The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance. There are various challenges in the analysis of multivariate high-dimensional systems, such as in the analysis of financial and neurophysiological data. The goal of each application and the features of the examined data should be considered in order to determine the suitable connectivity analysis scheme. For example, financial time series are nonstationary, contain nonlinearities and exhibit volatility clustering, whereas data in neuroscience experiments may present a high temporal resolution, be subject to artifacts, periodic respiratory or cardiac noise.Connectivity analysis focuses on identifying the interdependence relationships of the variables of a complex system. Connectivity measures can be subdivided into two main categories based on whether they quantify the direction of a relationship. Nondirectional measures assume that variables evolve in synchrony and the connectivity is examined based on symmetric measures, such as dependence measures. On the other hand, directed measures quantify the causal effects among the variables, assuming that causes precede their effects in time, such as Granger causality measures . A furthSince there is an abundance of connectivity measures that have been developed so far, there is also an urgent need to compare them and clarify the usefulness of each method. Comparisons are mainly performed in terms of applications of interest. Indicatively, comparisons of correlation measures can be found in ,9,10,11,There are a number of limitations and common pitfalls regarding the connectivity measures that complicate the correct inference of the connectivity of a system. For example, correlation measures are usually assuming linearity and do not handle outliers, are affected by the sample size and there are also interpretational mistakes when discussing their outcomes ,33,34,35The purpose of this paper is to provide a brief review on connectivity analysis, enlisting the most well known connectivity measures and identifying the possible pitfalls when forming connectivity networks. Selecting between correlation and causality measures for inferring the connectivity structure of an examined system is of the key interest of this study; therefore, it is essential to demonstrate the performance and pitfalls of the different connectivity measures focusing on those that have not been stressed and investigated extensively so far. In particular, we examine the influence of the existence of contemporaneous interdependencies to the extracted causal network, the effect of causal relationships when forming correlation networks and finally, inquire the case of having both contemporaneous and causal relationships among the variables of a multivariate system.The examination of the dependencies among the variables of a complex system is an essential task in statistics. Non-directional, symmetric connectivity measures aim to capture and quantify the strength of associations between the variables of a complex system, without indicating the direction of the relationship. Correlation measures are utilized for the characterization of statistical relationships.The most commonly used correlation measure is the Pearson correlation coefficient, also known as the Pearson product-moment correlation coefficient, or bivariate correlation or correlation coefficient . It is eMany alternatives to the correlation coefficient have been developed, aiming to address its limitations. Rank-based analogues of the correlation coefficient do no make any assumptions about the frequency distribution of the variables and do not assume that a linear relationship between the variables exists, whereas they can be used both for variables measured on interval scales but also at ordinal levels. Spearman’s rank correlation coefficient  and KendPartial correlation measures quantify the degree of association between two variables while controlling for the effect of any additional variables of the examined system. Their main contribution is that they can uncover spurious relationships and detect hidden relationships. The most common partial measure is the linear partial correlation coefficient, which can be estimated based on the bivariate linear correlation coefficients of the examined variables. Its nonparametric analogue is the partial Spearman’s rank correlation coefficient.Hoeffding’s test of independence is a test of correlation for two variables with continuous distribution functions . MultivaThe coefficient of determination is based on linear regression models  and is eQ and the coefficient of colligation or Yule’s Y, respectively. Various different extensions have been defined subsequently, such as Digby’s coefficient H [The odds ratio quantifies the dependence between two binary variables and ranges from zero to infinity. Yule ,53 introicient H  and coefnt H [Y* .Copulas are used to model the dependence between random variables. For example, the Randomized Dependence Coefficient measures the dependence between multivariate random variables . Copula The continuous analysis of variance test (CANOVA) is a nonlinear correlation measure defined by the neighborhoods of the data points of two continuous variables . The ratAn ensemble of information-theoretic alternatives to the linear correlation coefficient aim to account for nonlinearities and temporal dependencies in the data. Mutual information is used as a generalized correlation measure that quantifies nonlinear associations ,66. MutuNonlinear correlation information entropy is specified by the rank sequences that are obtained from the original data sets . The entTime domain measures express the variation of amplitude of signal with time. Frequency domain analysis of signals is performed in reference to frequency, rather than time. Coherence function quantifies linear correlations in the frequency domain . Cross-cPhase synchronization stems from the notion of synchronization of chaotic oscillators, whereas the global diversion of the phases of the signals is examined ,78. DiffPhase locking is a key notion in dynamical systems. Phase locking value is a measure robust to fluctuations in amplitude that quantifies the absolute value of the mean phase difference between two signals ,78.Recurrence analysis examines how close the states of a dynamical system are after some time . Its extWe should note here that most correlation measures are bivariate since extensions of them in the multivariate case most probably lead to directional measures, such as the conditional mutual information, which is an extension of mutual information that accounts for the remaining variables of the system which infers about the directionality of the information flow ,86,87.A list of some well known correlation measures in time and frequency domain is displayed in The different correlation measures have been vastly applied in different fields, such as in finance, neurophysiology, meteorology, biology and engineering. Applications include the identification of genomic associations ,90, the Directional connectivity measures seek to infer the direction of the relationship from the data samples, relying on the principle that causes precede their effects. The most common procedure of causal discovery is Granger causality, where probabilistic causation relies on the concept that causes change the probabilities of their effects ,110.Model-based directional approaches assume the linearity of interactions. The standard linear Granger causality is the pioneer technique based on autoregressive models that seeks to determine whether prediction of the target (driven) variable can be improved by exploiting past values of the source (driving) variable .Various model-based extensions of the standard Granger causality test have been developed so far. The conditional Granger causality is its multivariate extension that exploits all the available information of the observed data . PartialNon-parametric extensions of Granger causality to nonlinear cases in the time domain include the Baek and Brok test , the HieNumerous directional measures stem from information theory. These model-free approaches infer linear but also nonlinear interactions. Transfer entropy is the most well-known information measure for studying directed interactions . PartialLinear cross-correlation is the simplest and most well known synchronization measure defined as the ratio of covariance to root-mean variance of the two signals. Event synchronization is another simple and computationally efficient method that quantifies synchronicity and time delay patterns between signals .Various nonlinear interdependence measures have been developed in regards of nonlinear prediction theory that use the neighborhoods of the reconstructed points of the state space aiming to determine the nonlinear driver-response relationships ,133,134.Graphical models have been suggested by  to accouThe field of causal discovery was signified by ,139, wheThe Peter Clark momentary conditional independence algorithm is a causal discovery method that incorporates linear or nonlinear conditional independence tests to determine the causal networks from multivariate time series data . This meData from time domain can be converted to the frequency domains with mathematical operators, such as the Fourier transform, which converts a time function into a sum or integral of sine waves of different frequencies. Causality measures from the frequency domain have been widely applied for the analysis of neurophysiological data. The majority of the developed spectral measures are based on linear models, and thus can only detect linear causal effects in the frequency domain, such as Geweke’s spectral