ObjectiveThis study aimed to develop a diagnostic model of multi-kinematic parameters for patients with amnestic mild cognitive impairment (aMCI).MethodIn this cross-sectional study, 94 older adults were included (33 cognitively normal, CN; and 61 aMCI). We conducted neuropsychological battery tests, such as global cognition and cognitive domains, and collected gait parameters by an inertial-sensor gait analysis system. Multivariable regression models were used to identify the potential diagnostic variables for aMCI. Receiver operating characteristic (ROC) curves were (...) applied to assess the diagnostic accuracy of kinematic parameters in discriminating aMCI from healthy subjects.ResultsMultivariable regression showed that multi-kinematic parameters were the potential diagnostic variables for aMCI. The multi-kinematic parameter model, developed using Timed Up and Go (TUG) time, stride length, toe-off/heel stride angles, one-leg standing (OLS) time, and braking force, showed areas under ROC (AUC), 0.96 [95% confidence interval (CI), 0.905–0.857]; sensitivity, 0.90; and specificity, 0.91. In contrast, a single kinematic parameter’s sensitivity was 0.26–0.95 and specificity was 0.21–0.90. Notably, the separating capacity of multi-kinematic parameters was highly similar to Montreal Cognitive Assessment (MoCA; AUC: 0.96 vs. 0.95). Compared to cognitive domain tests, the separating ability was comparable to Auditory Verbal Learning Test (AVLT) and Boston Naming Test (BNT; AUC: 0.96 vs. 0.97; AUC: 0.96 vs. 0.94).ConclusionWe developed one diagnostic model of multi-kinematic parameters for patients with aMCI in Foshan. (shrink)

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. However, the bionic prosthesis needs to know the next movement at the beginning of a new gait, especially in complex operation environments. In this paper, an upcoming locomotion prediction scheme via multilevel classifier fusion was proposed for the complex operation. At first, two motion states, including steady state and transient state, were defined. Steady-state recognition was backtracking of (...) a completed gait, which would be used as prior knowledge of motion prediction. In steady-state recognition, surface electromyographic and inertial sensors were fused to improve recognition accuracy; five typical locomotion modes were recognized by random forest classifier with over 97.8% accuracy. The transient state was defined as an observation period at the initial stage of upcoming movement, in which only the sEMG signal was recorded due to the limitation of sliding window length. LightGBM classifier was validated to outperform other methods in the accuracy and prediction time of transient-state recognition. Finally, a simplified HMM model based on prior knowledge and observation result was constructed to predict upcoming locomotion. The results indicated that the locomotion prediction was over 91% accuracy. The proposed scheme implements the locomotion prediction at the initial stage of each gait and provides critical information for the gait control of lower limb prosthesis. (shrink)

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram and functional near-infrared spectroscopy are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established (...) methodology proven to enhance brain–computer interface performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go. (shrink)

IntroductionThe assessments of the motor symptoms in Parkinson’s disease are usually limited to clinical rating scales, and it depends on the clinician’s experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device. And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.MethodsWe used Kinect®eMotion system to capture the (...) spatiotemporal gait data from 30 patients with PD and 30 healthy age-matched controls in three walking trials. First, a correlation matrix was made using the variables of upper and lower limbs. After this, we applied a backward feature selection model using R and Python to determine the most relevant variables. Three further analyses were done using variables selected from backward feature selection model, movement disorders specialist, and all the variables from the dataset. We ran seven machine learning models for each model. Dataset was divided 80% for algorithm training and 20% for evaluation. Finally, a causal inference model using the DoWhy library was performed on Dataset B due to its accuracy and simplicity.ResultsThe Random Forest model is the most accurate for all three variable Datasets followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry and a proportional relationship between ASA and the diagnosis of PD with a robust estimator.ConclusionsMachine learning techniques based on objective measures using portable low-cost devices are useful and accurate to classify patients with Parkinson’s disease. This method can be used to evaluate patients remotely and help clinicians make decisions regarding follow-up and treatment. (shrink)

