Interprofessional team training has a positive impact on team behavior and patient safety culture. The overall objective of the study was to explore the impact of an interprofessional teamwork intervention in a surgical ward on structure, process and outcome. In this paper, the implementation of the teamwork intervention is reported to expand the understanding of the future evaluation results of this study.The evidence-based Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) program was implemented in three phases according to the program’s implementation plan, which are built on Kotter’s organizational change model. In the first phase, a project group with the leaders and researchers was established and information about the project was given to all health care personnel in the ward. The second phase comprised 6 h interprofessional team training for all frontline health care personnel followed by 12 months implementation of TeamSTEPPS tools and strategies. In the third phase, the implementation of the tools and strategies continued, and refresher training was conducted.Trial registration Trial registration number (TRN) is ISRCTN13997367. The study was registered retrospectively with registration date May 30, 2017 In today’s specialized, complex and pressurized healthcare, harm caused by adverse events during hospital care are acknowledged as a serious threat to patient safety, with human factors as a central issue , 2. EnhaA considerable number of adverse events are related to surgical treatment , 5. A miTeam training is an effective method to improve frontline healthcare personnel’s teamwork competencies . Team trTeam Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS)  was chosDespite previous research which shows that interprofessional team training interventions improve the quality of clinical practice, there is little knowledge about its impact on hospital wards. The overall objective of the study was therefore to explore the impact of an interprofessional teamwork intervention in a surgical ward on structure, process and outcome. Here, we report on the implementation of the TeamSTEPPS program as additional data to facilitate a better understanding of the evaluation results of this study.The interprofessional teamwork intervention was carried out in a surgical ward at hospital in eastern Norway. The ward was selected for convenience, and based on the management`s interest to participate in the study. The target group was frontline healthcare personnel consisting of physicians, registered nurses and nursing assistants working at the ward. A consecutive sample of elective and emergency patients with the inclusion criteria being 18 years or older, understanding Norwegian, and being in a mental and physical health condition that made it ethically justifiable to participate, were included in the study. Furthermore, healthcare personnel in a surgical ward at a hospital in southern Norway constituted the control group.The intervention was initiated by a research group consisting of four researchers from two universities in Norway. Two of the researchers and four of the leaders at the ward had attended a TeamSTEPPS master training. The TeamSTEPPS program and teaching materials were translated into Norwegian by a translation agency, and approved by three faculty members. The intervention was conducted according to the recommended TeamSTEPPS implementation plan , which iMultiple actions occurred to assess organizational readiness for a TeamSTEPPS initiative , 21. AftThe onset for the TeamSTEPPS intervention was 6 h of compulsory interprofessional team training conducted for all frontline healthcare personnel during work hours (n = 41). TeamSTEPPS leaflets and pocket guides were distributed to all healthcare personnel. The team training was conducted by the leaders of the ward in collaboration with two of the researchers (RB and ORA), and completed over 3 days  during 3 weeks in May 2016. The team training consisted of didactics, videos, role-play and high-fidelity simulation training with debriefing sessions. The first lecture aimed to create a sense of urgency (Kotter’s step 1) by presenting the Sue Sheridan video  and by pThe initial team training was followed by an implementation phase in the intervention ward. A Change Team was established (Kotter’s step 2) consisting of multi-professional healthcare personnel from the ward , in addition to the Chair of the surgical department, a former patient and a researcher serving as a coach (ORA). The Change Team served as a guiding coalition and was led by the Nurse Unit Manager. A vision of “Zero errors” (0 patient harm errors) was set, and an action plan was developed based on the identified patient safety risk areas in the ward and approved by the Chair of the surgical department (Kotter’s step 3). The action plan was communicated in unit staff meetings and by email to all employees (Kotter’s step 4). The leaders empowered a broad-based action to make the implementation as smooth as possible, and to remove obstacles that could undermine the changes (Kotter’s step 5). Posters with explanations of the TeamSTEPPS tools were placed in working stations at the ward. In addition, posters for patients and visitors were placed in the ward corridors, with a request to speak up if they perceived something that might be a threat to patient safety. One of the authors (ORA) coached the implementation by giving and gathering input from site visits and e-mail communications with the leaders and the Clinical Nurse Specialist, and as a member of the Change Team throughout the study period.The Nurse Unit Manager and the Clinical Nurse Specialist led the implementation of tools and strategies. Five TeamSTEPPS tools were implemented during the first 6 months of the study period . Five months after the initial team training, the master-trained Unit Nurse Manager and the Clinical Nurse Specialist organized 75 min TeamSTEPPS refresher training for the nursing staff. The master-trained Chief Surgeon conducted a 20 min refresher training for the physicians.The implementation of the tools and structural changes continued into the sustainment phase, with five more tools implemented during the next 6 months. After 11 months, one more 75 min TeamSTEPPS refresher training was conducted for all the nursing staff. The leaders and the frontline healthcare staff used the tools in their daily work, and the changes were consolidated (Kotter’s step 7). They anchored the change to let the change effort to become a lasting part of the organizational culture (Kotter’s step 8). This step is the final leg of Kotter’s leading change model. The implementation period was 12 months.An overview of the intervention is illustrated in Fig. The intervention will be evaluated by qualitative focus group interviews with healthcare personnel, and by quantitative questionnaires administered to healthcare personnel and patients. To study changes in patient safety culture, the Hospital Survey of Patient Safety Culture Questionnaire (HSOPS) , 24 willThe main limitations of these studies are as follows: (1) the change of the Unit Nurse Manager during the study period might have influenced the results, (2) the research team had limited control of the intervention, and (3) the convenience sample of the intervention ward could be a possible bias.