Granger causality , the dirNonparametric methods have been also employed, such as the nonparametric approach based on Fourier and wavelet transforms in , the nonGranger causality relationships are examined by considering the past values of the involved variables. However, the prediction of the target variable, may at cases be improved by including the available current information of the source variable. In such a case, the instantaneous causality relation between the source and target variable should be considered . For exaWithin the framework of stationary autoregressive modeling, the instantaneous causality is usually tested by using Wald tests for zero restrictions on the innovation’s covariance matrix. Extended Granger causality accounting for zero-lag effects in the linear regression schemes implemented by the VAR model . InstantA causality framework in frequency domain that considers instantaneous effects is introduced in . An instCompensated transfer entropy is a nonlinear causality measure that regards contemporaneous relationships ,169. A mA list of well known directional connectivity measures in time and frequency domain is displayed in The pioneer Granger non-causality test has been developed for analyzing financial data ; howeverThe estimation of symmetrical and causal relationships from observational data has been vastly explored, along with the limitation and pitfalls of the corresponding measures ,189,190.Linear model-based connectivity measures assume linearity of the relationships ,192, wheIn general, measures of connectivity are biased, and, therefore, under the null hypothesis of no connectivity the estimates will be different from zero. Accurate estimation of connectivity measures requires sufficient sample sizes . GuideliReal data may entail various types of noise and noise levels; there are different data measurement methods that may entail measurement errors depending on the application. For example, noise in financial data may stem from small price movements and trading noises that illustrate heavy tails. The effect of noise on correlation measures has been examined in different studies ,201,202.There are various reasons for inferring spurious causal effects, such as due to unobserved variables, contemporaneous relationships, common inputs, synergetic and redundant influences and strong autocorrelations in the sample. Another difficulty in causal inferring is the discrimination between direct and indirect interactions when common inputs exist, although direct causality measures have been developed for this. Robust methods that can account for latent effects of unobserved variables are an open area of investigation in connectivity analysis ,207,208.The determination of causal directionality for contemporaneous links is an emerging area. An instantaneous causal effect can be interpreted as a zero-lag causality or as a symmetric causal relationship. However, it has been noted that instantaneous causality may arise in case of common sources and latent, unobserved variables ,171.A plethora of connectivity measures have been briefly discussed above, along with some main pitfalls and limitations. However, a key question that arises is whether to apply symmetric correlation measures or directional causality measures to infer the connectivity network of an examined system. Since connectivity of real systems is unknown, the nature of the examined data and the performance of the connectivity measures are of great importance.Therefore, we generate synthetic time series with known connectivity structures and demonstrate the efficacy of the connectivity measures in three different scenarios. In particular, we examine the influence of different types of dependencies in the samples to the efficiency of the connectivity measures. First, we consider a simulation system with only contemporaneous dependencies and explore the performance of the connectivity measures and in particular of the causality measures. The second simulation system demonstrates the effect of time-lagged directional relationships on the connectivity measures and in particular on correlation measures that are not defined in order to detect lagged dependencies. Finally, we consider a system with contemporaneous and time-lagged directional relationships and examine the performance of the connectivity measures. To better simulate real data which are usually non-normal, the noise terms of the considered stochastic simulation systems are not exclusively Gaussian, as usually assumed in the literature, but also skewed and non-symmetrical noise terms are regarded.Z. The multivariate connectivity measures are similarly defined; however, instead of Z, an ensemble of conditioning variables Based on the equations of each simulation system, 100 realizations with sample size The aim of the study is to provide insights on the effectiveness of the different types of connectivity measures. Therefore, an indicative selection of measures is performed since it is impossible to include the ensemble of existing connectivity measures. The examined measures cover the most commonly used types of connectivity measures.X and Y. Estimation of PPCor is performed based on “partialcorr” function from the Matlab Statistics Toolbox.Partial linear Pearson correlation coefficient (PPCor) Partial Spearman rank correlation coefficient (PSpCorr), defined similarly to PPCor but on the series of the ranks. Estimation of PSpCorr is performed based on “partialcorr” function from the Matlab Statistics Toolbox.Partial distance correlation (pdCor) is the extension of the distance correlation (dCor) in the multivariate case. The distance correlation of two random variables is obtained by dividing their distance covariance by the product of the distance standard deviations, i.e.,  product . Estimat product .X. The k-nearest neighbors (KNN) estimator has been utilized for the estimation of MI [Mutual information (MI) = on of MI .The considered correlation measures are the following ones:X: X variable and https://github.com/dkugiu/Matlab/ (accessed on 23 October 2021).Conditional Granger causality index (CGCI) is defined on the unrestricted and restricted vector autoregressive model (VAR) of order P, fitted to the time series of https://users.auth.gr/dkugiu/ (accessed on 23 October 2021).Restricted conditional Granger causality index (RCGCI) is defined similarly to CGCI, however a modified backward-in-time selection method is used and a subset of lagged terms enter the unrestricted VAR model. Matlab codes for the computation of RCGCI can be found in Y on X in the presence of the “appropriate” past terms of all the variables X one step ahead. Matlab codes for the estimation of PTENUE can be found in http://www.lucafaes.net/its.html (accessed on 23 October 2021).Partial transfer entropy on non-uniform embedding (PTENUE) measures the direct effect of f, it is given as P and Partial directed coherence (PDC) is based on VAR models as CGCI; however, it is defined in the frequency domain. For a frequency The regarded causality measures are listed below:Partial mutual information on mixed embedding (PMIME0) is an extension of the causality measure PMIME, that also contains zero lag terms. For the estimations, the Matlab code was provided by the authors .https://github.com/jakobrunge/tigramite (accessed on 23 October 2021).Peter Clark momentary conditional independence algorithm (PCMCI+) addresses both lagged as well as contemporaneous causal discovery. Its an extension of PCMCI, which searches for causal parents based on conditional independence tests. The information-theoretic framework is considered here where the conditional mutual information is utilized as a general test statistic. Computations are performed using the python codes in Finally, two instantaneous causality measures are assumed in this study:t-distribution with p-values are then estimated from a one sided-test for the null hypothesis that two variables are independent. The number of permuted time series is set to be equal to 100. The significance of pdCor is also assessed using 100 permutations. As previously stated, the PDC is estimated for a range of frequencies in Standard free parameters and significance tests are utilized for each connectivity measure. In particular, the significance test for PPCor and PSpCorr is parametrically extracted based on the test statistic A parametric significance test is employed for CGCI and RCGCI since these measures are also defined on VARs . Order oFirst, we consider a five-variate stochastic nonlinear simulation system where by construction, data have known contemporaneous dependencies and there are no causal influences. The equations of the system are the following:d X2, X5 a.Correlation measures are symmetrical; therefore, results are displayed by upper triangular Tables. The PPCor detects the linear correlation between izations . PSpCor On the other hand, all the direct causality measures correctly suggest that no causal effects exist among the variables of the first simulation system. RCGCI, PTENEUE, PDC and PCMCI achieve low percentages of significant causal links over the 100 realizations  model of order 3 in five variables is formed. The systems’ equations are given below:3, X4→X5 b.In the second simulation system, correlation measures seem to be affected by the temporal dependencies and indicate significant contemporaneous dependencies . In partThe linear causality measures CGCI and RCGCI infer correctly the causal relationships, however the nonlinear link The nonlinear causality measure PTENUE also correctly indicates the directional linkages; however, the nonlinear effect Regarding the instantaneous causality measures, PMIME0 does not identify