Falls are a leading cause of serious injury and restricted participation among persons with stroke (PwS). Reactive balance control is essential for fall prevention, however, only a few studies have explored the effects of lesion characteristics (location and extent) on balance control in PwS. We aimed to assess the impact of lesion characteristics on reactive and anticipatory balance capacity, gait, and hemiparetic lower limb function, in PwS. Forty-six subacute PwS were exposed to forward, backward, right and left unannounced horizontal (...) surface translations in 6 increasing intensities, while standing. Fall threshold (i.e., perturbation intensity that results in a fall into the harness system) was measured. In addition, the Berg Balance Scale (BBS), 6 Minute Walk Test (6MWT) and Lower Extremity Fugl-Meyer (LEFM) were measured. Lesion effects were analyzed separately for left- and right-hemisphere damaged (LHD, RHD) patients, using voxel-based lesion-symptom mapping (VLSM). Our results show that voxel clusters where damage exerted a significant impact on balance, gait and lower-limb function were found in the corticospinal tract, in its passage in the corona radiata and in the posterior limb of the internal capsule. Additional significant impact was found to lesions affecting the putamen and the external capsule. Balance, gait, and hemiparetic lower limb function showed much overlap of the corresponding ‘significant’ voxel clusters. Test scores of RHD and LHD patients were affected largely by damage to homologous regions, with the LHD group showing a wider distribution of ‘significant’ voxels. The study corroborates and extends previous findings by demonstrating that balance control, gait, and lower limb function are all affected mainly by damage to essentially the same brain structures, namely - the corticospinal tract and adjacent structures in the capsular-putaminal region. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. There (...) are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

Background and ObjectiveAbsolute angle represents the inclination of a body segment relative to a fixed reference in space. This work compares the absolute and relative angles for exploring biomechanical gait constraints.MethodsGait patterns of different neuromotor conditions were analyzed using 3D gait analysis: normal gait, Cerebral Palsy, Charcot Marie Tooth and Duchenne Muscular Dystrophy, representing central and peripheral nervous system and muscular disorders, respectively. Forty-two children underwent gait analysis: 10 children affected by CP, 10 (...) children by CMT, 10 children by DMD and 12 healthy children. The kinematic and kinetic parameters were collected to describe the biomechanical pattern of participants’ lower limbs. The absolute angles of thigh, leg and foot were calculated using the trigonometric relationship of the tangent. For each absolute series, the mean, range, maximum, minimum and initial contact were calculated. Kinematic and kinetic gait data were studied, and the results were compared with the literature.ResultsStatistical analysis of the absolute angles showed how, at the local level, the single segments behave differently depending on the pathology. However, if the lower limb is studied globally, a biomechanical constraint emerges.ConclusionEach segment compensates separately for the disease deficit so as to maintain a global biomechanical invariance. Using a model of inter-joint co-variation could improve the interpretation of the clinical gait pattern. (shrink)

We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and (...) representing new kinds of situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree, K nearest (...) neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

BackgroundAlthough gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.PurposesTo assess the efficacy of the Stable Sparse Classifiers procedure for discriminating young and healthy older adults, as well as healthy and cognitively impaired elderly groups from their gait patterns. To identify the walking tasks or (...) combinations of tasks and specific spatio-temporal gait features that allow the best prediction with SSC.MethodsA sample of 125 participants was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index, based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves and the stable biomarkers were identified.ResultsThe discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance. The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers.ConclusionsThe study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques. (shrink)

