Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment. Bogies are located at both ends of high-speed train carriages and play an important role in the safe and stable operation of the trains. With the increase in train speed, the working environment of the bogies under heavy-load and long-term alternating stress conditions is becoming more and more severe. Axle bearings, as an important rotating part of the bogie, are subjected to a variety of dynamic loads and harsh working conditions during operation . As a reThe collision of the damaged rotating roller will generate a series of periodic impacts and corresponding resonances when the surface of the bearing component fails. The fault characteristic frequencies of different types of bearing failures are varied. However, when overwhelmed by heavy noise, the fault impulse feature generated by an incipient fault is too weak to detect directly. This has led to a considerable amount of research on the vibration-based diagnosis of bearings in the last decades. Many signal processing methods, such as the spectral kurtosis (SK) algorithm ,3, morphTraditional TFA methods, such as short-time Fourier transform (STFT), Wigner−Ville distribution (WVD), and wavelet transform (WT), have been widely employed for fault signal analyses. Despite the respective good performances of these techniques, shortcomings also remain that limit their practical application. Because of the Heisenberg uncertainty principle, STFT and WT technologies are unable to obtain a high-accuracy time−frequency resolution . WVD is Time-reassigned multisynchrosqueezing transform (TMSST) is the latest TFA method proposed by Yu et al. , which c(1)The TMSST method is less robust to noise interference. The key components of the rotating machinery often work under very harsh conditions, and the measured vibration signals contain a large number of background noise interference components. The fault feature information is immersed in heavy background noise, which leads to fault impulses that are difficult to recognize. TMSST is only effective for fault impulse components of the signals, but cannot eliminate the interference of noise. (2)The self-adaptability of the TMSST is relatively poor. In practical applications, two algorithm parameters of the TMSST need to be set in advance, and they have a significant influence on the results of the TFR. So far, the choice of the TMSST algorithm parameters still depends on human experience, which leads to a high uncertainty for the TFR results. To tackle the above problems and expand the practical application of the TMSST, a framework combining singular value decomposition (SVD)-based denoising and self-adaptive TMSST is presented in this paper. The proposed method can eliminate noise interference as much as possible, and can effectively extract weak fault impulse features for axle bearing fault diagnosis. The main contributions of the work proposed in this paper are summarized as follows.(1)An SVD-based denoising method is introduced to eliminate noise interference. The SVD technique is reviewed as a competitive noise reduction method, which has been widely used in signal denoising ,22. Henc(2)Adaptive optimization TMSST is developed to acquire the optimal algorithm parameters and extract the TF fault feature information. A new evaluation metric, time−frequency spectrum permutation entropy (TFS-PEn), is proposed to quantitatively evaluate the TFR performance of the TMSST. To further improve the adaptability of TMSST, an optimized water cycle algorithm (WCA) is introduced to determine the algorithm parameters adaptively.The rest of the paper is structured as follows. SVD-based denoising based on the maximum SV mean is described in The SVD-based denoising algorithm has two remarkable advantages. (1) SVD is a non-parametric technique and is easy to implement. For a given matrix, it can be decomposed into two orthogonal matrices and some corresponding singular values without any parameters. Some denoising algorithms, such as the Kalman filter algorithm and wavelet transform filter algorithm, need to set relevant parameters in advance. (2) The essence of SVD is to retain the useful information in the signal subspace and to remove the interference components in the noise subspace. So, the SVD-based denoising algorithm does not introduce additional components into the denoising process. It does not pollute the raw signal, which is different from other denoising algorithms, such as morphology filter and stochastic resonance denoising. Based on the above analysis, the SVD-based denoising algorithm is suitable for signal denoising preprocessing.X is formed asm < N, n = N − m + 1. m is the rows of the matrix and n is the columns of the matrix. In order to enhance the performance of the SVD, the Hankel matrix should be decomposed as fully as possible [Assuming that possible . When coFor matrix The performance of SVD-based denoising depends on the choice of the SV order. i-th SV mean. k SVs are reserved for matrix reconstruction to reconstruct the denoising signal. Through the above analysis, knowing how to make a reasonable choice for the SV order is crucial for SVD-based denoising. The Hankel matrix, described by Equation (1), shows that two adjacent row vectors lag only one data point. Hence, adjacent row vectors are highly similar sequences with a strong resistance. The Hankel matrix consists of useful signal components and has a significant feature that the SVs are larger in the first few values and the rest are much smaller. However, the difference in SVs for a noise matrix is similar because the two adjacent row vectors in the noise matrix have a bad correlation. Based on these analyses, the maximum SV mean method is proposed in order to determine the reasonable SV order in this paper. The SV mean is illustrated with the following equationA mono-component signal can be expressed as follows:The first-order Taylor expansion of Substituting Equation (8) into Equation (7) results in the followingThe blurry TF energy is squeezed into the GD trajectory through the operation of a 1D integration along the time direction.Combining Equations (10), (12), and (13), we obtain the following:It can be seen from Equation (13) that the blurry TF energy of The second-order Taylor expansion of The Gaussian window function used in STFT isSubstituting Equations (14) and (15) into Equation (7), we obtain the following:The 2D GD estimate of Equation (16) can be obtained as followsNow, substituting N-th areThe fixed-point iterative algorithm of N reaches infinity, the following result can be obtained:When the iteration number So, Equation (13) can be written asAfter a sufficient number of iterations, we obtainThe TMSST method can squeeze the TF energy of a strong frequency-varying signal into the GD trajectory through multiple fixed-point iterations. In the TMSST result, there are a series of significant TF amplitude points that denote the fault impulse interval. The maximum value of the TF envelope spectrum (TFES) for each amplitude point can be used to represent the fault impulse feature, which is calculated as followsThe antinoise capability of TMSST is poor because the noise components cannot be squeezed into the GD trajectory through the TMSST. In the course of TMSST implementation, there are two algorithm parameters (moving window width According to Equation (22), the TMSST result is integrated along the frequency axis to acquire the time−frequency spectrum (TFS) as followsTFS represents the distribution of the TF energy along the time axis in the TMSST result. When a highly concentrated TFR of a fault impulse appears in the TMSST result, the TFS shows an obvious regularity feature. Alternatively, if the TFR of the TMSST result is blurry, the distribution of the TFS has a large randomness. To further estimate the TFES result, permutation entropy (PEn) is introduced to calculate the TFS-PEn. N, an embedding procedure is used to form a vector m and the lag is m is fixed, there are PEn is a parameter of average entropy that was first proposed by Bandt et al. . It can The corresponding normalized entropy is given as follows:m = 6 and In the calculation of PEn, the embedded dimension and the lag are set to rding to .When the TFS series shows regular fault impulse characteristics, the TFS-PEn value is smaller and, vice versa, TFS-PEn value is larger. TMSST results with different (1)Construct the Hankel matrix of the analyzed signal according to Equation (1) and perform the SVD on the Hankel matrix.(2)Acquire the SVs of the Hankel matrix and determine the optimal SV order according to the maximum SV mean method, and then reconstruct the denoising signal according to the optimal SV order.(3)Set the WCA’s initial parameters. The number of rivers and sea is (4)The minimum TFS-PEn evaluation criterion is used in the WCA to adaptively select the optimal algorithm parameters of the TMSST. Perform the WCA optimization until the maximum iteration number is satisfied, and obtain the optimal TMSST parameters.(5)Carry out the TMSST and calculate the maximum TFES values to extract the fault impulse features of the faulty bearing.To further improve the efficiency and accuracy of the algorithm parameter identification, a water cycle algorithm (WCA) is introduced into the TMSST to adaptively acquire the optimal parameters in this paper. As a novel metaheuristic optimization algorithm, WCA is used for solving the constrained engineering optimization problems, which has many advantages, such as fast convergence, excellent capability for optimal weighting searching, high accuracy, and good robustness . For detT = 14 ms, and i-th fault impact and r(t) is the Gaussian white noise with a signal to noise ratio (SNR = −7dB), which is white noise with a mean of 0 and standard deviation. The pure signal and mixed signal with noise are shown in A simulated bearing fault signal consisting of fault impulses and noise interferences is given as follows (27)h(t)First, the SVD-based denoising method is used to process the simulated signal, and the results are shown in The fault signals of the bearings are acquired from a high-speed train axle bearing test rig. The structure of the test rig is shown in The main parts of the test rig are illustrated in As a comparative analysis, the traditional TMSST based on the optimal algorithm parameters is directly used for the analysis of the bearing outer race fault signal. The TMSST result is displayed in The time-domain waveform of the axle bearing inner fault signal is shown in The result of the traditional TMSST using the same algorithm parameters is given in In order to demonstrate the superiority and effectiveness of the proposed method, four existing common TFA methods, STFT, MSST, WT, and Hilbert-Huang transform (HHT), are utilized to analyze the axle bearing fault signals. To fully verify the superiority of the proposed method, the fault signals after the SVD-based denoising are also processed by these four comparison methods. In the actual working condition, the fault vibration signal of high-speed train axle bearing contains