contemporaneous relations, and suggests the correct causal effects. As noted in the first simulation system, the percentage of significant causal effects for the non-causal links may exceed the nominal level; however, the exported percentages are generally lower compared with those obtained for system 1. PCMCI+ performs worse than PMIME0. It suggests only temporal dependencies; however, large percentages of significant causal effects are noted for non-causal links, where the highest erroneous percentages concern Finally, we consider a system with temporal and contemporaneous dependencies, i.e., lagged and zero-lag dependencies. The causal influences between the variables are known by construction. The equations of the considered system are the following ones:3, X3→X5 c.Correlation measures correctly indicate the contemporaneous relation of ariables . TherefoRegarding the causality measures, PTENUE suggests the correct causal effects but additionally indicates the causal effect from Such a designed system favors the instantaneous causality measures, since both contemporaneous and causal effects exist. PMIME0 correctly identifies the contemporaneous dependence of In this paper, we have presented a brief review of the main connectivity measures currently used to infer the connectivity network of a examined complex system. Connectivity analysis is essential in different applications, such as in finance and neurophysiology. Nondirectional measures indicate the symmetric relationships of variables that evolve in synchrony, whereas directional measures infer the directions of the causal influences. The main limitations of the connectivity measures have been discussed in brief.When studying the interdependencies of a system, connectivity may be inferred based on correlations or causality. However, selecting the proper methodology is still an open issue since the nature of real systems is in general unknown. This study investigated the efficacy of different connectivity measures for different simulated data, whereas complexity was further increased by considering non-normal noise terms in order to generate samples with skewed or/and non-symmetrical distributions. Simulation experiments were used to demonstrate the performance of the connectivity measures in three different scenarios, i.e., when data exhibit only contemporaneous dependencies, only directional causal effects, and finally both contemporaneous and temporal dependencies.(a)Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. Correlation measures tend to erroneously indicate contemporaneous relations even though only lagged dependencies exist.(b)Causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. We should note here that the poor performance of PDC for systems 2 and 3 may be due to the fact that significant PDC values are reported comprehensively for all the examined frequencies. In real applications, usually specific frequency bands are selected according to the types of samples ,212.(c)Instantaneous causality measures handle contemporaneous and causal effects at the same time. Therefore, it seems to be highly promising for analyzing the connectivity structure of real data.Although both considered instantaneous causality measures seem to have potential and effectively infer the dependencies of most examined systems, they tend to give high percentages of significant causal effects for non-causal pairs of variables. This is a problem that explicitly reduces the effectiveness of the measures. The consideration of different values for the free parameters of the measures, such as the significance level or the number of neighbors for PMIME0, may improve the performance of the measures; however, here, only standard values of free parameters are used at all the examined systems for all causality measures. A possible optimization of the free parameters of the measures is out of the scopes of this work. However, the necessity of an automatic selection of standard free parameters of any connectivity measure in case of real applications should be pointed out.The main outcomes of the simulation study can be summarized to the following:The indicative simulation study highlights the limitations and advantages of the different connectivity measures. The outcomes of this study are suggesting the superiority of the causality measures over the correlation measures and the instantaneous causality measures. Correlation measures are highly affected by lagged directional relationships. Instantaneous causality measures, although promising, still need to be optimized to be effectively applied. At this point, we should note that correlation measures are effectively utilized long-term and are suitable for specific data types with possibly known topological features or characteristics, e.g., ,214. FurFuture studies aim to further investigate the above findings by testing additional scenarios regarding the samples and the nature of the dependencies, i.e., by considering samples with longer memory, samples that exhibit volatility clustering and samples of higher dimensions.


Respiration rate is an essential indicator of vital signs, which can demonstrate the physiological condition of the human body and provide clues to some diseases. Commercial Wi-Fi devices can provide a non-invasive, cost-effective and long-term respiration rate-monitoring scheme for home scenarios. However, previous studies show that the breathing depth and location may affect the detectability of respiratory signals. In this study, we leverage the variation of the Doppler spectral energy extracted from the channel state information (CSI) collected by Wi-Fi devices to track the chest displacement induced by respiration. First, the random phase is eliminated by phase-fitting method to obtain the complex CSI containing the Doppler shift. Then, the multipath decomposition of CSI is carried out to obtain the channel impulse response, which eliminates the interference phase of the time delay and retains the Doppler shift. The dynamic path units are also separate from the multipath, which overcomes the indoor multipath effect. Finally, we conduct a time–frequency analysis to dynamic units to accumulate Doppler spectral energy. Based on these ideas, we design a complete respiration rate-monitoring system to obtain the respiration rate by using the consistency between the Doppler energy change period and the respiratory cycle. We evaluate our system through extensive experiments in several typical home environments filled with multipath. Experimental results show that the errors of the three scenarios are approximate, the maximum error is less than 0.7 bpm, and the average errors are approximately 0.15 bpm. This result indicates that our scheme can achieve high precision respiration monitoring and has good anti-multipath ability compared with existing methods.  The past few years have witnessed an evolution of sensing technology and the improvement of people’s awareness of their own health; thus, people prefer ubiquitous health monitoring in the daily life to improve the quality of life. Respiration rate, one of the health metrics of vital sign, provides feedback for physiological conditions, such as sleep quality, cardiac arrest and sleep apnoea ,2. RespiAccordingly, the non-invasive respiration-monitoring scheme based on radio frequency has attracted extensive attention. This scheme uses electromagnetic waves that travel through space to observe breathing movements, and no device is required. Doppler radar ,13 and uLiu et al.  used theTo address this key challenge, we present a new respiration-sensing scheme in this study to extract the Doppler features from CSI, which can be used to detect respiratory movement. We first introduced the respiration-sensing principle based on the Fresnel model and found that the CSI amplitude and phase could not always effectively detect breathing signals. Borhani et al.  used a rWe design a complete set of respiration rate-monitoring system by using commodity Wi-Fi device on the basis of the analytical result. First, our system detects the presence of breathing in the environment. If a breathing signal is detected, then the amplitude and phase of raw CSI are preprocessed to eliminate noise and phase offset. The clear complex CSI is obtained. Then, the Doppler information generated by chest movement is obtained by multipath decomposition and short time Fourier transform (STFT) of the dynamic path units. Finally, the periodic breathing signal is extracted, and the respiration rate is calculated by peak-to-peak distance method. We implement our system by using a pair of commercial Wi-Fi devices and conduct extensive experiments in three typical home environments  to evaluate its performance. The experimental results demonstrate that the system can achieve high accuracy for respiration rate estimation in different scenarios. The mean estimation errors in three scenarios are 0.16, 0.15 and 0.14 bpm. We also evaluated the impact of different influencing factors on the system.The main contributions of this study are summarised as follows: A respiration-sensing model based on the Doppler frequency shift was proposed to solve the problem that the breathing depth and location may affect the detectability of CSI amplitude and phase. The feasibility of extracting Doppler frequency shift for respiration monitoring is theoretically verified. In view of the phenomenon that Doppler caused by weak chest movement in indoor environment is interfered by multipath effect and cannot be directly extracted, we propose a Doppler spectral energy extraction method to effectively extract breathing signals, including multipath decomposition, dynamic path unit extraction