BackgroundGait Analysis of healthy people, imitating pathological conditions while walking, has increased our understanding of biomechanical factors. The influence of the pelvis as a biomechanical constraint during gait is not specifically studied. How could mimicking a pelvic attitude influence the dynamic mechanical interaction of the body segments? We proposed an investigation of the pelvic attitude role on the gait pattern of typically developed people when they mimicked pelvic anteversion and posteroversion.Materials and methodsSeventeen healthy volunteers were enrolled in (...) this study. They simulated a pelvic anteversion and posteroversion during walking, exaggerating these postures as much as possible. 3D gait analysis was conducted using an optoelectronic system with eight cameras and two force plates. The kinematic, kinetic, and spatio-temporal parameters were compared between the three walking conditions.ResultsIn Pelvic Anteversion gait we found: increased hip flexion, increased knee flexion during stance, and reduction of ankle flexion-extension Range of Motion compared with Pelvic Normal gait. In Pelvic Posteroversion gait compared with PN, we found: decreased hip flexion-extension RoM with a tendency to hip extension, decreased knee maximum extension in stance, and increased ankle maximum dorsiflexion in stance.ConclusionThe configuration of PA contains gait similarities and differences when compared with pathologic gait where there is an anteversion as seen in children with Cerebral Palsy or Duchenne Muscular Dystrophy. Similarly, attitudes of PP have been described in patients with Charcot-Marie-Tooth Syndrome or patients who have undergone Pelvic Osteotomy. Understanding the dynamic biomechanical constraints is essential to the assessment of pathological behavior. The central nervous system adapts motor behavior in interaction with body constraints and available resources. (shrink)

It is no inconsiderable endeavor to undertake a philosophical analysis of the notions of inertia and absolute space from Newton to Einstein. This is all the more so insofar as Ghins' approach is far from the orthodoxy of the logical empiricists: his claim is that the scientists themselves were seeking to attribute the effects of inertia to real causes, or, in other words, "to specify adequately the system or systems of reference relative to which motions, whether accelerated or (...) not, would give rise or not to such effects". In terms of this causal point of view, he then defines the fundamental question as that of the determination of the inertial or noninertial character of a motion or of a reference system. (shrink)

Classical electron theory with classical electromagnetic zero-point radiation (stochastic electrodynamics) is the classical theory which most closely approximates quantum electrodynamics. Indeed, in inertial frames, there is a general connection between classical field theories with classical zero-point radiation and quantum field theories. However, this connection does not extend to noninertial frames where the time parameter is not a geodesic coordinate. Quantum field theory applies the canonical quantization procedure (depending on the local time coordinate) to a mirror-walled box, and, in general, (...) each non-inertial coordinate frame has its own vacuum state. In particular, there is a distinction between the “Minkowski vacuum” for a box at rest in an inertial frame and a “Rindler vacuum” for an accelerating box which has fixed spatial coordinates in an (accelerating) Rindler frame. In complete contrast, the spectrum of random classical zero-point radiation is based upon symmetry principles of relativistic spacetime; in empty space, the correlation functions depend upon only the geodesic separations (and their coordinate derivatives) between the spacetime points. The behavior of classical zero-point radiation in a noninertial frame is found by tensor transformations and still depends only upon the geodesic separations, now expressed in the non-inertial coordinates. It makes no difference whether a box of classical zero-point radiation is gradually or suddenly set into uniform acceleration; the radiation in the interior retains the same correlation function except for small end-point (Casimir) corrections. Thus in classical theory where zero-point radiation is defined in terms of geodesic separations, there is nothing physically comparable to the quantum distinction between the Minkowski and Rindler vacuum states. It is also noted that relativistic classical systems with internal potential energy must be spatially extended and can not be point systems. The classical analysis gives no grounds for the “heating effects of acceleration through the vacuum” which appear in the literature of quantum field theory. Thus this distinction provides (in principle) an experimental test to distinguish the two theories. (shrink)

ObjectiveTo determine whether gait and accelerometric features can predict disorientation events in young and older adults.MethodsCognitively healthy younger and older participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized (...) mixed effect models. The accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines and Hidden Markov models.ResultsBayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.ConclusionDisorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events. (shrink)

In this work, a virtual submerged floating operational system based on parallel and serial robotic platforms is proposed. The primary aim behind its development lies in carrying out simulated underwater manipulation experiments in an easier and safer way. This VSFOS is consisted of a six-degree-of-freedom parallel platform, an ABB serial manipulator, an inertial sensor, and a real-time industrial computer. The 6-DOF platform is used to simulate the movement of an underwater vehicle, whose attitude is measured by the (...) inertial sensor. The ABB manipulator, controlled by the real-time industrial computer, works as an operational tool to perform underwater manipulation tasks. In the control system architecture, software is developed to receive the data collected by the inertial sensor, to communicate and send instructions. Furthermore, the real-time status of the manipulator is displayed in this software. To validate the proposed system, two experiments have been conducted to test its performance. In the first experiment, the test is carried out to check the communication function of VSFOS, while in the second one, the manipulator is intended to follow the movement of the parallel platform and perform simulated operational task in the space. The obtained results from these two experiments show clearly the effectiveness and the performance of the proposed VSFOS. (shrink)

