient with a minimal, low-cost, and non-intrusive set of sensors, and it has been designed considering the outcome of a preliminary analysis in real gyms, involving real users. We have considered well-known regression methods and we have validated them using a collected data (i) about the users’ physical effort, through the sensors, and (ii) about the users’ music preferences, through an Android app that the users have used during the training. Among the regression methods considered, the one that provided the best results is the Random Forest, which predicted Spotify music features with a mean absolute error of 0.02 and a root mean squared error of 0.05. We have implemented Gym Intelligence and deployed it in five real gyms. We have evaluated it conducting several experiments. The experiments show how, with the help of Gym Intelligence, the users’ satisfaction about the provided background music, rose from 3.05 to 4.91 (on a scale from 1 to 5, where 5 is the maximum score).

Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors

ObjectiveRapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. This study investigated the use of a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors. MethodsPolysomnography signals from 50 participants with RBD and 50 age-matched healthy controls were used to evaluate this study. Three stage sleep classification was achieved using a random forest classifier and features derived from a combination of cost-effective and easy to use sensors, namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG) channels. Subsequently, RBD detection was achieved using established and new metrics derived from ECG and EMG channels. ResultsThe EOG and EMG combination provided the optimal minimalist fully-automated performance, achieving 0.57 ± 0.19 kappa (3 stage) for sleep staging and an RBD detection accuracy of 0.90 ± 0.11, (sensitivity and specificity of 0.88 ± 0.13 and 0.92 ± 0.098, respectively). A single ECG sensor achieved three state sleep staging with 0.28 ± 0.06 kappa and RBD detection accuracy of 0.62 ± 0.10. ConclusionsThis study demonstrates the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques. SignificanceThis study proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG); ideal for screening purposes.

Adequate Sensor Placement Method to Ensure Diagnosability in Interpreted Petri Net

At the industrial level detect and compensate faults in the plant is important, to avoid irreparable damage and the increase of unproductive time, being necessary that the equipment has adequate sensors and the plant model analyzes the full operation of the system, among other requirements, to obtain under simulation the missing characteristics to diagnose faults. This paper works with the number of sensors in plant to reach diagnosability, for this the modelling is relationed with the acquired signals from the plant, it begins with an input/output identification method modelling by interpreted Petri net, then diagnosability general characteristics was determined, mathematically interrelated; and a sensor placement method for diagnosability was established. It uses matrix analysis to evaluate event-detectability and observability. The sensor placement method stablishes the plant operation points where additional sensors are needed; this process is explained with a simulation example. The investigation result is a minimal sensor set that makes the plant diagnosable, avoiding sensors redundancy and reducing related costs.

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

On the minimal set of controllers and sensors for linear power flow

We consider a linear power flow model with interval-bounded nodal power injections and limited line power flows. We determine the minimal number of power injections to control based on a minimal set of measurements, such that the overall system is feasible for all assignments of the non-controlled power injections. For the important case where the possible measurements are the nodal power injections, we show that the problem can be solved efficiently as a mixed-integer linear program (MILP). When also line power flows are considered as potential measurements, we derive an iterative, greedy algorithm that provides a feasible, but potentially conservative solution. We apply the developed algorithms to both a simple microgrid and a modified version of the IEEE 118 bus test power system. We show that in both cases a sparse solution in terms of the number of required controllers and measurements can be obtained. Moreover, the number of required measurements can be reduced significantly if line flow measurements are considered additionally to nodal power injections.

State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach.

The interaction between the rotating blades and the external fluid in non-axial flow conditions is the main source of vibratory loads on the main rotor of helicopters. The knowledge or prediction of the produced aerodynamic loads and of the dynamic behavior of the components could represent an advantage in preventing failures of the entire rotorcraft. Some techniques have been explored in the literature, but in this field of application, high accuracy can be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This paper applies the Kalman filtering technique to rotor load estimation. The nature of the filter allows the usage of a minimum set of sensors. The compensation of a low-fidelity model is also possible by accounting for sensors and model uncertainties. The efficiency of the filter for state and load estimation on a rotating blade is tested in this contribution, considering two different sources of uncertainties on a coupled multibody-aerodynamic model. Numerical results show an accurate state reconstruction with respect to the selected sensor layout. The aerodynamic loads are accurately evaluated in post-processing.

Autonomous Electrical System Monitoring and Control Strategies to Avoid Oversized Storage Capacity

A significant solution to achieve energy efficiencies and stability of the electrical network is upgrading auto-consumption in a single PV system. In this paper, we presented a methodology to implement low-cost sensors and actuators to have better monitoring and control for a feasibility solution. This approach has been conducted through a case study on Greenhouse’s PV system in Grenoble, France. We proposed a minimal set of sensors to reduce system complexity while giving us enough information to make decisions. Some technical issues were analyzed as accuracy, sampling rate, response-ability of our system. The influences of inverter operation mode to system losses were considered. After that, we figured out energy problematics in our system based on available design data and simulation data from PVSyst. A PV production prediction model was investigated with input as forecast cloudiness data collected from web service, updated every 3 hours. This model, combined with real-time monitoring data and setting mode on an off-grid inverter, was used to identify control strategies with objectives of avoiding oversize storage capacity and maximizing the autonomous duration of the PV system.