and Doppler shift energy accumulation. We design a complete respiration rate-monitoring system and conduct extensive experiments to verify and evaluate its performance in actual indoor home environment. The results demonstrate that our system can accurately estimate the respiration rate and achieve developed performance compared with the existing respiration-sensing system.This work aims to measure human respiration. Before that, we briefly review the previous work related to respiration sensing.2 in the breathing gas, requires the patient to wear a mask or nasal cannula. The three primary methods to measure breath by wearable sensor are as follows: elastomeric plethysmography, impedance plethysmography and respiration inductive plethysmography [In a clinical scenario, respiration is measured by using special equipment. For example, capnometer , which imography . These tmography ,10 to semography ,23 can dDoppler radar ,13 can iHalperin et al.  proposedIn this chapter, we first introduce the basics of CSI. Then, we present the respiration-sensing model based on the Fresnel zone and analyse the limitations of CSI waveform in respiration sensing. Finally, we characterise the Doppler effect in respiratory movement and derive the respiration-sensing model with Doppler frequency shift to analyse the feasibility and specific method of extracting Doppler information from CSI.N is the total number of paths.When the wireless signal propagates in space, certain phenomena, such as reflection, diffraction and scattering on the surface of the object, occur. The received signal is superposition of multiple paths called multipath effect. The signal undergoes complex multipath changes, which can be expressed in the form of the channel impulse response (CIR) in the time domain and the channel frequency response (CFR) in the frequency domain.According to the physical layer definition of the IEEE 802.11n protocol  Wi-Fi siThe principle of Fresnel zone originated from the research of interference and diffraction of light. In the context of radio propagation, this concept refers to a series of concentric ellipse regions with the position of the transceiver as foci . The ellThe length of the reflected path increases as objects move across the Fresnel zone away from the centre in proper order. According to the formula, every time after a Fresnel zone boundary, the reflection path length increases λ/2, and the phase increases π. The phase difference between the direct signal arriving through the LOS path and the signal reflected through the object to the receiver continuously increases. The superposition of the signal strength also alternately increases and decreases with the alternate change of the two signals in phase or out of phase.According to the previous work , the mulThe dynamic path discussed in this study refers to the path of changes caused by periodic breathing through the reflection of the thoracic cavity in the indoor environment. A breathing cycle consists of inflation and deflation of the lungs with pauses between each cycle. When inhaling facing the link, the lungs are inflated, and the chest is close to the transceiver; when out haling, the lungs are deflated, and the chest is away from the transceiver.From the principle of the Fresnel zone previously described, we know that the amplitude of CSI can produce a fragment of the sinusoidal-like waveform when an object moves a distance of λ between the Fresnel zones. With regard to breathing movement, the chest anteroposterior displacement is 4.2–5.4 mm, and the mediolateral dimension is 0.6–1.1 mm during normal exhalation and inhalation . The lenTherefore, the changes in the waveform caused by the respiratory signal do not constitute a complete waveform. Case 1: Case 2: Case 3: The above theoretical analysis indicates that the depth and location of breathing will affect the detection of signal. Monotonous amplitude or phase changes exist in the smallest-depth breathing; thus, an optimal detection location can be observed in the middle or boundary of the Fresnel zone. Non-monotonic amplitude and phase changes exist in the largest-depth breathing. At this time, the CSI value cannot effectively sense respiration. This notion indicates that weaker respiratory signals are easier to recognise; however, weaker signals are more susceptible to environmental noise and multipath effects.In multi-carrier systems, such as OFDM, the Fresnel zone of each carrier has overlapping boundaries, and the difference between the corresponding boundaries will become larger with the increase in the number of zones. This situation may be good for one subcarrier but bad for another. In this case, finding the best perceptual location in the Fresnel zone can be even more complicated.In view of the limitations of detecting breathing signals with CSI waveform, we try to extract the part that directly reflects the respiratory movement from CSI for detection. The Doppler effect generated by thoracic movement during breathing inspired us to extract Doppler frequency from CSI for respiration sensing.The Doppler effect refers to the phenomenon that the frequency of received signal changes when the receiver of the signal moves relative to the transmitter. In the environment of wireless sensing, the transmitted signal is reflected from the moving object to the receiver when the receiver and transmitter remain static, and the carrier frequency of the reflected signal has a Doppler frequency shift . This coThe fundamental reason for Doppler shift is the change in path length of signal propagation. At time ipath in  has the t of arrival of each packet is the fast time dimension, and the propagation time delay τ of the multipath signal is the slow time dimension. The CIR is obtained by time–frequency transformation of CSI in the slow time dimension, whilst the Doppler phase term is only related to the fast time dimension; hence, the Doppler component remains unchanged during this process. Therefore, the process of conversion to CIR can retain the Doppler phase term and eliminate the phase interference caused by the delay.According to the previous definition, CSI is the frequency domain superposition of the CIR. The CIR signal does not involve complex time-varying components; however, it intuitively represents the attenuation and phase offset induced by time delay of the signal in different paths at the current moment. The collected CSI contains 2D time. The time Therefore, we perform multipath decomposition to CSI. Fine-grained path information can be obtained by separating multipath signals in the time domain, and the dynamic path containing Doppler shift can be resolved. Specifically, the CIR is obtained by taking the IFFT of each CSI along the slow time dimension:The CSI has 30 subcarriers with a subcarrier interval of 312.5 kHz. Accordingly, the corresponding The comparison of Equations (5)–(7) illustrates that the multipath decomposition separates the path elements containing the Doppler shift, which reduces the interference of static clutter. In the first three path units, the phase of the dynamic path is mainly determined by the Doppler shift, which creates the conditions for extracting the Doppler shift from the dynamic path. As an example, Fourier transform is applied to the first path unit along the fast time dimension, and the following expression is obtained: Therefore, Doppler information can be extracted from CSI. According to the previous analysis, the static component can be considered a constant, and it is much larger than the dynamic component. The value of We designed a system to monitor human respiration by using CSI data collected from commercial Wi-Fi devices. The specific process is shown in In the data extraction module, the original CSI data are collected by commercial Wi-Fi devices. The fluctuation in CSI is observed to determine the existence of humans in the current environment, and respiration can be detected. In the data preprocessing module, we first remove high-frequency noise and unstable part of the static component in the data. Then, we process the linear fitting error of the unsynchronised phase information to obtain usable phase information. Finally, we obtained cleansed complex CSI information. In the respiration signal extraction module, we first perform multipath decomposition. The IFFT of CSI along the fast time dimension at each moment is used to obtain the CIR. Then, we extract the Doppler information in the first three multipath units containing dynamic components along the fast time dimension. STFT is applied to each multipath unit to obtain the spectrum of the Doppler shift. Finally, we remove the static component of the spectral energy and extract the respiratory signal from the zero-frequency accumulated Doppler spectral energy waveform. In the respiration rate estimation module, the respiration rate was calculated by the method of peak-to-peak distance detection.Our system estimates human breathing rate at rest; hence, we need to determine if the environment is suitable for breathing detection. CSI can be affected by human movement in the environment, and the value of CSI irregularly fluctuates when someone moves around, stands up or sits down in space. The fluctuation at this time is more violent compared to the static state or no one state.Accordingly, a statistical feature-based method was adopted to detect respiratory signals and calculate the sum of the standard deviations of the CSI values within the sliding time window of all subcarriers. Then, a smooth statistic curve was obtained with a moving average filter. In our experimental scenario, we assumed that no one was present in the initial environment. After someone