a lot of noise components. Although the TMSST method is able to produce clearer energy concentrated time−frequency (TF) trajectories to extract fault impulse features in the time−frequency representation (TFR), the interference of the background noise seriously restricts the TFR performance of the TMSST. To address this issue, a hybrid SVD-based denoising and self-adaptive TMSST is proposed to extract the fault feature of the axle bearing. On the one hand, a maximum SV mean method is put forward to determine the SV order to reconstruct the signal. The SVD-based denoising pre-processing technique can remove a large amount of background noise and reserve the useful fault impulse components. On the other hand, a minimum TFS-PEn evaluation criterion is presented to quantitatively evaluate the performance of TFR for the TMSST. Then, a water cycle algorithm is introduced into the TMSST to adaptively acquire the optimal algorithm parameters and to improve the reliability and robustness of the TMSST. Both the simulated fault signal analysis and experimental data prove the feasibility of the proposed method. In general, the proposed method fixes the shortcomings of the traditional TMSST and expands its application. Moreover, it has a certain value for axle bearing fault detection and provides a new way of thinking about the TFR of fault features, which is very likely to contribute to future related topics regarding other time−frequency analysis (TFA) techniques. The signal SVD and the optimization algorithm iteration process have a high computational cost. The main limitation of the proposed method is that it requires a large number of calculations and it is less efficient. Hence, improving the operating efficiency of the proposed approach still needs further research.


Consumers are increasingly concerned about the sustainable production of food, leading producers and scientists to evaluate farming practices that preserve environmental resources, provide adequate production, and are economically viable. However, there are challenges to synthesize these results and apply them on-farm in a holistic nature. Simulation modeling of farm systems, such as the dairy system, can allow producers, industry members, and policy makers to prioritize interventions that improve sustainable outcomes. We introduce the Animal Module of the Ruminant Farm Systems (RuFaS) model—a whole farm dairy system model—and describe its use to assess the environmental impact of improved feed efficiency in dairy cows. By decreasing the amount of feed intake required to produce the same amount of milk, the RuFaS model provides estimates of the reduction in feed use, enteric methane, and manure production.Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production—which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively. The FAO  predictsManaging the need for sustainable food production requires a careful allocation of limited farm resources. One method to guide policy, support farm decisions, and evaluate novel technologies is the use of simulation models. The effect of implementing new technologies on-farm or changing production management can be evaluated on multiple outcomes, generally providing a more robust evaluation than traditional research experiments. Existing whole-farm models for dairy production include the Integrated Farm Systems Model , DairyMo2 eq/kg milk) achieved in past decades by comparing LCA estimates of US dairy GHG footprints in 1944, 2007, and 2017.Within the RuFaS framework, the environmental impacts of dairy production can be determined from each of the four main parts of a dairy farm: animals, manure, field, and feed storage. Previous life-cycle assessments (LCA) of dairy production have estimated the total environmental impacts of dairy production and the relative contributions of each part of the dairy farm and supply chain e.g., ,14). Tho. Tho14])Much of the reduction in GHG emissions per kg of milk is attributable to improvements in feed efficiency. In particular, Capper and Bauman  highlighAlthough the dilution of maintenance concept describes improvements in individual animal efficiency with increases in milk output, many other factors will influence feed efficiency and the subsequent environmental footprint over an animal’s lifetime and at a farm-system scale. In addition to some of the reproduction and life-cycle management impacts mentioned by , feed efResidual feed intake  is a metric used to evaluate differences in feed efficiency between cows with the goal of identifying animals with improved feed efficiency due to reduced feed consumption for an equivalent production. There is a long history of interest in using RFI as the phenotypic expression of feed efficiency to inform breeding programs ,18,19, bWhole-farm models like RuFaS can be used to estimate system outcomes to management or biological changes, but most existing models cannot easily represent the impact of a change in RFI. The aggregate or static representation of animal traits and simplifications in the connection between diet and feed consumption, animal life-cycle, and methane and manure production mean that the impact of a reduced individual animal RFI on whole-farm feed consumption and downstream methane and manure production are difficult to include in most farm systems models. We have intentionally designed the Animal Module of RuFaS to enable the investigation into a wide variety of current and future precision management practices that are expected to influence whole-farm efficiency and environmental outcomes . As a reThe objective of this work is to document the advances in dairy farm simulation modeling made by the Animal Module as part of the RuFaS modeling ecosystem and to demonstrate its utility through a case study of the environmental impacts of improving feed efficiency. Thus, our first objective is to provide detailed documentation of the algorithms that define management, production, and nutrient flows in a dairy herd. For our second objective, we describe and implement methods to add the representation of variable RFI and use this altered model to compare the impact of RFI on feed intake, enteric methane production, and manure production in a simulated herd with 1000 lactating Holstein cows. The future application of the RuFaS model will support scientific investigation and on-farm decision support related to precision-feeding, breeding, and other dairy herd management practices.The Ruminant Farm System (RuFaS) model consists of four biophysical modules: animal, manure handling, crop + soil, and feed storage . The simThe model inputs span decisions at the farm, herd, and animal levels . The firThe animal module simulates the individual animal from birth to culling, accounting for farm management decisions and individual animal responses to those decisions. Animals are simulated individually, and outputs such as animal growth, animal production, and manure production are estimated daily for each animal. The main routines of the animal module are the animal life cycle, animal nutrition, and manure excretion . The aniThe animal life cycle simulates the animal from birth to culling, encompassing weaning, first reproductive cycle, first lactation, and subsequent calving and lactation cycles. There are five animal classes: calf, heifer I, heifer II, heifer III, and cow. Calves transition to heifers based on weaning day. The heifer stage is divided into three categories: heifer I (from weaning to breeding period), heifer II (first estrus to transition period), and heifer III . Cows include lactating and dry cows. Animals are culled from the herd or added to the herd depending on the management inputs to maintain the herd size specified in the user input.The life cycle uses stochastic, Monte Carlo methods incorporating the probability of events and parameter averages, distributions, and standard deviations to simulate random variables . The proCalves are the first animal class initialized, and the success of a calf’s birth, its sex, and its longevity on the farm are determined by stochastic processes as described above. A target average daily gain (ADG) is used to estimate growth until the weaning day is reached. The daily update for the calf includes a check to see if it has reached its weaning day and a method to record bodyweight changes.The three classes of heifer are used to organize reproduction and life events during the heifer period. Heifer I includes growth until the breeding start day is reached. If timed artificial insemination (TAI) is used, no estrus simulation occurs. The target ADG is set to achieve 55% of mature shrunk bodyweight (BW) at first pregnancy . The heiConceptus growth from 51 to the end of gestation is calculated asA heifer moves to the Heifer III class when they are within the user-specified pre-fresh period. Heifers are culled from the herd if they exceed a user-specified heifer reproduction cull time, i.e., if the heifer is not pregnant by the specified age. The target ADG is adjusted to reach 82% of mature shrunk body weight (BW) at the first parturition.Cows are managed from first calving to culling from the herd. When the days in pregnancy equal the gestation length, the cow calves reset the days in pregnancy to zero and initiate milk production. The milking update estimates milk production from a Wood lactation curve ,25. The The user input herd number is used to maintain the herd size. If the number of heifers is greater than the herd needs, Heifer IIIs will be sold. If the number of heifers is less than the herd needs to maintain the herd size, Heifer IIIs are purchased from the replacement market. Animals can be culled at any life stage depending on user inputs. Male calves can be sold, heifers culled for reproduction failures, and cows for reproduction or milk production issues. Health reasons for culling include lameness, injury, mastitis, disease, udder problems, and unknown issues.The animal nutrition routine automatically formulates a ration to meet animal nutrient requirements using the feeds specified by user inputs. Feed nutrient composition is based on feed tables in the 2001 NRC with new 2021 starch values added for starch concentrations . The maxFor calves, the feed intake of milk or milk replacer is assumed to be 10% of the birth weight, a common industry practice  . The intThe ration formulation for heifers and cows includes four key processes: requirement calculation, compiling types and amounts of available feeds, nonlinear program optimization, and ration reporting. Rations for heifers and cows are formulated to meet the energy, protein, Ca, and P requirements provided in the NRC . Individ1 is the price of the ration ($), cj is the price of feed j ($/kg), xj is the amount of feed j (kg), and aij is the nutrient concentration of the feed . Sequential quadratic programming is a non-linear optimization method capable of handling both discrete and continuous constraints [Automated ration formulation currently uses an algorithm for least-cost non-linear optimization similar to that described by Rotz ; howeverstraints . In addistraints . If the The ration formulated for each pen is applied to each animal for the user-defined period between ration formulations, and the total amount of feed consumed by each pen is recorded daily. The manure subroutine calculates the animal manure excretion and sums the manure excretion by both animal class and pen. Total manure (kg as-excreted), total solids (kg DM basis), and methane emissions (g/d) are calculated for each animal and for all animal classes based on animal and dietary characteristics.For calves, total manure and manure solids are calculated according to animal bodyweight . The aveFor all classes of heifers, total manure, manure solids, and volatile solids are calculated from ASABE  equationManure production and methane emissions of cows are divided into lactating and dry cows. For lactating cows, fecal water, total solids, urine excretion, manure excretion, volatile solids (separated into degradable and nondegradable volatile solids) are calculated according to  using drThe user has the option to create a new initialization herd for each simulation. Starting a simulation with an initialization herd substantially reduces the time for the model to stabilize. The initialization herd creates a database of animals that are used to populate the herd and replacement heifer market on day 1 of the simulation. The replacement heifer market is a large number of Heifer III class animal instances that can be selected to meet the herd number targets after cow culling. When the user chooses to create a herd initialization database, a minimum of 1000 calves are created and simulated for a minimum of 5000 days. The initialized herd requires the same user inputs as the simulated herd to create a large herd population with the same characteristics  as the animals in the desired simulation. An SQLite database file is created to store the simulated animals at the end of the initialization of the program. The simulated animals represent the five animal classes plus a replacement herd of instances of the Heifer III class . Random Each animal is updated daily through the execution of a daily update function that is specific to each animal class. The function updates each animal according to its life cycle functions, calculates the daily manure excretion, and, if it is the end of the ration interval, calculates the animal nutrient requirements, redefines lactating cows’ pen assignments, and creates a new ration. Animals are culled from pens, and new animals are selected from the replacement during the daily update.i) that represents the proportional change in DMI of animal i. Because the biological mechanisms that drive feed-efficiency are still not well understood, the objective of the parameter ρ is to modify the cow’s DMI without changing the ration formulation or predicted milk production. During the initialization of each animal, a random draw from the user-defined distribution for this parameter is assigned to that animal’s record. After a ration has been formulated for each pen, when the ration is assigned to each animal’s record, the expected intake for that animal is modified by multiplying the previous model-predicted DMI by ρi. The individual animal DMI that has now been adjusted to reflect that animal’s feed efficiency is then used as the input to predict the animal’s enteric methane and manure production. The result is a representation of the RFI phenotype that is commonly measured and used to study feed efficiency.To demonstrate the utility of the RuFaS Animal Module, we developed a method to alter the animal-level feed efficiency and used the model to estimate the expected outcomes of a herd that has been bred for improved feed efficiency. We created a feed-efficiency random-variable  are shown in We successfully implemented a feed efficiency parameter that influences the individual animal intake and reflects the variation in RFI seen in commercial and research herds. The simulated feed efficiency outcomes are given in The expected impact of feed efficiency on herd production, intake, enteric methane, and manure production is shown in The average and SD of the inclusion rates for each feed are given in We display the daily simulated outputs for enteric methane, manure N, milk production efficiency, and milk:manure ratio in box-plots in 2-eq of 30 for CH4 and a CO2-eq of 298 for N2O for the purpose of illustrating the comparative impacts [2-eq than the expected benefit from reduced enteric methane (Although the RuFaS Animal Module is not yet integrated with the process-based manure management simulation, we provide an empirical estimate of the carbon footprint of the predicted enteric methane, manure volatile solids, and manure nitrogen using the methods described by IPCC . For man impacts . Based o methane .The Animal Module of RuFaS offers a comprehensive representation of individual dairy cow life events, feeding, milk production, and manure production, as well as the application of a wide variety of herd management options to simulate dynamic, holistic outcomes from the animal part of a dairy farm system. These holistic outcomes and their expected response to different management scenarios are essential information for scientists and industry members to guide sustainable development in the dairy sector. Although the static, deterministic estimates of environmental outcomes offered by LCAs ,14 are u2-eq from enteric methane and manure production, we summed the CO2-eq from 2-eq by 4.6% and the Very High efficiency scenario reduced the total CO2-eq by 9.3%. This preliminary assessment of the impact of the improved feed efficiency on enteric and manure GHG emissions can be used to support and set targets for breeding programs. In future work, we plan to add the representation of genetic inheritance between dams and calves to simulate not just the target of breeding programs but the speed with which different breeding and management programs will be expected to get there. The cumulative outcomes and environmental benefits of breeding for improved efficiency over the years can then be estimated.The feed efficiency case study highlights the utility of simulating individual animals instead of groups or classes of animals and the adaptability of the model to target specific study questions, which is another key feature of the RuFaS model. A single stochastic parameter to represent the observed animal variation in RFI, and thus the feed efficiency, was easily added to the model code base and facilitated the comparison of multiple outcomes of interest with a single input value. This case study was selected because improving feed efficiency through the use of metrics like RFI has gained increased attention in recent years in the scientific literature ,40,42 anIn addition to adding genetic inheritance, future work to fully integrate the Animal Module with modules that represent manure processing and storage, field management, and crop harvest and storage will complete the dairy farm nutrient cycle to allow for the dynamic feedback of animal management on the whole farm and enable process-based estimates of environmental outcomes both down and upstream from the dairy barn. Upon completing version 1 of the whole-farm model, we will publish the code and underlying documentation in an open-source repository, so that scientists and industry professionals may engage with the model directly. The fully integrated model will have many applications in science to probe questions about the outcomes of single and combinations of management changes. The RuFaS model will also have applications in industry as a tool to estimate whole-farm environmental impacts, and expected changes to the adoption of novel practices, so that producers can meet market and societal demands for improved sustainability.We have presented a description of the Animal Module of the Ruminant Farm Systems modeling platform, described its structure and advantages over other existing models, and used the example of feed efficiency to illustrate some of its functionalities and applications. The results of the case study demonstrate the milk, manure, methane, and herd demographic outputs available from the RuFaS Animal Module and provide preliminary estimates of the multiple environmental benefits of improving feed efficiency by reducing RFI. The use of the Animal Module within the larger RuFaS modeling platform will enable an even more comprehensive assessment of how improvements in management and biological performance will affect the environmental impacts of dairy production.


Home-based rehabilitation after an acute episode or following an exacerbation of a chronic disease is often problematic with a clear lack of continuity of care between hospital and home care. Secondary prevention is an essential element of long-term rehabilitation where strategies oriented toward risk reduction, treatment adherence, and optimization of quality of life need to be applied. Frail and sometimes isolated, the patient fails to adhere to the proposed post-discharge clinical pathway due to lack of appropriate clinical, emotional, and informational support. Providing a suitable rehabilitation after an acute episode or a chronic disease is a major issue, as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from hospital to home. Virtual coaches could help these patients to engage in a personalized rehabilitation program that complies with age-related conditions. These coaches could be a key technology for empowering patients toward increasing their adherence to the care plan and to improve their secondary prevention measures. In this paper, we are presenting a novel virtual coaching system that will address these challenges by combining recent technological advances with clinical pathways, based on joint research and validation activities from researchers from the medical and information and communication technology (ICT) domains. Non-communicable diseases (NCDs) represent the leading public health challenges globally in the 21st century, resulting in ill health, economic burden, life loss, diminished quality of life (QoL), and poor social development equally in both high-resourced and low-resourced countries. NCDs include mostly strokes, heart diseases, Parkinson's disease (PD), cancers, diabetes, chronic kidney disease, respiratory diseases, and Alzheimer's disease. We have got to know to a point which one out of six people in the EU has a disability  could support these patients to continue with a personalized rehabilitation at home, adjusted to the age-related conditions. This way, also, they partly compensate the absence of a human caregiver. By natural language processing interaction, facial recognition, and behavioral monitoring, VCs could motivate patients to engage and perform their recommended physical activity routine, to stick to the medication plan, to avoid unhealthy habits, and to keep their social life active. In sum, such VCs could be key measures for the empowerment of patients toward implementing long-life healthy actions.Figure 4, the approach of a VC is to accompany the professional