Previous research has shown that demographics, beliefs, and self-reported own health influence TTO values. Our hypothesis is that attitudes towards length and quality of life influence TTO values, but should no longer affect a set of related choices that are based on respondents’ own TTO scores. A representative sample of 1339 respondents was asked their level of agreement to four statements relating to the importance of quality and length of life. Respondents then went on to value 4 EQ-5D 5L states using an online interactive survey and a related set of 6 pairwise health-related choice questions, set up, so that respondents should be indifferent between choice options. We explored the impact of attitudes using regression analysis for TTO values and a logit model for choices. TTO values were correlated with the attitudes and were found to have a residual impact on the choices. In particular, those respondents who preferred quality of life over length of life gave less weight to the differences in years and more weight to differences in quality of life in these choice. We conclude that although the TTO responses reflect attitudes, these attitudes continue to affect health-related choices.The online version of this article (10.1007/s10198-018-1017-8) contains supplementary material, which is available to authorized users. The time trade-off (TTO) method is a widely applied method used in health economics to elicit respondent preferences for health state valuation  and moreIn an early survey , respondIn this paper, we explore the extent to which the trade-off valuations fully capture attitudes that are relevant to health-related choices. Our hypothesis is that attitudes towards length and quality of life influence TTO values. We then go on to test whether these attitudes towards length and quality of life have any residual impact on a set of related choices that are based on respondents’ own TTO scores.Cint and was approved by Ethics Committee at Glasgow Caledonian University.We analysed data from an internet-based survey conducted in June 2014 with a representative UK sample aged 18–70 . The finThe survey was split into four main sections. Prior to valuing any states, in section 1, respondents were asked their age and gender, their own health measured by EQ-5D 5L and questions to assess attitudes. Section 2 had respondents valuing states by TTO, while section 3 asked the respondents direct choices between two lives set up, so that respondents should be indifferent between the two lives offered based on their previous TTO responses. The final section asked respondents demographic questions including employment, marital status, and educational level.The health states used in the survey were based on the EQ-5D 5L descriptive system. Respondents were randomised into valuing a set of ‘mild’ or ‘moderate’ health states. The health states in the mild group were 11121, 21211, 12212, and 13122 and in the moderate group were 13122, 13224, 23242, and 23314. The states were chosen to have varying degrees of severity on different dimensions while minimising the likelihood that any state would be rated as worse than dead to circumvent the need to value worse than dead. Health state 13122 was common to both groups. Finally, the states were chosen such that there existed strict dominance between at least two states, allowing straight-forward checks for response consistency. We used 20 years throughout the TTO questions as life expectancy of most of the subjects in the survey was at least 20 years. Furthermore, longer TTO durations have been found to produce TTO values consistent with those derived over a 10 year period, and it is only shorter TTO durations that produce most differences [The TTO questions involved a between-group 2 × 2 design in which respondent were randomised to different TTO variants. The first broad difference between the TTO variants was whether the elicitation procedure was iterative or non-iterative. Respondents were first presented with a choice between 20 years in Life A and 10 years in Life B, as illustrated in Fig. The second broad difference between the TTO variants was whether the elicitation procedure for each health state was sequential or concurrent. In sequential procedure, the elicitation procedure was completed for each health state in turn before moving onto value the next heath state. In the concurrent procedure, the elicitation procedures were ‘spliced’ together, so the respondent considered the iteration procedure for the one health state and then the next iteration procedure for the next health state and so on until all four health states were asked, and then, the process was repeated. The health states were valued in the order: 12212, 11121, 13122, and 21211 in the mild group and 23242, 13122, 23314, and 13224 in the moderate group except for the non-iterative concurrent group, where health states were valued in a random order.For values in which the respondent continually refused to trade, 19, 19.5, and 19.75 years in full-health were offered, to increase sensitivity at this end of the scale. Conversely, for those who continually choose full-health and preferred 1 year in full-health over 20 years in the other health state, they were asked if they would prefer death, and if so these observations were assigned a value of 0 and dropped from the main analysis.F test to determine