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health (...) status of the power grid sensor network. The LPR-MLP model consists of two parts: local binary pattern, principal component analysis, and ReliefF are used to process image data and meteorological and mechanical data and the multilayer perceptron method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods. (shrink)

Introduction -- Conceptual analysis of resolution -- Ontology development method -- Resolution of single observations -- Resolution of observation collections -- Ontology design patterns for resolution -- Ontology of resolution -- implementation stage -- Conclusion.

The external worlds do not objectively exist for living systems because these worlds are unknown from within systems. How can they escape solipsism to survive and reproduce as open systems? Living systems must construct their hypothetical models of external entities in the form of their internal structures to determine how to change states (i.e., sense and act) appropriately to achieve a favorable probability distribution of the events they experience. The model construction involves the generation of symbols referring to external entities. (...) This paper attempts to provide a new view that living systems are an inverse-causality operator. Inverse causality (IC) is an algorithmic process that generates symbols referring to external reality states based on a given data sequence. For applying this logical model involving if–then entailments to living systems involving material interactions, the cognizers-system model was employed to represent the IC process; here, living systems were modeled as a subject of cognition and action. A focal subject system is described as a cognizer composed of sub-cognizers, such as a sensor, a signal transducer, and an effector. Analysis using this model proposes that living systems invented the “measurers” for conducting IC operations through their evolution. (shrink)

The subject of this study is the special theory of relativity (SRT) by A. Einstein, the debate about which has been going on for more than a hundred years. The aim of the study is to evaluate SRT from the side of whether everything that is discussed in this theory and thus in the new, relativistic physics is possible in nature itself. At the same time, the author pays special attention to three issues: the principle of relativity, the necessary condition (...) for the objectivity of the content of scientific theories and the Einstein style of scientific thinking, which predetermined the ideological component of SRT and all relativistic physics. But such issues as relativistic reductions of lengths and durations, the relativity of simultaneity and a single space-time do not remain without attention. The study revealed the fallacy of a long-standing and widespread opinion about the relativity of properties. The necessary condition for the objectivity of scientific theories is revealed. The natural form of expression of quantities is revealed and the non-relative nature of all natural quantities is shown. The hidden form of relativity inherent in the SRT postulate about the constancy of the speed of light in all inertial reference frames and extended in SRT to the magnitude of all lengths and durations considered in it is revealed. It is shown that such relativity is based on the dependence of the quantities on the choice of their reflection systems. It is shown that such dependence is impossible in nature itself. It is concluded that the SRT is erroneous. At the same time, the factors that served as favorable ground for the emergence and spread of this erroneous theory are listed. (shrink)

Measurement in soft systems generally cannot exploit physical sensors as data acquisition devices. The emphasis in this case is instead on how to choose the appropriate indicators and to combine their values so to obtain an overall result, interpreted as the value of a property, i.e., the measurand, for the system under analysis. This paper aims at discussing the epistemological conditions of the claim that such a process is a measurement, and performance evaluation is the case introduced to (...) support the analysis, performed in systematic comparison with the paradigm of measurement of physical quantities. Some background questions arising here are: – Are the chosen indicators appropriate performance indicators? – Do such indicators convey complete and non-redundant information on performance? – Does the chosen combination rule generate results suitably interpretable as performance values? And enlarging the focus: – Does the obtained value specifically convey information on the system under analysis, instead of some different entity (typically including the subject who is evaluating)? Operatively: would different subjects evaluate the same system in the same way? i.e., is the obtained information objective? – Does the obtained value convey information that is interpretable in the same way by different subjects? Operatively: would different subjects who have agreed on a decision procedure make the same decision from the same performance information? i.e., is the obtained information intersubjective? Any well founded positive answers to these questions significantly support a structural interpretation of measurement encompassing both physical and soft measurement. (shrink)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender (...) systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI. (shrink)