work with practical tool that engages patients to work on their risk factors and motivates them into implementing the healthy measures.Home rehabilitation intends to re-build the patients independence and to reduce their psychological stress, so that they can take part again in different daily activities or even to re-enter the economic circuit and to become a productive member of the society . In partHome VC-assisted rehabilitation could be indicated when the patient is able to engage in several suggested activities and when learning and skill development is required for long-period therapy continuation. Meaningful planning and responsiveness are the key determinants of home rehabilitation for improving the QoL of the patientPhysical activity—Physical activity for exercises of varying intensity to revive motor function, health screening with vital signs measurement, and adjustment for the next exercise session in a real-time situationVital signs or parameters monitoring—Assessment of parameters when there is no physical activity to see whether the medical therapy is acting as expected.Smart behavioral pattern analysis—Using Internet of Things (IoT) solutions for the seamless collection of different sources of data  and analysis.Compensatory strategies—Especially in the event that a care plan is not executed properly, allowing the system to give feedback. The user needs to be continuously engaged until he/she becomes habituated to the activities suggested  should be level- or intensity-adjustable based on patients' clinical evolution and based on his/her likeliness. These games should be motivational to avoid loss of interest.User literacy—Education of patients regarding disease, treatment, or handling of a device. It should be the objective of any rehabilitation strategy to encourage the patient to be independent.Device portability—Any device or sensor that is attached should be lightweight and comfortable and should be focused on the body part involved.User needs are aligned with the input data analysis results, as mentioned above. For the success of a planned care rehabilitation, at home and not having permanent assistance from medical staff is a key aspect. Thus, a user-friendly coaching system ensuring guidance, assessment with immediate feedback strategy, and continuous engagement is needed. For ensuring patients' appropriate therapy, the coach should act personally and should provide assistance as required by the users' needs and individual rehabilitation care pathways. This will be outlined in vCare's virtual coaching concept.Self-management home-based rehabilitation solutions are often lacking specialized equipment and have insufficient alignment with patient's needs. Adaptability of the technical means to the individual care needs and abilities is mostly not provided. In the literature, several studies have analyzed the interaction between virtual coaching and users in various clinical settings and for different purposes. We have focused our selection on those studies that are dominant and throughout the years remain reference points in this field.Ding et al.  highlighIn , VCs andInterestingly, in a single case presented in , a powerVirtual coaching can furthermore be an efficient and low-cost approach offering an innovative opportunity to improve patient/clinician partnerships in managing chronic conditions, in particular in the improvement of patient/clinician communication .For the elder population, Bickmore et al.  and 14)14) evaluAlthough the literature on virtual coaching still reports a limited number of subjects involved and few clinical conditions investigated, by using rule-based coaching systems, data on the use of internet-based coaching are encouraging and show that people can get used to electronic communication in order to take advantage of it for their state of health; a recent scoping review on virtual coaching  concludeFurther to the scientific literature, several research projects  integrate technology of virtual coaching, such as the following: Well-being and Health Virtual Coach (WellCO) is a VC that is intended to cater to seven areas of human health, namely, cognitive stimulation, leisure and entertainment, support groups, physical activity, health status, nutrition, and tips . These aThe above-mentioned projects have been compared with vCare in terms of the following criteria: (a) secondary prevention approach, as secondary prevention using VC is usually neglected; (b) personalized care plan through adaptive care pathways in opposition with standardized care plan managed manually by clinician/caregiver instead of dynamic adaptation; (c) continuity of care, as it brings clinical focus to home contextualization, usually neglected in the clinical practice; (d) extended QoL perspective, as it focuses on multiple aspects of the secondary prevention other that rehabilitation; and (e) multiple clinical settings, taking charge of two different clinical contexts but for some related aspects due to severity and cure modalities, very close to each other.In sum, the related work shows that there is significant need to investigate virtual coaching for rehabilitation cases. As there is less evidence on the design and validation of VC systems, we summarize the main design principles of the vCare concept in the following chapter.The paper summarizes the conceptual work for a novel VC system based on personalized clinical pathways. It basically consolidates the finding of the project “vCare” , which is funded by the European Commission to advance the field of virtual coaching activities. The ambition for building an intelligent VC is mainly due to the scarcity of evidence for particular VC-based treatments , the appMaterials and Equipment, we present the materials and equipment, focusing on the vCare approach and the underlying architecture. In Methods, we present the methodologies by listing the four pathologies that we are addressing, as well as the chosen validation framework. In Anticipated Results, we present the clinical endpoints with quantified key performance indicators. Discussion is about the conclusions and the future work.This paper is organized into six sections. Following the abstract and the introduction, in vCare develops and investigates a new information and communication technology (ICT)-based concept, encapsulating a set of coaching services for empowering and motivating people, helping them to proceed with a personalized rehabilitation that complies with age-related physical, cognitive, mental, and social conditions by a VC.In particular, by utilizing the VC, we aim to enhance the QoL of patients by helping them to adhere to the care plan that consisted of personalized healthy recommendations for the daily activities. The coaching will lead to a continuous reduction of risk factors that are related to the probability of a relapse of the disease, the manifestation of disabilities, or the decline of  health see .vCare monitors the patients' daily activities, conditions, and habits to provide personalized feedback aiming to improve its QoL. For that, the coaching programs are initialized by specialists by assigning a well-elaborated clinical pathway as a tailored rehabilitation plan for the patient . According to this plan, vCare enables the personalization of the intensity and way of interaction and will trigger suitable exercises , suggestions, and feedback. Additionally, the context information that helps to monitor daily life activities and individual habits will be integrated.vCare integrates semantic technologies , well-elaborated coaching services, and clinical pathway services having the VC as the central and controlling element see .vCare channels the interaction with the patient at home via a virtual avatar, which communicates mainly, but not exclusively, via natural speech communication. Alternative modalities depend on the users' preferences or context. The VC encapsulates coaching and supportive services as well telemonitoring services. The latter provide the data-related basis for the VC's decision logic and tracing the data that the service provision is based on. However, the innovativeness of vCare does not only reside in the utilization of single technologies but in the combination of these technologies see . This isRefraining from static rehabilitation programs (which might be feasible in the inpatient sector), the continuous adaptation to patients' needs and context ensures the patients' empowerment to adequately deal with their circumstances. The system focuses on the change of people's attitudes and behaviors toward a healthier lifestyle and supports the elderlies in their everyday rehabilitation activities and supports better therapy adherence. In general, there would be two options in case a patient leaves the desired safe thresholds vCare's architecture follows an open and decoupled design to leverage the existing solutions. vCare addresses the challenge to assemble the different existing solutions to integrate required functionalities for context integration, usage of clinical pathways, monitoring of health data, and provision of smart avatar as well as a set of serious games see . The desThe core layer is the knowledge layer, which deals with the ontology-based storage, and reasoner for personalization of the user interaction concerning the behavior of the patient and his/her care plan. This also allows data exploitation and the dynamic generation of new interactions to fulfill specific needs according to the patient profile and the specific context.The sensor layer provides the services to integrate sensor data that provide vital data as well as context information such as movement patterns. Therefore, this sensor layer integrates open IoT platforms. vCare deployed FIWARE as the reference platform for the middleware. FIWARE has shown the greatest adoption in the market in the last years as well as covering very well cross-domain scenarios. However, for covering the specific domain of the smart-home at the edge, universAALvCare as a service approach in order to give access to its valuable intelligent resources. The first use case to be implemented will be to install a serious game platform to provide both physical and cognitive training. The following figure shows an overall view of the first release of the vCare platform.The highest layers in the architecture in The major advancement of the vCare concept is an adaptive integration and use of several coaching, monitoring, and adaptation services according to a personalized pathway. All services, devices, and sensors can be (re-)composed and re-configured if needed. The knowledge layer's agents address the relevant medical use cases by making activity or adaptation recommendations. Moreover, the knowledge layer's ontology contains the information about the patients' needs and conditions, services, and clinical pathways. The ontology gives the input for the machine learning (ML) algorithms that analyze the compliance and progress of the patient in comparison with his/her pathway. Depending on the result, adaptations of the former pathway