whether the dummies and their interactions were simultaneously zero. This is similar to testing for significant differences between a model with these variant variables added and a model without them, i.e., the difference between full model and reduced models.We ran a regression to explore the extent to which we could pool responses from the TTO variants to allow us to focus upon the impact of attitudes towards length and quality of life. The regression model that was constructed contained dummy variables representing the TTO variants and all seven states, with a dummy for the severity of the health states to which the respondent was randomised (mild and moderate). Also included were interaction terms (cross products) between states and TTO variants. We used an X years in one health state and Y years in the other. The choice questions were set up in order that they should be indifferent between the two lives offered based on their previous responses. The assumptions required for these choices to hold is mutual utility independence and constant proportional trade-off [Following the TTO exercises, respondents were presented with six pairwise choices which asked them to directly compare rade-off  and woulU1 and U2 are the TTO utility values for health states 1 and 2, respectively, and that U1 U2. The choice questions would present respondents with X years in health state 1 and U1/U2 × X years in health state 2. In each choice, one of the two states always appeared in Life A—while the other appeared in Life B—and this was set in advance. The health states included in Life A v Life B comparison were as follows: 11121 vs 21211, 11121 vs 12212, 11121 vs 13122, 21211 vs 12212, 21211 vs 13122, and 12212 vs 13122. Thus, either Life A or Life B could involve the greater number of life years—depending on the respondent’s valuation of the health states in the TTO. The number of years was randomly chosen as either 17, 18, or 19. Therefore, for example, if U1 and U2 equalled 0.6 and 0.8, respectively, and 18 years was selected as the value of X, Y would then be set at 0.6/0.8 × 18 = 13.5 years. Figure U1 = 21211 = 0.6 and U2 = 12212 = 0.8, and 18 years was selected as the value of X,To set up the choice, the computer program would first select the state with the lower utility value. For example, suppose that Respondents were asked to state their level of agreement with four statements, as shown in Fig. The Cronbach alpha coefficient was used to explore the internal consistency of responses to the Likert scales statements to establish if they were measuring the same underlying construct. In addition, it was important that each scale contributed some unique information to avoid duplication. The Cronbach alpha coefficient ranges from 0 to 1 with low levels indicating lack of internal consistency and very high levels indicating a potential redundancy of one or more scale. The threshold criterion used to determine whether the scales could be summed can vary by the number of items in the scale and whether the analysis is exploratory in nature to devise a new scale. In our analysis, we follow the standard criterion that a Cronbach alpha between 0.70 and 0.90  indicateWe explored the impact of the attitude scale using a generalised estimating equation model for TTO values and a logit model for choices which allowed for clustering of responses by respondent. Our hypothesis was that attitudes would affect TTO values, but should no longer affect a set of related choices that are based on respondents’ own TTO scores.2, gender, marital status (dummy for married), age left full-time education and employment status (dummy for employed full time), and the severity of the health states. A secondary analysis estimated the same base model but without the health state severity, to be comparable with the base model used by van Nooten and colleagues [F test (comparing the model with the variable included against a model with them excluded). All regressions and data analyses were handled in STATA Version 14.0 .We first estimated a base model that adjusted for demographic variables and included age, agelleagues . In the stan, derived from one or more attitude statements and that the ATLQLstan score was highest for those who indicated quality of life was most important and lowest for those who considered length more important:To establish if and how attitudes affected preferences elicited by TTO, the base model was run with a measure of attitudes towards length and quality of life. For example, suppose that respondents’ attitudes towards length and quality of life could be measured by the variable ATLQLstan was then included in the base model, as shown in Eq. . To establish if attitudes affected choices, the base model was run with ATLQLstan added , along with their interaction with years. The model was subsequently reduced until only pairs with significant interactions remained . Our hypothesis is that if attitudes have a residual impact on choices then for those respondents who have a relative preference for quality of life they will choose the life with the health state which brought them higher utility, and give less weight to the differences in years. For this reason, we expect the coefficient on the interaction term between ATLQLstan and yearsδ  to be negative.The constant term in Eq.  is the lIn total, there were 1462 respondents who took