approached the device, sat down and remained stationary, we calculated the respiration rate. The statistics start small and then go back to a smaller value after a sharp rise and fall. We set the threshold to 0.1. When the statistics is above the threshold, we assume someone is nearby. When the statistics is below the threshold for the first time, we collect the CSI for the subsequent operations.The change of the internal state of the device and the electromagnetic interference in the environment often introduce high frequency noise in the CSI. These noises may interfere with the extraction of weak respiratory signal or even drown the signal. Thus, the raw data received must be preprocessed.First, the previous analysis indicated that the static and dynamic component amplitudes are basically stable during breathing; accordingly, the CSI periodically fluctuates on the horizontal line. We use Hampel Filter to detrend the unstable part in the static component. Specifically, we set the sliding window of the filter to 400 samples with a threshold of 0.05. After the basic trend of the original data is obtained, we determined the difference between the basic trend and the trend at the initial moment. Then, we remove this part of the trend from the original data. The main static components in this method are retained to avoid the removal of the unseparated dynamic components to remove the main static components in the following steps.Then, we use Savitzky–Golay Filter  to filteCertain problems, such as the clock of the receiver and the transmitter is not synchronised, and the hardware is not fully compensated, occur due to the circuit characteristics of the equipment. Thus, the original phase of the measured CSI presents a tendency of random distribution and cannot be directly used for detection. We design a special phase sanitisation step to correct the error between the measured and the real values. According to previous studies ,36, the  Carrier frequency offset (CFO): The carrier frequency offset is caused by the incomplete synchronisation of the carrier frequency generated by the oscillator of the receiver and transmitter. The CFO corrector compensated the carrier frequency; however, a residual CFO was still present, which is noted as  100 kHz . Accordi Sampling frequency offset (SFO): In the sampling process of analogue-to-digital convertor, the sampling frequency is offset due to the unsynchronised clock. This phenomenon introduces a time offset termed as  Packet detection delay (PDD): The packets of the Wi-Fi signals are transmitted in the frame format specified by the protocol. The packet detector detects the arrival of a packet by using the preamble. This situation introduces the packet detection time delay The above error sources demonstrate that the measured phase of the subcarrier can be expressed as follows: α and intercept β of the fitting linear function are as follows:We cannot calculate the error of each packet with only NIC. However, all significant phase errors in CSI are linear to the subcarrier index; hence, the phase error term can be regarded as a linear function of subcarrier number k. We used a method similar to SpotFi  to searcIn particular, Equation (3) illustrates that the CSI phase of each path also has the form of a linear function. The total The principle of using Doppler information to sense respiration is that the chest displacement produces the Doppler shift during the inhalation and exhalation. Thereafter, the chest stays still for a short time without a Doppler shift. This notion means that the Doppler change period corresponds to the breathing period. We perform multipath decomposition for the preprocessed CSI to separate the dynamic path unit containing Doppler information. Specifically, 30 point IFFT is applied to each packet to obtain the CIR at each moment. The data from the first three path units that include Doppler information are extracted. Then, we use the time–frequency analysis tool STFT to obtain the Doppler shift spectrum. Calculating specific frequency values in the STFT requires a long integration time to ensure the desired frequency resolution. However, this integration time may exceed the time that the Doppler frequency exists in each period. The Doppler spectral energy changes also remain periodic because the Doppler shift periodically occurs. When the time window of STFT slides from the rest moment between breathing cycles to the breathing moment in the next cycle, the accumulated Doppler energy gradually increases; otherwise, the accumulated energy decreases from the breathing moment to the rest moment. Thus, a breathing cycle produces a peak in the spectral energy waveform.Specifically, STFT was applied to the dynamic path unit. The window length was set as 1 s, and the step size was 0.1 s. The Doppler frequency component falls within the zero-frequency bin, in which a DC component also exists. Considering that the spectral energy of the DC component basically does not change with time, we subtracted the spectral minimum energy to obtain a clearer energy waveform.We chose to test our method in a scenario where multipath effects are severe. We extracted the spectral energy waveform at the zero frequency of the spectrogram in Each peak of the respiratory signal corresponds to a respiratory cycle. We exploit the peak identification algorithm to estimate the respiration rate, which has higher time resolution than the FFT method. Specifically, fake peak removal algorithm  is used We implemented our system in two commercial computers  with Intel 5300 NIC. The operating system is Ubuntu 14.04 LTS. The two computers, which act as a sender and a receiver, are equipped with one antenna respectively. The device is shown in To evaluate the performance of our system in the actual scenario, the transmitter and receiver are placed in the indoor scenario, and the experimenter sits on the side of the link and breathed on the link. The application scenario of our system is daily home health monitoring. Thus, three typical indoor environments are selected to evaluate the performance of our system: a 5.8 m × 3.4 m living room, a 3.6 m × 2.7 m bedroom and a 2.9 m × 1.3 m balcony. The plan of the experimental scenarios is shown in The purpose of our system is to obtain the respiration rate of the human body. Thus, the estimation error between the respiration rate calculated by the algorithm in this study and the ground truth is chosen as the metric to measure the accuracy. Estimation error statistics are the common evaluation parameters in the field of respiration rate detection.We first evaluated the performance of our system under different scenarios. We selected two schemes in previous work as the benchmark to compare the performance: the method based on CSI amplitude  and the In the previous section, we analysed the influence of the breathing position on the detectability of CSI the amplitude and phase. In this section, we evaluate the effect of breathing location on the performance of our method and the traditional method.According to previous studies , more thIn the results of In this section, we analyse the major factors influencing respiration detection and evaluate their impact on our system. In the actual scenario, Wi-Fi devices may be placed in any place; hence, the influence of different distances between the devices on respiration rate detection is considered. We are interested in how the sampling rate affects the performance of the system. A higher transmission rate means more CSI values, which reflect the trend of respiratory signal in more detail, but it also increases the computational complexity of the system. In this study, we present a respiration rate-monitoring system that is applied in daily home health-monitoring scenarios by commercial Wi-Fi devices. This system uses Doppler information for breathing sensing.From existing respiration-sensing model based on the Fresnel zone, we investigate that breathing depth and location affect the detectability of the breathing signal. Therefore, the method based on CSI amplitude and phase cannot achieve full state detection. We proposed a respiration-sensing model based on Doppler frequency using the principle of periodic Doppler shift produced by periodic thoracic movement during breathing to solve this problem. Moreover, we theoretically analysed the possibility of extracting Doppler information from CSI.Weak Doppler generated by respiratory movement is easily submerged in static clutter caused by multipath effect in a real indoor environment. Therefore, we propose a method to extract Doppler information from CSI. First, the random phase shift is eliminated to obtain vector CSI. Then, IFFT is used for decompose multipath, and the signal in the dynamic path unit containing the Doppler frequency is extracted. Finally, the Doppler spectral energy is accumulated by STFT to calculate the accurate respiration rate.We designed and implemented a complete respiration rate-monitoring system, including environmental detection, amplitude and phase preprocessing, multipath decomposition, Doppler spectral energy extraction and respiration rate estimation steps. We implemented our system with commercial Wi-Fi devices in three classic indoor home scenarios and evaluated its performance through extensive experiments with detection results of high precision in all scenarios. The results demonstrate good anti-multipath ability, robustness and ability to solve problems with the detectability of respiratory signals compared with traditional methods in different scenarios and locations. We provide a non-invasive, cost-effective, reliable respiration rate-monitoring program scheme for home health monitoring.