can be proposed.The primary innovation for a “virtual coach” is that it aims to support health management for secondary prevention as a home self-care service . CurrentOverall, within vCare, it is foreseen that the prototype of the VC system, once defined and developed in a highly multidisciplinary context , has to be evaluated according to an incremental approach composed of different phases with the aim of testing its functionalities, usability, and reliability.Ordinarily, rehabilitative therapy begins in the acute-care hospital after the person's overall condition has been stabilized. The main clinical ambition of vCare is to fill one of the existing gaps in chronic patients' long-life management, with an ICT system able to give a patient's tailored care pathway after hospital discharge.Neurological and cardiovascular diseases embraced in the vCare context are especially relevant for people as they age. For sure, both these pathologic conditions are quite diverging with regard to the acute onset or their chronic course but tend to converge when speaking about their pathogenic mechanisms and therapeutic treatment. That is why, fortunately, the overall procedural structure of the rehabilitation phase is still somehow similar. However, particular rehabilitation care pathways differ at some points. The main objective following a rehabilitation plan is to regain body control. In fact, patients are supported to re-learn or regain the ability to deal with their home environment without (extensive) help. Due to the major progress in the acute treatments , the majority of people survive acute severe events. Long-term disabilities are however still very prone to appear. vCare seeks to support the rehabilitation process by reducing the disabling conditions as much as possible.The different testing phases that have been identified in vCare and the overall implementation scope are depicted in vCare is deploying a 2-fold joint testing phase for the validation studies. This is intended to provide real-time feedback from the patient perspective, where the patient is simulated by a physician. The users are members of the rehabilitation team, and they have to test the platform from the clinical point of view. First, the testing phases are carried out in a tech lab strictly in contact with developers. The different functional parts and services that build the entire prototype will be deployed in this laboratory site, trying the integration with external systems. The system will be refined, taking into account the results of these tests. The purpose of the pre-evaluation is to verify the compliance of the prototype to the clinical requirements or the external validity in terms of fitness with the needs of the reference sites. The vCare project has set up a tech-lab phase in three different sites , to work in the technical integration and validation that should release the first version of the vCare service.Second, the clinical evaluation phase will initially be performed in a controlled environment (living lab) where the patients behave like being at home while they stay in the clinic. For this, a domestic environment is usually foreseen in a rehabilitation hospital for occupational therapy purposes, equipped with project technologies. The users are real patients. Initially, a member of the rehab team supervises them. Later, they are left alone with the VC but always in a “protected” environment. In the second stage, which will be performed with a subgroup of patients, users will act alone in their home setting . To assess the compliance of the VC-supported rehabilitation scheme, the evaluation will be done based on concepts derived from clinical good practice and overall experiences together with standard procedures for rehabilitation.Finally, the validation study (pilot test study) will evaluate the efficiency of a real home-based rehabilitation program using the VC of vCare. In this pilot study, patients will be recruited and engaged in a personalized home rehabilitation program, based on the clinical profile and the clinical status of the patients. The study will be conducted in a home setting and will be configured as a randomized controlled trial (RCT), with the enrolment of patients who will undergo home rehabilitation with the support of the VC and patients who will receive conventional rehabilitation . The overall aim of the pilot study is to refine and test the use of the vCare coaching system to find out if it is useful in motivating and empowering patients with various functional impairments and comorbidities to engage actively in performing personalized rehabilitation activities in order to be independent and to improve their QoL.Summing up, the first step is the validation from a technical point of view without the involvement of patients. This is to primarily test the correct functioning, the infrastructure, and security of the solution (tech-lab phase). This phase is managed at the technical development centers with the supervision and the participation of clinical staff. On that basis, the living-lab phase will start. This phase aims to evaluate the usability and liking of patients and the abilities of the system of adapting according to patients' characteristics and needs in a controlled environment . Third, the testing process of the vCare virtual coaching system will end with a pilot RCT (pilot test phase) that will be carried out in patients' real homes. This last and  most important phase of “clinical validation” will be presented and is in focus in the remaining manuscript.The system will be evaluated for neurology's [patients affected by stroke (SD) and PD] and cardiovascular patients' rehabilitation (patients affected by HF and IHD).The situation for these four pathologies slightly differs with regard to eligibility criteria outcome measures and care scenarios at all. Therefore, we first briefly outline background information on these four cases and the envisioned advancements (compared with the situation without the vCare solution showing how the VC can advance within the different rehabilitation scenarios). Afterwards, we continue to describe the foreseen shape and constraints of the study and how to operationalize and test the expected advancements.Stroke is the leading cause of disability worldwide. It causes different impairments such as loss of muscle tone and strength, sensation and coordination deficits and gait and balance disorders, motor disabilities of the upper limbs and hands, deficits in haptic, praxias, and bimanual and/or hand–eye coordination disturbances. Impairments and the related disabilities may have a significant impact on an individual's independence, safety, and QoL. Cognitive impairment can be associated with stroke and has important repercussions in everyday life as well. While stroke patients highly benefit from intensive rehabilitation program in the inpatient setting, they suffer from the lack of continuity and support of a comprehensive rehabilitation program at home to optimize motor and cognitive recovery.The home rehabilitation pathway lacks clear clinical guidelines and requires major personalization effort with regard to the patient's condition, needs, and abilities. vCare will allow to fill the gap of the care continuum between discharge from the hospital. It supports professional care and provides a reliable tool for motor and behavioral engagement supporting the patient's long-term recovery from stroke's impairments.PD is the second most common neurodegenerative disease after Alzheimer's disease. It is estimated that the prevalence is 2–3% of patients over 65 years of age. The main problems of the disease are in the motor domain  and in the neuropsychiatric sphere . Most of these aspects can benefit from an intense rehabilitation program for both motor and cognitive recovery. In addition to the presence of the symptoms with the evolution time, it can be observed that these symptoms have both motor and non-motor fluctuations. This requires individualized adaptation of PD-specific rehabilitation programs (currently not available in the EU), ensuring good adherence, the same as for the pharmacological treatment, and avoiding the risks inherent to this symptomatology.There is still a lack in a comprehensive and standardized approach in implementing home-based rehabilitation. The vCare solution integrates sensors that collect information from motor and non-motor symptoms by adapting this information to specific processes of rehabilitation at home and, respectively, supporting the patients in daily life given the personal restriction caused by PD.For HF patients, cardiac rehabilitation represents a professional program, medically supervised, and individualized accordingly to the disease and to the needs of every patient. It has a decisive role in helping patients recover after an unwanted cardiovascular event and mainly to help in getting healthier and in passing easily through the early outpatient period. This includes educational services and guidance meant to support the patients in changing their lifestyle, to improve the effort capacity and the overall health, and to reduce the risk of future cardiac events.It is important to offer a different intelligent model of home rehabilitation based on rules and machine learning supported by a VC, which could improve uptake in cardiac rehabilitation programs and ensure longevity by tackling some of the mentioned barriers .IHD is one of the leading causes of death worldwide . HoweverFor IHD patients, it is important to offer a transition to cardiac rehabilitation in the home setting after completing phase II, as there is a substantial lack of uptake and lack of adherence to both the phases. A VC system interacting and coaching with the patients in their home might be a solution to manage risk and lifestyle control and to improve QoL.The pilot study is designed as a prospective interventional study whose primary purpose is supportive care. The study will be conducted in a home setting and will be configured as an RCT. Specifically, the physician will recruit patients upon discharge from the hospital, following inclusion and exclusion criteria and will plan a personalized rehabilitation program for each subject. For each site, a subset of 10 patients (for each disease) will follow a rehabilitation program at home with vCare for a 6-month period. A second group of 10 patients (for each disease), enrolled as a control group and matching demographic characteristics, will be used for the comparison in terms of clinical and economic efficiency.Only for HF patients will a third group be enrolled: it will consist of patients who will have to perform 6 months' ambulatory rehabilitation program which consists of three 45-min physical activity sessions per week, one monthly psychological session, and one monthly dietary session. Following the 6 months' period, all three groups will perform a cardiovascular evaluation to assess their evolution.Each of the four clinical centers will recruit 10 patients  to use the VC system individually at home.Twenty patients affected by SD