part in the web-based survey. Prior to analysis exclusions were made as shown in Online Appendix 1 which resulted in a ‘cleaned’ data set of size 1339. The mean respondent age was 45 with a standard deviation of 14 years, and a gender (%) split of 54 female/46 male. Approximately half of the sample were married (49%) and a further 12% lived with a domestic partner. A demographic breakdown of respondents is given in Table I would always prefer to have good quality of life than to live for a long time in a poor health state. The statement which caused the most to be unsure was statement 4 that was asked from a societal perspective, with 53% expressing a view that quality is more important than length. There was a mild trend with age and attitudes, with older people tending to have a preference for longer life.The responses to the attitudinal questions are shown in Fig. Table The four-item scale was active across all values in its range.stan, that is used throughout the rest of the analyses. The variable ATLQLstan score is highest for those who indicated quality of life is most important and lowest for those who considered length more important.A single scale was created by reversing the responses to statements 2 and 3 (as they were inversely correlated with statements 1 and 4) and the four scores summed. This scale was then normalised and standardised (see Online Appendix 2) to create the variable, ATLQLF test to determine whether the dummies and their interactions were simultaneously zero. The regression was run on the cleaned data set of 1339 and the detailed results are shown in Online Appendix 3. The regression results show that we can pool across all TTO variants provided we dropped health state 12212 in the iterative sequential variant and 23314 in the non-iterative sequential variant. The reported results for the remaining analyses are based on the pooled data set.We tested the extent to which we could pool TTO values from across the different TTO variants by running a regression model with dummies for the different TTO variants, and used an The base model included demographic variables and health states, and a secondary analysis included only demographic variables. We found qualitatively similar results between the two types of models, so report here the model that included demographic variables and health states. With all variables included, gender and employment were not significant and these variables could be omitted without loss of powerstan is significant as expected, and for every 1 standard deviation increase in the attitude scale individuals value states on average 0.0693 lower. Therefore, respondents that express a preference for quality of life over length of life typically trade away more time which results in lower values.Table stan is not significant as anticipated. The interaction term on ATLQLstan × yearsδ is significant  and negative. Respondents with a higher ATLQLstan score are, therefore, more likely to choose the life with the higher quality and the fewer years. For 1 standard deviation decrease in attitudes, individuals would require an extra 0.038 years (2 weeks) in order for their probability of choosing Life A to remain constant. Sensitivity analysis was run to explore if the exclusion of the two health states in the pooled data set could explain the results, but we found qualitatively similar results when these values were retained. We also ran the model with three-way interactions and found again that we were unable to reject the model that attitudes continued to influence choices (see Online Appendix 4).A similar process of elimination was used to derive a base logistic regression model for the choice data that is shown in Table In this paper, we explore the extent to which the trade-off valuations fully capture attitudes that are relevant to health-related choices. Our hypothesis is that attitudes towards length and quality of life influence TTO values, but should no longer affect a set of related choices that are based on respondents’ own TTO scores.As anticipated, we find that TTO values are influenced by respondents’ attitudes towards the length and quality of life, with a 1 Std Dev. increase predicting TTO values of 0.069 lower. This is a change associated with moving from the 50th to 83rd percentile on the attitude scale and so would be thought of as a clinically important difference in other contexts and similar in size to that of background variables like age and gender. Finally, the attitude variable modestly increases the ability to explain the variation in willingness to trade life years. For example, the ability to explain variation in TTO values increased from 0.2695 with the baseline model alone to 0.3085 with baseline model and attitude variables.We find the somewhat surprising result that though the TTO responses are affected by respondents’ attitudes towards the length and quality of life, attitudes have a residual impact on choices. In particular, the significant interaction between attitudes and years in the choice questions is − 0.0378 and suggests that respondents who preferred quality of life over length of life still preferred the life with the higher quality and the fewer years in the choices. Hence, it seems that using respondents’ own TTO scores to set up choices over two lives which they ought to be indifferent between is picking up some