Building information modeling (BIM) is evolving as a digital infrastructure model for innovation in the construction field. The innovation-enabling potential of BIM has been highly neglected in the literature. This study explores the innovative potential of BIM, specifically its value in enabling construction innovation (CI). Through reflective research and a literature review, the relationship between BIM and CI is redefined, BIM-CI's value spectrum and underlying mechanisms are mapped and their required resources and activities are illustrated. The results indicate that different BIM applications provide various proinnovation environments wherein CI may flourish. Extra attention should be paid to BIM-enabled systematic collaborative innovation and digital innovation ecosystems with BIM as the core infrastructure that integrates the physical space with cyberspace to accelerate radical innovation. This study extends BIM management research by considering digital innovation and providing a new perspective for CI management theory and practice. The results will provide academics with a solid point of departure for developing relevant research and serve as a reference for practitioners who intend to utilize BIM for efficient innovation in construction projects. Since it was first proposed in the 1970s, building information modeling (BIM) has become the newest trend in the construction field. An effort has been devoted to pursuing the potential of BIM. Some have focused on the functionality provided by BIM , while others have emphasized productivity and efficiency achievements enabled by BIM, such as existing construction management tools being integrated with open BIM to extend their capabilities in construction ecosystems [However, regardless of the benefits related to productivity improvement in particular activities, the innovative capability of BIM has been highly neglected in the literature , 3. InnoThe essential reason for this lack of academic research and untapped practical potential may be the absence of a holistic picture of the value of the innovative capability of BIM in construction projects. Thus, the purpose of this conceptual study is to map a value spectrum of the ability of BIM to enable innovation in construction projects. To achieve this goal, reflective research and a qualitative literature review were conducted to reidentify the relationship between BIM and CI. By coupling CI classifications and BIM application modes, a value spectrum depicting a holistic picture of the innovative capability of BIM is mapped and analyzed, and its underlying mechanisms and required resources and activities are also illustrated.Innovation-related research in the BIM management field mostly considers BIM as an innovative application or technology and examines its adoption, application, and implementation based on innovation diffusion theory or the technology acceptance model , 7. A BIThe construction industry is a diverse, project-based sector . ArchiteCurrently, the digitization of the construction industry is overwhelming. The inertia characteristics of the construction industry  render it “ripe for digitization” , 9. In tDigital innovation involves the use of digital technology during the innovation process. Nambisan et al.  conceptuIn the digital economic era, BIM is considered the main paradigm of the construction industry's technological transformation . The BIMLiu et al.  conducteRecently, there have been an increasing number of researchers claiming that divergent thinking and creative research methods are required for construction research . In partResearch design and method selection are extremely effective and have the greatest potential for innovation when they match the nature of the research question . In thisConceptual articles are devoted purely to thought-based conceptions and are devoid of data . If induActor network theory (ANT), path creation theory (PCT), trading zone theory (TZT), and digital innovation theory have been applied to assist the research process. Based on ANT, technology and other artifacts are regarded as actors shaping roles and relationships in networks . Under tIndeed, BIM is much more than a purely simple innovative technology; rather, it is an engine that promotes CI . With thThe current innovative capability of BIM has attracted the attention of researchers. For instance, Selçuk Çıdık et al.  proposedThe aforementioned research concerning the innovative capabilities of BIM primarily focuses on the scope of design practice. Increasingly, the enabling effect of BIM on CI has been expanded to the project life cycle or beyond. In a deductive case study, Holmström et al.  investigCI represents the engineering of problem-oriented innovation activities on the construction project level, which are usually completed collaboratively. The path for CI can be illustrated as a dynamic problem-solution matching process from both an individual organizational perspective  and a cross-organizational perspective . During these processes, the embeddedness of BIM serves to activate an individual organization's subjective initiative and accelerates collaboration between organizations to support CI.A CI network enabled by BIM can be considered a digital ecosystem, a concept that Sawhney et al.  defined As demonstrated in According to Galison , when coAlthough BIM is an overall approach, BIM application is a dynamically evolving elaboration, because there are various functional specifications that can be implemented at different phases of a project's life cycle that can bring distinctive benefits and costs . In realAs previously mentioned, there are many possibilities of the application of BIM in construction projects, or various so-called “BIM application modes,” which are highly dependent on the type of interactions and relationships between the participants and how BIM is used . Based oThe classification of CI made by Slaughter has been commonly acknowledged in this field , 20, whiAccording to The five types of innovation presented through the model can be arranged as per the scale of required change from the current state-of-the-art practice as an incremental, modular, architectural, system, and radical  see Tab.The innovation classification system proposed by Slaughter  is utilix-axis represents the evolution of BIM application modes, while the value of the innovative capability of BIM is demonstrated on the y-axis. As the scope and maturity level of a BIM application evolves, BIM application modes become more innovation-friendly environments. In other words, in different application modes, BIM provides different levels of innovation value. The wider the scope and the more mature the implementation of BIM, the higher the innovation value of its application in a construction project. Thus, the BIM application mode can be arranged according to the innovative value level of aspects such as visualization, coordination, analysis, collaboration, and infrastructure, as illustrated in Based on the combination of Slaughter's  CI classSince the 1990s, the visualization mode of BIM has been widely applied in the construction industry, mainly for marketing or aesthetic requirements. The application of BIM visualization demonstrates a great effect on the design and construction phase. First, 3D models of BIM designed on the basis of a 3D entity component provided by suppliers highly improve the designing efficiency. Second, the application of BIM makes communication between designers and owners regarding design intentions more convenient, thus enabling effective information to be correctly conveyed to the relevant team member. In a virtual environment based on BIM, many kinds of “what ifs” can be demonstrated and compared intuitively, which can accelerate the optimization of design solutions by smoothing the communication and cooperation targeting design objectives and solutions. On the construction stage, compared to two-dimensional (2D) painting, 3D models enable contractors to more easily and clearly understand the owner's actual design requirements and intentions, which consequently reduces changes and requests for information subsequently in the project's life cycle.The BIM visualization mode is essentially the transformation from 2D to 3D presentation technology. Therefore, the visualization mode of BIM application accelerates incremental innovation in construction projects at any time during the project. Incremental innovation enabled by BIM based on the visualization mode only involves small improvements and most of time is constrained by the scope of a single organization with no special resources or coordination required. The related supervision of the individual task level focuses on improving the notification manner and the supervisory competencies of the specific improved product, process, or service.The coordination mode of BIM creates mutual benefits and avoids gaps and overlaps to connect various project activities and efficiently realize project objectives . The cooThe coordination mode of BIM application invokes conceptual change of coordination issues in construction projects  while having little influence on other systems. In this mode, innovative solutions are likely to be created in order to realize conceptual goals. Thus, the value level is in modular innovation. Modular innovations enabled by BIM are usually created during the design and bid stages. Because changes to the links to other components or systems are rare, very little explicit or implicit coordination within the project team is required. The resources required for a conceptual change must be prepared to conduct modular innovation enabled by BIM, such as the training required to install and maintain a component embodying a modular innovation. Relative supervisory activities  are required on the design and bid levels to ensure modular innovations enabled by BIM meet project objectives.The analysis mode of BIM application realizes mutual benefits through sharing and cooperation. In the 3D virtual project environment based on BIM, the constructability and construction sequence can be clearly simulated. The entire arrangement of any specific task can be visualized in a 3D virtual environment based on BIM so that the schedule can be properly allocated. The BIM analysis mode provides the possibility to moderate and analyze construction costs and operations from the whole life cycle perspective. Moreover, BIM possesses many potential analytical capabilities for construction projects . Theoretically, one central BIM model could be formed with all of the information needed by all contractors and consultants—information that contractors previously had to create independently from an architect's 2D blueprints . The appIn this BIM application mode, there is more potential for architectural innovation to occur, which requires the support and encouragement of organizations at the system level with strong management and control capabilities. For major changes to system or component links, architectural innovation enabled by BIM may require a greater degree of supervision and coordination among the affected parties. To ensure high-level performance from the completed system, the supervisory