Indirect measurement of anterior-posterior ground reaction forces using a minimal set of wearable inertial sensors: from healthy to hemiparetic walking

BackgroundThe anterior-posterior ground reaction force (AP-GRF) and propulsion and braking point metrics derived from the AP-GRF time series are indicators of locomotor function across healthy and neurological diagnostic groups. In this paper, we describe the use of a minimal set of wearable inertial measurement units (IMUs) to indirectly measure the AP-GRFs generated during healthy and hemiparetic walking.MethodsTen healthy individuals and five individuals with chronic post-stroke hemiparesis completed a 6-minute walk test over a walking track instrumented with six forceplates while wearing three IMUs securely attached to the pelvis, thigh, and shank. Subject-specific models driven by IMU-measured thigh and shank angles and an estimate of body acceleration provided by the pelvis IMU were used to generate indirect estimates of the AP-GRF time series. Propulsion and braking point metrics (i.e., peaks, peak timings, and impulses) were extracted from the IMU-generated time series. Peaks and impulses were expressed as % bodyweight (%bw) and peak timing was expressed as % stance phase (%sp). A 75%-25% split of 6-minute walk test data was used to train and validate the models. Indirect estimates of the AP-GRF time series and point metrics were compared to direct measurements made by the forceplates.ResultsIndirect measurements of the AP-GRF time series approximated the direct measurements made by forceplates, with low error and high consistency in both the healthy (RMSE= 4.5%bw; R2= 0.93) and post-stroke (RMSE= 2.64%bw; R2= 0.90) cohorts. In the healthy cohort, the average errors between indirect and direct measurements of the peak propulsion magnitude, peak propulsion timing, and propulsion impulse point estimates were 2.37%bw, 0.67%sp, and 0.43%bw. In the post-stroke cohort, the average errors for these point estimates were 1.07%bw, 1.27%sp, and 0.31%bw. Average errors for the braking estimates were higher, but comparable.ConclusionsAccurate estimates of AP-GRF metrics can be generated using three strategically mounted IMUs and subject-specific calibrations. This study advances the development of point-of-care diagnostic systems that can catalyze the routine assessment and management of propulsion and braking locomotor deficits during rehabilitation.

Propulsive joint powers track with sensor-derived angular velocity: A potential tool for lab-less gait retraining

Lower propulsive joint powers, particularly at the ankle, are often observed in older compared to young adults. Interventions to increase joint powers often require labs with motion capture and force treadmill technology. Translating these interventions out of the lab requires identifying portable measures that track (i.e., strongly correlate with) changes in joint powers. The purpose of this study was to determine if kinematics collected using inertial measurement units (IMUs) correlate with propulsive joint powers calculated using inverse dynamics. We collected data simultaneously with motion capture, force plates, and IMU sensors as young and older adults walked at varying speeds overground in a laboratory. Hip, knee, and ankle joint powers were calculated using inverse dynamics and positive peaks in the second half of stance were identified as the propulsive powers of interest. Raw IMU gyroscope data were oriented to a functional medial–lateral axis and peaks in the second half of stance were identified for segment (thigh, shank, foot) and joint (hip, knee, ankle) angular velocities. Pearson correlation coefficients were calculated between peak joint powers and peak angular velocities. We identified significant (all p < 0.001) correlations between hip joint power and hip and thigh angular velocities (r = 0.80–0.83) and between ankle joint power and ankle, shank, and foot angular velocities (r = 0.77–0.89). Correlation strength was similar between young and older adults and between segment and joint angular velocities. These results suggest that changes in joint powers longitudinally or over the course of an intervention could be tracked using a minimal set of wearable sensors.

In-flight thrust monitoring: an acoustics-based approach

Purpose The purpose of this paper is to present an acoustics-based method for measuring turbofan nozzle exhaust thrust, while assessing the potential of scaling the methods for in-flight measurements. Design/methodology/approach Although many methods proposed for achieving in-flight thrust measurements involve complicated, sensitive and expense instruments, an acoustics-based approach is discussed that greatly simplifies the technology development pathway to in-flight applications. Findings Results are provided for a minimum set of sensors applied in the exhaust of a research turbofan engine at Virginia Tech, showing the difference in acoustics-measured thrust and nozzle thrust found by integrating thermocouple and Kiel probe measurements to be less than 6 per cent at the maximum fan speed examined. Practical implications Measuring accurate thrust values in flight will prove immediately valuable for installed thrust validation and engine health monitoring. Acoustics-based methodologies are attractive because of the robustness and low cost of sensors and sources. The value of in-flight thrust measurements, along with the benefits of acoustic approaches, makes the current topic of great interest for further development. Originality/value This paper presents unique applications of a time-of-flight acoustic thrust sensor, while providing an original assessment of technological challenges involved with the progression of the technique for in-flight measurements.