residual attitudinal impact.not the main drivers of the results reported here.Why might there be a residual impact of attitudes in these choices? The first explanation for this result might be around how we set up these choices. Most studies that estimate TTO values for QALY assume a linear QALY model holds  –20. HoweAnother possible explanation suggests that although the TTO responses reflect attitudes, they are not doing so adequately, so that people are not trading sufficiently in their TTO responses, so the choice questions are still picking up a residual impact of attitudes. This suggests at least here that choices are able to capture additional attitudinal issues better than TTO and raises the question about which is the better method to use. One crucial difference between TTO and the choices presented to respondents here is, of course, that each state is valued against normal health and death in TTO, while two health states are being valued ‘head to head’ in the choices. It is plausible that comparing two states directly focuses attention on differences in quality of life to a greater extent than in the TTO. As most interventions involve moving the patient from one health state to another, and it is ‘moves’ between health states that are commonly valued in economic evaluations, it could be argued that the ‘head to head’ evaluations are most appropriate. Therefore, if value elicitation exercises are to be more focused on policy orientated questions, it would seem the choice questions comparing different periods in ill-health are closer to these policy questions than TTO.There are a number of limitations of the study. Some of the characteristics which have previously been found to affect TTO values were not included, like number of children, household income and education, so these could have some underlying impact on the responses, but could not explain the continued influence of attitudes in the choice questions. A further limitation of the study design is that health states are not randomised to Life A and Life B in the direct choice, and the strong preference for Life A could, of course, indicate a tendency to favour the left hand option. However, again, this limitation is unlikely to explain the continued influence of attitudes in the choice questions. Finally, we used the TTO method and a limited number of health states so it is possible that our result might be influenced by the elicitation method used and the health states included.We conclude that although the TTO responses reflect attitudes these attitudes continued to affect health-related choices. Our study suggests that choices are able to capture additional attitudinal issues compared to the TTO and raises the question about which is the better method: a method that values health states against normal health and death as in TTO or a method that compares two health states ‘head to head’ as in choice.Below is the link to the electronic supplementary material.Supplementary material 1 (DOCX 76 KB)


The device is suitable for the use in a biochemistry environment. It has a chamber size of 72 mm such that a 100 ml reaction flask can be used for synthesis. For an alkaline-based precipitation, the change of magnetic properties of SPIONs during the nucleation and growth phase of the synthesis is demonstrated. The device is able to record the changes in the amplitude and phase spectra, and, in turn, the hysteresis. Hence, it is a powerful tool for an in-depth understanding of the nanoparticle formation dynamics during the synthesis process.Magnetic Particle Spectroscopy (MPS) is a measurement technique to determine the magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) in an oscillating magnetic field as applied in Magnetic Particle Imaging (MPI). State of the art MPS devices are solely capable of measuring the magnetization response of the SPIONs to an oscillatory magnetic excitation retrospectively, i.e. after the synthesis process. In this contribution, a novel  It is a quantitative imaging technique, which provides high sensitivity and can provide submillimeter spatial resolution3. The hardware realization for MPI is possible in a number of ways and is illustrated by Buzug et al.4. The properties of the magnetic particles strongly influence their usage for MPI or other applications. The quality of MPI images, for example, strongly depends on the anisotropy and size distribution of the SPIONs used as tracer material3. The nanoparticles used for the first experiments in MPI were predominately developed for magnetic resonance imaging (MRI) and are of limited use in the excitation fields of an MPI scanner. Moreover, magnetic nanoparticles have also been used in magnetic fluid hyperthermia (MFH)5. In MFH the nanoparticles serve as a medium for deposition of energy to a specific area using hysteresis losses6. Therefore, the hysteresis of the particles plays a critical role for most applications. Furthermore, SPIONs can be used for drug delivery7. In general, one can state that different fields of application require individually tailored nanoparticles. Actually, there is immense research to produce SPIONs, which are specifically tailored for the use in MPI as well as for MFH-based therapy9. These new types of SPIONs should have defined magnetic properties  as well as geometrical properties  to show optimal results in MPI. There are a lot of