activities required include notification, agreement, and constant review and revision. Coordination among project team members based on BIM includes both official contract changes  and unofficial coordination . Small conceptual changes may lead to a wide scope of BIM-enabled architectural innovations, from design-to-implementation, with special resources associated with complementary changes.The collaboration mode involves the full application of BIM in a construction project. Collaboration in a construction network based on BIM is a nonadversarial, team-based environment of mutual trust in which key participants are jointly involved in developing and augmenting the central BIM model throughout the life cycle of the project and where everyone understands and respects the input of others. The BIM model, as a central information resource, is a collaboration platform based on which the project's life-long activities are conducted  , 30. ThrCommitment to systematic innovation enabled by BIM is usually made during the conceptual design phase because it often involves new functions or increased levels of performance of an entity or a facility as a whole . During With the coming digital economic era, the Internet 3.0 has enabled various possibilities for many different industries. In contrast to previous technological changes in the AEC industry, Morgan  claimed Radical innovation enabled by BIM entails not only financial and project risk but also the potential for technical failure, meaning that the commitment to this kind of innovation must be completed as early as possible, most often before the project is initiated . Correspondingly, coordination of explicit and implicit cooperation and relative supervision requires the support of top management of all involved organizations. Consequently, the objective and scope of construction projects must be redefined, and the special resources required for radical innovation related to scientific or technological breakthroughs enabled by BIM might bring new challenges for top managers in terms of technical competencies in the specific scientific or technological area to monitor the implementation process and adjust the innovation activities accordingly .Industry practitioners and academia mostly perceive BIM as a tool and an application and occasionally as a methodology. Consequently, research related to BIM mostly focuses on practice and application. BIM theory-building has been vastly limited . TherefoAs is depicted in The collaborative BIM application mode is the current goal of BIM implementation in a construction project. Collaborative BIM applications can reap value from incremental to system innovation, as shown in BIM-CI is a dynamic problem-solution pairing process . The impGiven the critical position of systematic innovation enabled by BIM in the value spectrum of the ability of BIM to enable innovation in construction projects, Liu et al.  proposedTo reach the synergistic effect of a BIM-CI network, the subsystems must fulfill enormous emerging requirements and overcome many challenges . For exaThrough collaboration, CI enabled focuses on reaping the systematic innovation value of BIM within the confines of a construction project. The extensive digitization of the construction industry persuades us to consider the bigger picture on an interproject and an industrial level and beyond.Industry 4.0 has introduced digital technologies, sensor systems, intelligent machines, and smart materials to the construction industry . This trUnder such circumstances, the CI enabled by digital technologies urgently requires specific attention. The concept of digital innovation proposed in the information systems management field can be applied here. As explained by Khin and Ho , digitalDigital innovation research brings digital technologies into the foreground of innovation management . On the To do so, a digital technologies ecosystem with BIM in the center as a common data infrastructure for enabling radical CI should be established. This “digital innovation ecosystem” refers to a dynamic, evolving network comprising heterogeneous elements . The comThis study explores a neglected area—the innovation-enabling potential of BIM in construction projects. Specifically, it analyzes and evaluates the value level of the ability of BIM to enable CI. The relationship between BIM and CI was redefined as shown in Theoretically, this research extends the scope of the BIM management research considering digital innovation management issues and provides a new perspective for CI theory-building and management practice. The relationship between BIM and CI and the mapping of the value spectrum of the innovative capability of BIM provides researchers in this field with a novel perspective on the innovative capabilities of BIM. Relevant research should take the relationship structure and value spectrum map as departing points to design and develop further research. For practitioners, the detailed resources and activities required and the enabling mechanisms for relative levels of BIM-enabled CI can serve as good reference points to guide and inspire BIM-based project management practitioners to develop innovative solutions, solve corresponding engineering problems, improve efficiency, and build better projects.However, regardless of its contributions, this study also has some limitations. This conceptual study is an initial exploration of the value spectrum of the innovative capability of BIM. Because the goal of this study is to conceptually delineate the innovative capability of BIM, the main research methods applied in this study were the literature review, reflective thinking, and thought experiments, during which abductive and logical reasoning was required, and traditional qualitative research methods  were not utilized, which may cause some concern regarding the validity of the results. Thus, future qualitative and quantitative exploration of the value spectrum map of the innovative capability of BIM is required. In other words, these also create future opportunities to research BIM-enabled CI or digital innovation in the construction field. For instance, further research can apply more quantitative or mixed-methods research to validate the corresponding relationships between BIM and CI and develop relevant research questions, such as the following: Is there a corresponding relationship between the BIM application mode and the level of CI? Do the strategy and resources for enabling relative BIM-enabled innovation illustrated in this study suffice? Is the match between BIM and CI influenced by the delivery mode or owner involvement? How can the innovative capability of BIM in construction projects be activated?As overwhelming as the development of Industry 4.0 was, the new concept of Industry 5.0 has also been recently proposed . Industr


Introduction  An In-Practice Prevention (IPP) programme was developed by the Local Dental Network in the North Yorkshire and the Humber area in England in response to an oral health needs assessment. The underpinning logic model drew on a flexible commissioning approach and aimed to incentivise dental teams with NHS contracts to promote the delivery of prevention. This used care pathways that involved the whole dental team and was cost-neutral.Aim The programme was evaluated using realist methodology to identify 'what works, in which circumstances, how and for who?'.Design Realist evaluations are explanatory in nature and attempt to understand the factors that appear to influence the success (or not) of an intervention, rather than demonstrating causality.Methods and results  Following a review of the pertinent literature, semi-structured interviews and focus groups, five theory areas were considered to be critical to the delivery of IPP. In order of stated priority, these were: 1) clinical leadership; 2) 'skill mix'; 3) financial incentives; 4) institutional logic/practice culture; and 5) behaviour change.Conclusion The results appear to show that clinically-led programmes could offer value to dental commissioners within a flexible commissioning model, although this would need to be further tested using an experiment design. A cost-neutral preventive programme developed by a Local Dental Network in England was explored using realist methodology to understand how it 'worked' in practice.Multiple interviews were held iteratively with key stakeholders including dental commissioners, members of the Local Dental Network, general dental practitioners and dental care professionals.Clinically-led programmes appear to offer value to dental commissioners, improving both programme adoption and spread while driving innovation and 'peer-to-peer' improvement. Equally, the severity of tooth decay in five-year-old children in Hull was the third worst in England in terms of the decayed, missing, and filled permanent teeth index .Young children presenting with pain and sepsis resulting from dental caries are difficult patients to manage in high street NHS dental practices in the UK. The experience can also be distressing for young children and their families.Delivering better oral health)12was delivered over a defined number of appointments, with prescribed evidence-based interventions and oral health messages. Each intervention was associated with a payment (£36 for 3 prevention appointments) and this was offset against the target number of units of dental activity within the Annual Contract Value (ACV). In England and Wales, units of dental activity refer to a banded retrospective payment system which are produced by different types of clinical activity and are capped on an annual basis. This offset was approximately 3% on average, meaning that the prevention activity was resourced within the existing budget (ACV). In this way, the programme aimed to take a flexible approach to local commissioning and provide an incentivised and comprehensive programme to deliver consistent oral health advice and interventions that were targeted at children with the highest levels of disease.In response to this, the Local Dental Network (LDN) developed the In-Practice Prevention programme (IPP), which required NHS practices with NHS contracts to identify children (aged between 3-16 years of age) with experience of dental caries (at least one lesion) or those children that required a general anaesthetic. These children were then internally referred to Dental Care Professional (DCP)-led prevention clinics at the practice, where evidence-based prevention  of an intervention, rather than demonstrating causality. They are cyclical in nature , before refining the programme theory (stage 3) and reviewing the findings with key stakeholders in a final focus group (stage 4). This structured approach produces a rich account and is ratified by key stakeholders at the end of the process. The use of realist methodology is current and popular in health services research as it recognises the need to show how and why interventions work or not, as opposed to merely evaluating whether they work or not.15 The aim of this study was to undertake a realist evaluation to understand the experience of stakeholders participating in the IPP programme. This involved following the