An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists (...) of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants. (shrink)

Security issue against different attacks is the core topic of cyberphysical systems. In this paper, optimal control theory, reinforcement learning, and neural networks are integrated to provide a brief overview of optimal robust control strategies for a benchmark power system. First, the benchmark power system models with actuator and sensor attacks are considered. Second, we investigate the optimal control issue for the nominal system and review the state-of-the-art RL methods along with the NN implementation. Third, we (...) propose several robust control strategies for different types of cyberphysical attacks via the optimal control design, and stability proofs are derived through Lyapunov theory. Furthermore, the stability analysis with the NN approximation error, which is rarely discussed in the previous works, is studied in this paper. Finally, two different simulation examples demonstrate the effectiveness of our proposed methods. (shrink)

Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the (...) music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion. (shrink)

Implementing the “hierarchical diagnosis and treatment” system highlights the important role of general practitioners as “residents’ health gatekeepers.” Still, the low level of career growth always limits the realization of their service value. Inertial thinking uses a single factor to explain the complexity of career growth in previous studies; in fact, it isn’t easy to assess whether the factor is a sufficient and necessary condition for a high level of career growth. Herein, we have used a set theory (...) perspective to analyze the mechanism of influencing high-level career growth by combining psychological and organizational factors. This research aims to analyze causal complexity relationship between these conditions and results is analyzed in detail. We choose fuzzy-set qualitative comparative analysis with a sample of 407 GPs to test 5 antecedent conditional variables that can affect their career growth. The variables include professional identity, self-efficacy, achievement motivation, training mechanism, and incentive mechanism. To ensure the universality and diversity of data, the samples were selected from community medical institutions in different regions of China. The results show that three pathways can affect the high career growth of GPs, and the optimal pathway A2 is the linkage matching of high incentive mechanism, high professional identity, high achievement motivation, and high self-efficacy. At the same time, we find that professional identity plays an alternative role in the three pathways. When professional identity is at a high level, as long as achievement motivation and self-efficacy are superior, or achievement motivation, self-efficacy, and achievement motivation are superior, a high level of career growth can be achieved. We broke the shackles of previous studies that only focused on the impact of single factors on the career growth of GPs. From the perspective of set theory, we use configurational thinking to construct Influential pathways of high career growth of GPs by integrating antecedents. The results can provide effective support for improving GPs’ service ability and realizing their service value to protect residents’ health. (shrink)

In this paper, through research and analysis of the communication network of the physical activity monitoring system, we combine wearable technology and identification technology and design a physical health monitoring bracelet that integrates multifaceted physical data collection and effective identity matching function. We match the identity through the chip and collect the physical fitness data generated in the process of exercise and centralized test by the sensor in real-time. Finally, the data transmission is realized through the WIFI (...) communication function to achieve the purpose of monitoring physical exercise and improving physical quality. To ensure the continuity and stability of information transmission, the joint transmission method of direct transmission and indirect transmission is essential. Besides, considering the energy causality limitation of sensor nodes and relay nodes, a collaborative transmission model of wireless body area network based on wireless cognitive network is constructed. And, a power allocation algorithm based on maximum ratio merging and wireless cognitive network is proposed, which puts forward a new idea for the future research of wireless body area network resource allocation. (shrink)

Landslide is a critical natural geological hazard that causes severe damage to property, infrastructure, and humans. In general, some location-specific factors trigger a landslide. Wireless sensor network is an enabling technology to monitor most of the parameters associated with these factors. A challenge in landslide monitoring through WSN is that each sensed data item might be critical whereas the underlying wireless communication is often unreliable. In case of landslides, the terrains have irregular shapes, providing harsh conditions for wireless communication (...) thereby more data loss may be expected. This study focuses on the effect of lossy communication in WSN on the efficiency and accuracy of landslide monitoring systems. To this end, collaborative local data analysis is used to enable each node to decide locally whether its sensed data corresponds to a potential event-of-interest. Through extensive simulations, the performance of various landslide prediction and detection models has been evaluated. By and large, the study lets a significant insight into landslide monitoring before implementing a parameterized application for real-world deployment. (shrink)