conditioning a physical and/or cognitive long-term disability and discharged home after an intensive inpatient rehabilitation program will be recruited.Inclusion criteria: (i) patients who suffered a recent (<1 month) stroke (either ischemic or hemorrhagic) confirmed by CT and/or MRI scan, with a National Institutes of Health (NIH) Stroke Scale/Score (NIHSS) score of ≤ 14, corresponding to a mild–moderate neurological impairment; and (ii) age ≥ 65 years.Exclusion criteria: (i) global aphasia; (ii) presence of cognitive impairment ; (iii) any evidence of a serious condition that may affect participation in the study ; and (iv) unable to understand and comply with the protocol or to give informed consent.Twenty patients with PD, in a stage that causes them to have a disability with motor and non-motor symptoms (mainly cognitive), will be recruited. After a rehabilitation period at the hospital, adapted to their stage of illness, they are discharged to follow a personalized rehabilitation program.Inclusion criteria: (i) patients diagnosed with PD according to established clinical criteria (Brain Bank of London); (ii) age > 65 years; (iii) patients with scores higher than 60% on the daily life activities scale of Schwab and England; (iv) presence of motor fluctuations perceived by the patient; and (v) Hoehn and Yahr stage 1 and 4.Exclusion criteria: (i) patients diagnosed with atypical parkinsonism; (ii) patients with scores below 24 on the Montreal Cognitive Assessment (MOCA) scale; (iii) specifically exclude bedridden or dependent patients; (iv) patients with other chronic diseases such as HF, severe lung, or liver problems; (v) patients with severe psychiatric problems such as hallucinations or major depression; (vi) patients with poor adherence prior to pharmacological or rehabilitative treatment; and (vii) unable to understand and comply with protocol and/or give informed consent.Thirty patients who suffered an HF hospitalization that will match the inclusion criteria and will not have any of the exclusion criteria will be enrolled.Inclusion criteria: (i) diagnosis of HF class New York Heart Association (NYHA) II–III; (ii) age > 65 years; and (iii) hospitalized in the cardiology department of the Romanian clinical reference site.Exclusion criteria: (i) unstable angina; (ii) systolic resting blood pressure > 200 mmHg, diastolic blood pressure > 110 mmHg; (iii) blood pressure decrease with more than 20 mmHg at standing position; (iv) severe aortic stenosis; (v) sepsis; (vi) uncontrolled arrhythmias; (vii) uncontrolled atrial tachycardia heart rate (HR) > 120 bpm; (viii) decompensated HF; (ix) atrial-ventricular grade 3 transmission block; (x) recent pulmonary thromboembolism; (xi) phlebitis; (xii) persistent ST segment elevation of more than 2 mm; (xiii) uncontrolled diabetes; (xiv) locomotor disabilities; (xv) thyroiditis, hypokalemia, hyperkalemia, and hypervolemia; and (xvi) unable to understand and comply with protocol and/or give informed consent.Twenty patients affected by IHD, discharged from hospital and having finished a 12-week hospital-controlled mandatory cardiac rehabilitation program, will be recruited.Inclusion criteria: (i) patients hospitalized with IHD, either an acute ST- or non-ST myocardial infarction or a condition with unstable or stable angina pectoris requiring a percutaneous coronary intervention, a coronary artery bypass grafting, and/or medical treatments; (ii) age ≥ 65 years; and (iii) finished with the 12-week hospital-controlled mandatory cardiac rehabilitation program with permission from cardiologist to transit to phase III cardiac rehabilitation.Exclusion criteria: (i) unstable condition requiring treatment; (ii) unstable angina pectoris; (iii) uncontrolled sustained arrhythmias; (iv) severe valve diseases; (v) severe uncontrolled hypertension with resting systolic blood pressure > 180 and diastolic blood pressure > 110 mmHg; (vi) active myo-, peri-, or endocarditis; (vii) recent pulmonary embolism; and (viii) unable to understand and comply with protocol and/or give informed consent.The overall aim of the pilot study is to test and refine the use of the vCare coaching system so that it can motivate and empower patients with various functional impairments and comorbidities to engage actively in performing personalized rehabilitation activities, improving their independence and QoL.The specific objectives of the pilot study are defined as follows: (i) enhancement of QoL (primary outcome); (ii) risk factor reduction; (iii) better adherence to care plan at home; and (iv) personalization and health promotion. This is operationalized for the four relevant pathologies in the following.Furthermore, the pilot study will also test usefulness, feasibility, and effectiveness of virtual coaching in home setting in neurological and cardiologic patients.Enhancement of QoL Measure, in Particular Expectingincrease of the QoL score (SF-12), at the end of in-house pilot study period (6 months),increase of the SF-12 mental sub-score ,increase of the QoL score on the Stroke Self-efficacy Questionnaire, at the end of in-house pilot study period (6 months).The questionnaires will be administered to the two groups of subjects at the beginning of the study and after a follow-up of 6 months. A trained health professional will administer the questionnaires.Enhancement of QoL measure, in particular expectingincrease of the QoL score (SF-12), at the end of in-house pilot study period (6 months),15% increase of the SF-12 mental sub-score ,10% increase of the QoL score (PDQ-39), at the end of in-house pilot study period (6 months).SF-12 and PDQ-39 questionnaires will be administered to the two groups of subjects at the beginning of the study and after a follow-up of 6 months. A trained health professional will administer the questionnaires.Enhancement of QoL measure through SF-12, in particular expectingincrease of the QoL score (SF-12), at the end of in-house pilot study period (6 months),increase in the Minnesota Living with Heart Failure Questionnaire (MLHFQ) score.SF-12 and MLHFQ questionnaires will be administered to the three groups of subjects at the beginning of the study and after a follow-up of 6 months. A trained health professional will administer the questionnaires. Increased cardiovascular fitness will be defined as increase with more that 10% of the exercise capacity measure in the VO2max .Enhancement of QoL measure through SF-12, in particular expecting10% increase of the QoL score (SF-12), at the end of in-house pilot study period (6 months),15% increase of the SF-12 mental sub-score .SF-12 questionnaires will be administered to the two groups of subjects at the beginning of the study and after a follow-up of 6 months. A trained health professional will administer the questionnaires.The secondary outcome measures will emerge from the vCare platform, as follows.Risk factors are disease related, and their detection depends on the rehabilitation pathway. While piloting vCare, the patient's lifestyle will be monitored, and specific risk factors will be determined. The goal is to reduce each specific risk factor by at least 10% of the baseline value at the end of the study.The primary risk factor for the subjects with stroke will be related to the considered tendency toward inactivity, measured according to the following parameters:daily number of steps, expected to improve at least by 10% in 6 months;time spent exercising, expected to improve at least by 10% in 6 months.Rate of falls and fall risk reduction will be first investigated in the living lab, and if feasible, they will then be measured in the home setting also, as monthly number of falls. In the behavioral context, the tendency toward a less risky and more correct behavior will be measured according to the weekly number of corrective e-learning lessons that followed.The primary risk factor in patients with PD will be the increase in OFF time and detection of specific gait problems such as freezing, measured through the following parameters.OFF time in hours, expected to improve at least by 10% in 6 months;daily number of steps, expected to improve at least by 10% in 6 months;number of freezing episodes of the march, expected to improve at least by 10% in 6 months.The primary risk factor for the subjects with HF will be related to sedentary lifestyle, measured according to the following parameters:daily number of steps, expected to improve at least by 10% in 6 months;time spent exercising, expected to improve at least by 10% in 6 months;more than 15% reduction in low-density lipoprotein (LDL) cholesterol levels.In the behavioral context, the tendency toward a less risky and more correct behavior will be measured according to the weekly number of corrective e-learning lessons followed. Moreover, control of vital parameters will be taken into consideration (Table 11).The risk factors monitored for the subjects with IHD according to the Use Cases will be related to sedentary lifestyle, measured according to the following parameters:daily number of steps, expected to improve at least by 10% in 6 months;time spent exercising, expected to improve at least by 10% in 6 months.Also changes in weight loss , medication adherence, anxiety reduction, and smoking cessation will be taken into consideration. In the behavioral context, the tendency toward a less risky and more correct behavior will be measured according to the weekly number of corrective e-learning lesson that followed.vCare, raised up to 80%.Total no. of subjects that adhere to the Care Plan with Treatment adherence will be evaluated by monitoring access to the vCare platform and/or interaction with the VC at least once a week for a continuative period of 3 months.These actions include turning on the tablet and/or smart TV to access the platform, receiving feedbacks from the VC to the patient and vice versa, performing of at least one suggested activity per week, and recording of vital parameters. In other words, through the data collected inside the platform, the compliance of the patients will be assessed, expecting 80% of the subjects to engage consistently with the home program.vCare, raised up to 70%.No. of rehabilitation treatments, performed autonomously by the patient supported by It is expected that 70% of the rehabilitation plan, as in 70% of the proposed activities by the VC, should be followed, on average in 3 months.Refinement rate of rehabilitation therapy (exercises and programs) raised up to the 60% of the cases that require a change of treatment.The refinement rates have been hypothesized as the number of changes in the pathway solutions suggested by the vCare solution during the pilot test. The result shows the times in which the pathway changes.In order to measure acceptability and identify the characteristics of subjects who were likely to reject technological health services, the SUTAQ (Service User Technology Acceptability Questionnaire) will be administered at 6 months from the pilot test start, expecting to score in the highest quartile.Objectives of the Pilot Study.The overall aim of the application of