defined sequence outlined in Realist methodology is a form of theory-driven evaluation and seeks to address the following question: 'what works, for who, how and in what circumstances?'n nature  and stara priori (stage 1) before iteratively developing these theories using a systematic pattern of enquiry.16 This is the standard approach for realist evaluations and is the first stage of the process. It seeks to develop possible explanations for how the IPP programme may work, based on a review of the scientific and grey literature. IPTs are statements that contain at least one context, mechanism and an outcome. In this sense, context describes the socioeconomic, political or organisational environment or background, while an outcome describes what has changed  as a result of the action of a given mechanism. An example of an IPT statement for illustrative purposes is the following: in NHS dentistry (context), setting an ACV (mechanism) contains NHS costs for dentistry at a societal level (outcome). This statement describes one element of the current contract and as can be seen, is relatively simplistic and is a description at a high level (but it is the first step). The role of realist evaluations is to develop a series of these types of statements (stage 1) and then 'unpack' them (stage 2 and 3), so that the researcher gets a detailed understanding of how the intervention in question might work (or not) at a practical level. These are then fed back to a stakeholder group for further refinement to ensure that it captures their experience of the intervention (stage 4).The study was approved on 10 April 2017 . As a 'theory-driven' approach, candidate IPTs were developed Scoping the scientific and grey literatureConcept miningConceptualising the IPP programme using soft systemsIdentification of theory areasLiterature searchSelection and appraisal of documentsData extraction, analysis and synthesis.For stage 1 of the IPP programme evaluation, the following process was followed to ensure that the research team could generate as many potential IPTs as possible:12,17,18,19,20,21,22,23Concept mining is a process of exploring potentially important themes that could help explain how an intervention might work (or not). As its name suggests, it involves searching the literature for examples of how evidence-based preventive programmes might 'work' in practice. For the IPP programme, we drew on a number of potentially relevant sources.24 This has been used in a number of realist evaluations and attempts to understand how the intervention works by focusing on these six key elements:25,26,27Customers: beneficiaries of IPPActors: detail of roles and function within the programmeTransformations: changes required to facilitate IPPWorld views: underlying contextual cultureOwnership: actors required to drive IPP forwardEnvironments: factors that influence implementation.This was then followed by two workshops with key stakeholders, where the findings from the concept mining were discussed and detailed questions were asked about the nature of the IPP programme. The structure of the workshops were guided by 'soft systems thinking' using the CATWOE mnemonic .25 Systematic searches were conducted in three electronic databases: Ovid Medline, CINAHL and PsycInfo, using keywords identified through the search development process and 'keywords' adapted for each information source. The primary search was limited to material from 1990-2019. Abstracts were screened and the evidence summarised into data tables. This was then analysed to develop the IPTs, using a process of triangulation to look for emergent patterns in the data.26,27 This ensured that all the possible underlying contexts, mechanisms and outcomes in the IPP programme were captured.As the search for potential IPTs developed, a detailed search strategy was developed, taking account of the concept mining and 'soft systems thinking' workshops. Reflecting the realist approach, the search strategy was kept as broad as possible and combined primary and purposive searches in order to capture the most relevant evidence to build, support and/or refute the IPTs that were being developed.In stage 2 of the realist process, key stakeholders were invited to participate in semi-structured interviews using an interview schedule based on stage 1. Each participant was asked to reflect on whether or not and in what ways the IPTs explained their experience of the IPP programme. Open-ended questions were also included to identify any new IPTs that the research team had not identified. As a result of the COVID-19 pandemic, the semi-structured interviews were undertaken using Microsoft Teams, audio recorded and transcribed verbatim. In practice, the research team would present each statement to the participant and ask whether it made sense and it what ways their experience differed.28 For the IPP programme, this meant developing an understanding from stage 2 of how the different context, mechanisms and outcomes worked at a practical level, that is, the lived experience of the different stakeholders involved. These refined statements are known as context, mechanism and outcome configurations (CMOCs).17,18In stage 3 of the realist process, the research team used the data from stage 2 to refine the IPTs further, ensuring that the different context, mechanisms and outcomes were grounded in the experience of the different stakeholders. As can be seen from the example above, when IPTs are developed, they can appear relatively abstract and too simplistic. The idea that setting an ACV (mechanism) in NHS dentistry (context) contains NHS costs for dentistry at a societal level (outcome) doesn't really describe how this works in practice, nor does it capture the nuances of the issues involved. However, it is a good starting point to get at the elements that do matter to key stakeholders, as it helps to frame the discussions. As such, realist approaches are iterative and continue to test and refine the IPTs to build a rich picture of the experiences of those involved.29,30 This is a standard approach in realist evaluations, where the researcher teaches back their findings before asking for any reactions and comments from the group. The programme theories were then finalised.In stage 4 of the realist process, the refined CMOCs were taken to a further group of stakeholders to ratify the final programme theories of IPP. Ahead of the meeting, all the participants were provided with the CMOCs in a diagrammatical form (from stage 3), with a brief explanation of the theory areas. At the beginning of the workshop, participants were first asked to prioritise the five theory areas in order of importance. The research team then utilised 'teacher-learner cycles' before asking the stakeholders to comment.Over a two-and-a-half-year period, data from the dental commissioners revealed that the IPP programme had delivered over 17,500 evidence-based DCP-led interventions. During this time, data from the NHS Business Services Authority revealed that attendance patterns and the use of fluoride appeared to increase for child patients. Equally, there was no evidence of any negative influence of the programme on recall rates with adult patients and patient charge revenue, two important concerns for dental commissioners.Institution logic (n = 8)Clinical leadership (n = 9)Financial incentives in the NHS dental contract (n = 10)Behaviour change (n = 9)'Skill mix' (n = 11).31The IPTs that were subsequently developed from this synthesis are detailed in In the final workshop (n = 10), the ranking of the theory areas was as follows: CMOC 1) clinical leadership; CMOC 2) 'skill mix'; CMOC 3) financial incentives; CMOC 4) institutional logic/practice culture; and CMO 5) behaviour change 'The outcome of the peer-to-peer influence and the feedback that resulted, that loop created a responsive programme that allowed the programme to evolve so that strengths were incorporated and any problems could be addressed' (commissioner).'A further two mechanisms and an additional outcome were added to CMOC 1  before finalising . The rolI think one [most important element] was the DCPs supporting each other and leading each other' (PHE representative)'but the DCPs did roll it out in the practices, so it was all led by them really [...] they were vital to rolling it out' (practice owner)'I've seen it with my own eyes, where the patients had a better connection with the DCPs' (practice owner).''Skill mix' (CMOC 2) was considered to be the second most important theory area . A furthProviding you're not disadvantaged financially, if you're disadvantaged, then it's a no go. But you don't have to have a huge financial gain, it can be just the status quo as far as finances go, providing if then there are other benefits to doing it and it's more rewarding work' (practice owner)'It can't be financially disadvantageous, but there are other things that are important in terms of improving the quality of your working day really and the working balance by using a different skill mix' (PHE representative).'Financial incentives (CMOC 3) were also key . A furthCMOC 4  and CMOC 5 (behaviour change) were also considered to be important  and 6, b17,32 Findings in this study support this and demonstrate the importance of 'clinically owned and clinically led' initiatives (CMOC 1) in driving targeted clinical activity  and institutional logics of the NHS dental practice (CMOC 4).34 IPP promoted 'skill mix' and for many of those interviewed, it appeared to empower the DCPs involved, particularly the dental nurses. This 'freed up' latent potential within the dental teams to champion and promote a preventive culture to deliver the increase in care . This appears to support the importance of 'flexible commissioning', where targets are aligned to local public health objectives. As highlighted above, all of the interviewed practice owners believed this was key to the adoption of IPP, which was cost neutral for commissioners. Equally, this enabled practice owners to change their working practices and some reported that they were able to change their internal payment structures to facilitate IPP without impacting on care for adult patients .et al., argue that institutional logics within NHS dentistry not only includes dental practices as businesses, but also professional ethics and contextual factors, based on where the practice is embedded.23 As such, the drive to maintain (and maximise) the viability of an NHS practice can also be tempered by a practice owner's view about their sense of duty to their patients and their ideas about how best to deliver care for their patients and community. This was supported in the findings of the IPP evaluation.Goodwin Given the findings of the realist evaluation, it would appear that clinical leadership, 'skill mix' and financial incentives were seen as the most important elements of the IPP programme. Aligning public health priorities with potential financial incentives within the existing NHS contract was key. Equally, the utilisation of the whole of the dental team was critical for the success of the IPP programme and created local champions that drove the institutional logic within the practice and behaviour change.