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion (...) analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments. (shrink)

Currently, there are no computerized tools enabling objective interpretation of observational gait assessment based on Wisconsin Gait Scale, which is a reliable and well-tested tool. The solution envisaged by us may provide a practical tool for assessing gait deviations in patients with hemiparesis after stroke. The present study assessed agreement between a new application software for computerized WGS and 3-dimensional gait analysis, and reliability of the application. The study involved 33 individuals with hemiparesis after stroke. (...) The software was developed based on a model designed taking into account components of the WGS and incorporating auxiliary lines passing through the relevant anthropometric points on the patient’s body, as well as measurements of angular values, distances and duration of the specific gait phases, which make it possible to substantiate assessment based on this scale. Series of videos were made to record gait of the qualified patients. After the gait evaluation was carried out using the app, the data were retrieved from the software. The gait assessment was performed separately by three independent examiners who reviewed the video recording using the new app twice. Additionally, 3DGA was carried out for all the subjects, and the results of the app-aided assessment were compared to those acquired using 3DGA. The findings show statistically significant correlations between majority of the WGS items measured using the new app, and the relevant spatiotemporal and kinematic parameters identified by 3DGA. Agreement between the scores reported by the three examiners was high in both measurements, as reflected by Cronbach’s alpha exceeding 0.8. The findings reflect very good intra-observer reliability and inter-observer reliability of the new application software for computerized WGS. The opportunities offered by the observational gait scale objectified through our new software for computerized WGS result from the fact that the tool provides a useful low-cost and time-effective feedback to monitor ongoing treatments or formulate hypotheses. (shrink)

The concept of inertial frame of reference in classical physics and special theory of relativity is analysed. It has been shown that this fundamental concept of physics is not clear enough. A definition of inertial frame of reference is proposed which expresses its key inherent property. The definition is operational and powerful. Many other properties of inertial frames follow from the definition, or it makes them plausible. In particular, the definition shows why physical laws obey space and (...) time symmetries and the principle of relativity, it resolves the problem of clock synchronization and the role of light in it, as well as the problem of the geometry of inertial frames. (shrink)

Much of the discussion on business ethics is philosophical in nature. There is no lack of theories and ideals on moral reasoning. What is missing is translating these moral theories and principles into specific, operational procedures that can indicate a proper course of action. Although most business actions are routine and do not raise serious ethical questions, many people experience difficulty in applying their personal moral principles to specific business decisions in ethically-dilemmatic situations.This study seeks to develop a framework that (...) can be utilized to implement personal moral reasoning based on the teleological theory of Utilitarianism and the deontological theory of Ross''s Prima Facie Duties in the business decision-making process. The central feature in the framework is a point-system that quantities the ethical worth of a proposed business action and determines whether the action would be ethically desirable if taken. It provides an objective element in an otherwise qualitative ethical inquiry process. This study also illustrates practical applications of the system by analyzing the ethical implications of a proposed action where foreign bribery is involved. (shrink)

On the basis of ecological footprint theory and tourism ecological footprint theory, the sustainable development indexes such as ecological footprint, ecological carrying capacity, ecological deficit, and ecological surplus of the research area were calculated and the long-term change pattern of each index was analyzed. This paper shows that the ecological footprint of the research area increases year by year, but the ecological footprint is always smaller than the ecological carrying capacity, indicating that the area is still in the state of (...) sustainable development. However, the per capita ecological surplus shows a decreasing trend year by year, indicating that the sustainable development of the region is getting worse. This paper proposes a reordering method of tourist attractions based on heterogeneous information fusion, and realizes the retrieval and reordering of tourist attractions based on user query and fusion of heterogeneous information, so as to help users make travel decisions. In view of the shortage of tourism commercial websites to passively provide scenic spot information, this paper puts forward a scenic spot retrieval method based on query words to enable users to obtain scenic spot information according to their needs, and constructs a tourist consumer data analysis system. The preprocessing methods and methods adopted by the data preprocessing module are analyzed in detail, and the algorithms used in the travel route analysis and consumer spending ability analysis are described in detail. The data of tourism consumers are analyzed by this system, and the results are evaluated. (shrink)