the vCare's VC is to improve the patient's QoL after an acute episode or chronic disease-related rehabilitation. The vCare system will be a persuasive and non-obtrusive technology intended to help people change their habits and improve their home-based rehabilitation. Therefore, they can become healthier and gain more healthy time of living. The expected results are grouped into the following categories, based on four clinical objectives: Recovery of an Active and Independent Life at Home; Risk Factor Reduction; Better Adherence to Care Plan; and Personalization and Health Promotion . Here, wThe approach of rehabilitation guidance by a virtual coaching system does not intend to replace specialists but rather complement their work to guarantee the continuity of care in the home environment and daily life where current rehabilitation programs often fail because of the lack of continuous assistance. The VC should lead to a continuous reduction of risk factors that are related to the probability of a relapse of the disease, the manifestation of disabilities, or the decline of mental health.The global value of a rehabilitation program for a patient is hard to be evaluated in an objective manner because it is related to the individual's satisfaction or happiness with life in domains that he or she considers as important. The results of rehabilitation are often evaluated by applying standardized clinical scales that evaluate physical improvements and/or functional impairments, but these scales omit the measures of the patient's own perception of the mental and emotional effects of the bodily disabilities. Because of this, the state-of-the-art of QoL evaluation is based on predefined standardized tests like the SF-12. Taking into consideration the way in which patients view their own health situation is the most important element of patient-centered health care. Given the complex nature of clinical evaluation procedures, evidence regarding effects between QoL, and patient engagement is quite weak. Thus, vCare aims to enhance the QoL and provide a reliable basis with a 10% increase of the QoL score (SF-12), as off the in-house pilot study durations (6 months) and a 15% increase of the SF-12 mental sub-score .A relapse or decline of health status closely depends on the evolution the patients' risk factors . The rehabilitation supported by a VC should facilitate the reduction of risk factors. The risk factors are disease related, and their detection depends on rehabilitation pathway . While piloting vCare, the patient's lifestyle will be monitored, and specific risk factors  will be determined. The goal is to reduce each specific risk factor of at least 10% of the baseline value, at discharge.Often, patients disregard discharge recommendations or prescribed therapy protocols. The rates of non-adherence to chronic illness regimens are estimated to be between 30 and 60% , which iHome rehabilitation eases the alignment of the rehabilitation process with the patients' daily activities in his/her her home surroundings. To align the rehabilitation program to the physical, cognitive, and social status of the patient, it has to be personalized to patients' needs and conditions. Especially when it comes to non-chronic diseases, adequate health promotion can support long-term behavioral changes, decreasing the economic costs and the probability of a relapse. vCare expects a refinement rate of rehabilitation therapy (exercises and programs) raised up to the 60% of the cases where a change of treatment is required. The acceptance of the VC  will be assessed through specific questionnaires , measured in the highest quartile score .Rehabilitation after an acute episode or in a chronic disease is a key aspect for patients' management and is a main contributor to their increase in the QoL associated with improved self-care. Overall, inpatient multidisciplinary rehabilitation remains one of the cornerstones in the treatment of disabilities. Rehabilitation strategies particularly support patients in relearning skills that are lost and adapting healthy habits. However, the lack of continuity of care after the inpatient care and rehabilitation leaves a gap in the continuum of the fragile patients' management . The patients' motivation should be sustained during physical activity sessions in order to maximize the rehabilitation outcome. Motor actions need to be practicable in different tasks and environmental contexts to develop motor schemata that are versatile enough to meet the situations they encounter in daily life. Therefore, coaching systems must activate appropriate strategies to improve the motor and cognitive impairments of patients. vCare's VC will continuously apply and provide personalized rehabilitation recommendations, so that patients with such impairments will be supported and coached to improve their body functions and healthy habits. By doing so, the factors limiting the patients' reinsertion into their home or community and living independently will be diminished. In the digital era that we live in, the technological advances should provide a solid ground for developing new rehabilitation practices that could help patients with NCDs to improve their health condition. vCare will combine at the same time with technological advancements in terms of sensors devices for monitoring the patient's vital parameters and location with the advantages provided by the gamification elements in terms of therapeutic serious games, pseudo-human interaction possibilities by means of the virtual avatar, and machine learning abilities. All these will be supporting the personalization and adaptation of the treatment based on the initial plan, set rules, acquired data, and inferred behavioral patterns, blended with insights from clinical pathways.Anticipated Results. Limitations, such as low adoption level, cannot be excluded; however, they have been identified as risks with a mitigation strategy. Furthermore, the ethical approval process at the clinic reference sites' Ethical Committees has started, and approvals are confirmed. This is the basis to start the test phases with the patients in Autumn 2020.The concept of vCare is currently under validation first in tech labs at the present time, aiming to provide clear evidence about its impact on the rehabilitation process with regard to the efficiency and the tangible outcomes to the QoL, independent living, recovery, risk factor reduction, better adherence, and personalized health promotion of patients, as described in The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The virtual coach solution makes it possible to utilize a means of communication for the patient. Acting like a physical coach, the VC channels the information and provides suitable recommendations to the patient, as well as engages the patient “like a coach” to stay in their “training plan” and also provides a backlink of the outcomes as well as a possibility of intervention to the physician. This paper summarizes the core concepts and design decisions of the European vCare project.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.JR and AM were employed by MYSPHERA. LP was employed by Imaginary s.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The COVID-19 pandemic has a significant impact on health care processes. Precautions such as restrictions imposed on visitors and social distancing have led to multiple challenges, particularly in terms of communication. Using the case of nursing, palliative and hospice care facilities in Germany and drawing on agenda-setting theory, the present study aims to shed light on how health care facilities use their websites to inform  health care users about changes in regulations, new protective measures implemented and about recommendations in the context of the COVID-19 pandemic.The websites of nursing, palliative and hospice care facilities in Germany were examined using qualitative document analysis and qualitative content analysis. A total of 138 websites was analysed in the study. The data gathered includes all information about COVID-19 on these websites published from the beginning of March until August 15, 2020.Facilities show similarities in adhering to the measures taken by the authorities to restrict the spread of SARS-CoV-2 and to protect vulnerable patients and employees. All facilities urged the public to avoid paying visits to patients in facilities unless there was an emergency; however, visiting procedures in practice varied by types of facilities. For optimal communication, telephone and especially video calls were the options preferred by health care providers and health care users. Facilities made great efforts to prevent emotional stress and to strengthen resilience among all stakeholders. Transparency was adopted by many facilities in order to build the public’s trust.The agenda of health care facilities has been seriously affected by the COVID-19 pandemic. The study sheds light on the strategies developed by facilities, their efforts to increase emotional resilience among health care staff and health care users, the ethical guidelines they have adopted regarding privacy policies as well as how these themes are communicated via the facilities’ websites. The results can inform other health care facilities about how websites can be used as essential communication tools in times of public health crises. Countries have been affected by the COVID-19 pandemic at different levels, and the precautions taken by them have also differed in terms of their general characteristics resulting from normative regulations and cultural values. The number of cases still shows marked fluctuations for each region. With some exceptions, the general trend in most countries, including Germany . During communication between the health care facilities and health care users. The high load of information with respect to the spread of SARS-CoV-2, the symptoms of COVID-19, and rapid changes in regulations in response to daily developments have created the need and expectation for up-to-date information provided by respective facilities. Confronting everyday practices that are drastically unfamiliar : ‘Visiting inpatient care facilities, retirement homes, and senior citizens’ residences is prohibited.’” (In-3).“Excerpt from the general decree  have been cancelled right at the beginning of March until further notice.” (In-2).