It is suggested that the mathematically abstract coordinate frames of reference commonly visualized to be centered at the celestial bodies have real counterparts in the shape of well-defined rigid spatial resonant singularities of infinite extension, which accommodate the matter waves from the superimposition of which the body residing at the coordinate origin results. A universally valid inertial reference frame system is proposed. Qualitative explanations are offered for the inertial and gravitational forces, their observed proportionality, and for the (...) occurrence of second-order gravitational effects in the vicinity of massive bodies. The universal redshift is assumed to result from a closure condition of the eigenspaces introduced. (shrink)

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and (...) displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results. (shrink)

The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of ‘somatic surveillance’ increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication ‘a dissonance of attribution’ :7684, 2019. https://doi.org/10.1073/pnas.1805770115) (...) is created between perceptions of phenomena, materials and decision-making. This reconfigures relations not only between humans and machines, objects and subjects, but possibly disrupts attributive functions in the social system of Law. What it requires is shifting a legal accountability for action from a sovereignty of the human to a new materialist account based on a ‘cognitive assemblage’ between physiological data, computation and algorithmic sensing. This paper investigates the function of law as a guidance system to acknowledge this account of sensory and algorithmic computation as autonomous ‘sensing agents’ that may be accountable in situations of risk. This assemblage of robotic computation and sensory determination requires a clearer legal differentiation across the current static terminologies of person, property, liability and rights that maintain strict separations of object from subject. To neglect this, we argue, law will solely impute attributions of error to humans despite evidence of operation via mutual control. (shrink)

Music can express people’s thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatment and evaluation.” It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths (...) for the development of the medical field. This paper proposes an long short-term memory -based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model. (shrink)

Real-world walking activity is important for poststroke patients because it leads to their participation in the community and physical activity. Walking activity may be related to adaptability to different surface conditions of the ground. The purpose of this study was to clarify whether walking adaptability on an uneven surface by step is related to daily walking activity in patients after stroke. We involved 14 patients who had hemiparesis after stroke and 12 healthy controls. The poststroke patients were categorized as least (...) limited community ambulators or unlimited ambulators. For the uneven surface, the study used an artificial grass surface. The subjects repeated even surface walking and the uneven surface walking trials at least two times at a comfortable speed. We collected spatiotemporal and kinematic gait parameters on both the even and uneven surfaces using a three-dimensional motion analysis system. After we measured gait, the subjects wore an accelerometer around the waist for at least 4 days. We measured the number of steps per day using the accelerometer to evaluate walking activity. Differences in gait parameters between the even and uneven surfaces were calculated to determine how the subjects adapted to an uneven surface while walking. We examined the association between the difference in parameter measurements between the two surface properties and walking activity. Walking activity significantly and positively correlated with the difference in paretic step length under the conditions of different surface properties in the poststroke patients and step width in the healthy controls. The strategy of increasing the paretic step length, but not step width, on an uneven surface may lead to a larger base of support, which maintains stability during gait on an uneven surface in poststroke patients, resulting in an increased walking activity. Therefore, in poststroke patients, an increase in paretic step length during gait on an uneven surface might be more essential for improving walking activity. (shrink)

Cognitive, or knowledge, agents, who are in some way aware of describing their own view of the world (based on their mental concepts), need to become concerned with the expressions of their own conceptions. My main supposition is that agents’ conceptions are mainly expressed in the form of linguistic expressions that are spoken, written, and represented based on e.g. letters, numbers, or symbols. This research especially focuses on symbolic conceptions (that are agents’ conceptions that are manifested in the form of (...) their symbols). I attempt to logically (and using ????LSYM) analyse symbolic conceptions in terminological systems. ????LSYM is an assertional fragment of my developed Conception Language (????L), which is utilised as a formal-logical system for representing and explicating agents’ conceptions of the world. Keywords: assertional logic; cognitive agent; knowledge agent; conception language; conception; symbol; symbolic conception. (shrink)