“A second goal of facilities that emerged from the websites was to adapt to everyday practices that were drastically different from what they were familiar with. The roadmap for inpatient nursing facilities to tackle the COVID-19 pandemic consisted of the establishment of crisis teams evaluating the current situation on a daily basis. In order to protect existing patients and employees, and also considering the expansion of current capacity to meet the need for treating potentially increasing numbers of COVID-19 patients, the availability of medical supplies and equipment were other important aspects for inpatient nursing facilities. The facilities also shared the same goal regarding the recruitment of additional staff to fill gaps resulting from staff reporting sick, to reduce their employees’ workload, and to ensure the continuity of their services. Individual diagnostic measures and therapeutic interventions in inpatient nursing facilities and therapeutic measures in palliative and hospice care facilities were postponed at short notice, although urgent operations continued to be carried out in both types of facilities. Moreover, all events, activities, and courses in facilities were cancelled to reduce the risk of person-to-person transmission:“Current information for visitors and relatives:Please register early and bindingly before each visit so that there is no unnecessary waiting time.When you enter the elderly home, you enter a protected area. Please act accordingly and follow the instructions of employees. The protection of our residents and employees remains our top priority.” (In-50).Due to the measures taken by facilities and the rapid changes in regulations depending on daily developments, the facilities’ communication of up-to-date information was one of the first requirements to establish understanding between health care providers and other stakeholders.“Since contact with relatives is also important for patients, we ask you to maintain it in other ways .” .Avoiding face-to-face communication was a measure adopted by almost all the facilities to decrease the risk of direct person-to-person transmission. Due to the diversity of services provided, the continuity of communication between patients and their relatives was, naturally, more of a priority issue in inpatient nursing and palliative and hospice care facilities than in outpatient nursing facilities. For optimal communication, telephone and especially video calls were the options preferred by respective facilities:Live chat or video chat with clients was also preferred over in-person conversations in the facilities’ counselling offices.Some COVID-19-related information were encountered on the websites of inpatient nursing and palliative and hospice care facilities. Frequently asked questions (FAQ) pages and general information provided on COVID-19 and SARS-CoV-2 were generally related to answers to the following questions:How is the virus transmitted?What are its symptoms?What should be done after COVID-19 is contracted?What should be done in case of suspected infection?How can COVID-19 be avoided?Where can more information be found?Relevant links for further information included the websites of municipalities, the Robert Koch Institute, the Federal Ministry of Health, and the respective State Ministries of Health. Moreover, almost all the facilities highlighted that they were taking all the appropriate precautions, based on the recommendations of the Robert Koch Institute.“The on-site managers will be happy to answer any questions you may have. We all hope that at some point, normality will return for our residents, patients, guests, and our employees. Until then, however, we are very grateful to everyone for having coped with this strange time so well.” (Out-25).On their websites many facilities documented their efforts to show appreciation for their employees’ concerns, emotional stress and anxiety, as well as to increase their motivation, build emotional resilience, and to create a sense of solidarity among health care staff and health care users:name of a palliative and hospice care facility] support group would like to thank Y [names of companies and organisations] most sincerely for the large and small donations that have been paid into the account of the X support group in the past two weeks.” .“Our employees and the board of directors of the X . The man and the two women were aged 84, 87 and 91 respectively, and had previous illnesses. […] The positive cases are all in the new building, which is completely separated from the old building.” (In-6).“Another three residents have died in the X [Considering the choice of the content presented on the websites, the question of how the facilities have approached the pandemic agenda and cared for the rights of their patients is among the major challenging moral issues in the health care system during the pandemic. The content of the websites demonstrates that many facilities emphasized to be “transparent” when sharing current information about their facilities in order to establish the public’s trust. This information could also include, for example, the number of patients or employees who have contracted the virus, the daily death toll, and other daily developments:“I agree and consent to my data being stored in connection with the currently applicable regulations concerning COVID-19 and, if necessary, being used by the clinic or the responsible health authorities to trace contact persons. The data will be stored for a maximum of three months and destroyed afterwards.” (In-13).Facilities required visitors to use an app or a registration form in order to provide personal information and information on the current health status of the visitors, to regulate the number of incoming visitors, and to simplify appointment booking processes for visitors. The purpose of these procedures was also to ensure the wellbeing of patients and employees and to enable the authorities to trace any potential infection chain:There were special arrangements for visiting patients in exceptional cases such as their final days of life. Patients who were in inpatient nursing facilities, and particularly in palliative and hospice care facilities, benefited from these arrangements. According to the precautions taken by the authorities, the ban on patient visits could only be lifted at the discretion of the doctors treating the patient in question.The three main themes and their sub-themes identified in the present qualitative study elucidate how nursing, palliative and hospice care facilities manage their COVID-19-related agendas in order to raise public awareness of measures and applicable recommendations. The websites reviewed from different types of facilities have demonstrated that official regulations, especially the restrictions concerning physical contact and hygiene measures and compliance with these regulations were the priorities creating the respective agendas. Apart from general suggestions on social distancing, the pandemic agendas focused on the restriction or bans imposed on visitors. The facilities followed detailed guidelines including the implementation of mandatory pre-registrations.At the beginning of the pandemic, the lack of experience and the uncertainty around SARS-CoV-2/COVID-19 led to the development of short-term solutions  to reducThe COVID-19 pandemic had also a direct impact on the field of communication. Communication via internet-based solutions instead of personal conversation in visits and at the counselling offices  was prefBased on the websites it could be observed that sharing up-to-date information on developments occurring in a facility was also significant in ensuring active and transparent communication between health care providers and users. Regardless of their type, facilities sought to provide reliable and detailed information about COVID-19 and SARS-CoV-2 on their websites to all stakeholders. Considering the social diversity within the populations benefiting from the services, some inpatient nursing and palliative and hospice care facilities offered multilingual COVID-19-related information. The Robert Koch Institute was generally portrayed as one of the major reliable sources and was thus recommended by facilities for following COVID-19-related information, reflecting the fact that the Robert Koch Institute was broadly regarded as the main scientific authority in Germany.The pandemic agenda of facilities also included efforts to increase the emotional resilience of health care users and staff and to strengthen their collaboration. In order to decrease levels of emotional stress and anxiety , all facAs the websites show, the performance of religious rituals within facilities was similarly affected by social distancing rules implemented to prevent the spread of SARS-CoV-2 – same as in the general society . ReligioTransparency was adopted by many facilities as an important element to build trust among the public and potential health care users. The websites indicated the apparent connection between feelings of social unity and transparency , 27. EthTo the best of our knowledge this is the first study which systematically examines how providers of nursing, palliative and hospice care make use of their websites as communication tools during the COVID-19 pandemic. The study is unique in that it compares the pandemic agendas of three different patient-focused health care services and examines their websites with regard to COVID-19-related information. Its strengths comprise the theoretical approach based on agenda-setting theory, the systematic sampling process and the large number of websites analysed. Limitations of the study include its focus on facilities from only three federal states; the inferences taken from quotations from these websites cannot necessarily be generalised to the facilities in other federal states in Germany. However, we do not assume that the spectrum of communication patterns differs between the three states examined and the other states in Germany.The study contributes to a better understanding of how health information are presented on the websites of different types of facilities during the COVID-19 pandemic. It sheds light on the strategies developed by the facilities, their efforts to increase emotional resilience among health care staff and health care users, and the ethical guidelines they have adopted regarding privacy policies.The findings of this study show that the agendas of health facilities have been seriously affected by the COVID-19 pandemic. Even if the agendas of different health facilities show similarities in the context of the pandemic, it is clear that facilities have been affected by the pandemic to different degrees, depending on the type of services they provide. This study demonstrates that the restrictions in person-to-person contact in health services has been dominant in the agendas, as circumstances have directly led to changes in visiting regulations. It further reveals that facilities sought to clearly convey that they acted in accordance with the authorities in the steps they undertook and that there is a link between the desire for social solidarity and the facilities’ demonstration of transparency to build trust among the public. The intense sharing of COVID-19-related information through websites connotes that web-based communication is considered by health care providers as an essential communication tool in times of public health crises. Future research should consider taking advantage of web-based data by giving it a more prominent place in scientific studies. It should further explore the policies these facilities will implement post-COVID-19 to prepare for possible future public health crises, and how they will differ across different federal states or countries.