commercially available particles which have been characterized for MPI and compared according to their magnetic properties with the help of an MPS10. For example, nanoparticles with a core diameter of 20 nm and hydrodynamic diameter of 30 nm showed the best MPI performance so far11.Magnetic Particle Spectroscopy (MPS) is a technique to characterize superparamagnetic iron oxide nanoparticles (SPIONs) for their possible performance in magnetic particle imaging (MPI). MPI is a tomographic imaging modality measuring the spatial distribution of the SPIONs12. Due to this, the particles require more energy to get magnetized, which leads to the requirement of a higher field strength. Furthermore, the Brownian and Néel relaxation time increase with increasing size14.For MFH the hysteresis curve area should be large enough to deposit the required energy to cause cell ablation. For example, the larger the size of the nanoparticles, the steeper is the rise in the magnetization curve leading to the measurement of higher harmonics17. Moreover, the pH of the solution affects the susceptibility of the SPION solution as reported by Lucchini et al.18. These properties cannot be measured with the help of an MPS device, but they play an essential role in the imaging process as well as in therapeutic applications. It is worth mentioning that the change in temperature has a direct effect on the response of SPIONs in an MPS. This is due to the fact that the Brownian relaxation time (τB) and the Néel relaxation time (τN) are dependent on the thermal energy. Moreover, heating leads to a change in viscosity which also affects the Brownian relaxation time (τB)20.In addition to the magnetic properties, the geometrical properties such as size and shape of the nanoparticles are of major importance for the biodistribution and biocompatibility in biomedical applications23, developed for different MPI associated applications. Moreover, there are also new approaches to characterize the SPIONs such as a frequency-mixing method based on search coils, which measure the phase lag of the magnetic moment compared to the drive field, giving more precise information regarding saturation magnetization and average hydrodynamic size24. The measurements from MPS devices usually consist of amplitude and phase spectra. The amplitude spectra contain both the even and odd harmonics, odd harmonics are attributed to the particle signal and even harmonics are caused due to the presence of a DC offset magnetic field. These MPS devices are able to provide information regarding physical and geometrical particle properties after synthesis completion. Therefore, the synthesis process is largely unobserved and the nanoparticles produced are characterized after the completion of the synthesis process giving no information regarding the nucleation and growth of the SPIONs. Other methods such as X-ray scattering or transmission electron microscopy are being used for real-time monitoring of nanoparticles growth26, but these devices have a very tedious setup and cannot be used in a normal chemistry laboratory. Real-time transmission electron microscopy is a very advanced tool and provides optical and structural information in the nanometer to sub-Angstrom scale, but requires special in-situ chambers for carrying out the chemical reactions29. X-ray diffraction techniques do not provide any optical details but also require special experimentation chambers for observing different samples at higher temperatures and pressures31. Moreover, X-ray diffraction is very useful in studying mechanochemical reactions33 Contrarily, in-situ Magnetic Particle Spectrometer (INSPECT) does not require any special sample chambers or experimentation setups for conducting syntheses. Moreover, INSPECT is a low-cost device with low instrumentation complexity and does not require user expertise in comparison to X-ray diffraction and TEM. INSPECT directly gives access to the change in the magnetic moment during the synthesis, a parameter which is important for magnetic imaging (e.g. MPI) and magnetic thermo-therapeutic scenarios. However, one limitation of INSPECT is that it relies on the magnetic properties of the SPIONs and hence cannot be used with other nanostructures.There are a variety of techniques for measuring the magnetic properties of SPIONs such as vibrating sample magnetometry that measures the static magnetization curve and the AC magnetometry, which measures the magnetic susceptibility. Unfortunately, none of these are able to measure the spectral magnetic moment at the desired frequency and field strength used in an MPI scanner. On the other hand, there has been an immense progress in the development of MPS devices as, for instance, one-dimensional MPS without offset and with various excitation frequencies, two-dimensional Lissajous MPS, arbitrary-waveform spectrometer and many morein-situ Magnetic Particle Spectrometer (INSPECT) is introduced. INSPECT measures the magnetization response of the tracer material during a sinusoidal magnetic excitation in-situ. The INSPECT hardware is based on the same principles as typical MPS21, but contains less hardware components and is adapted for usage in a chemistry laboratory. In Theory, a brief description of MPS related magnetism physics and the particle nucleation and growth process are given. The dedicated hardware and software as well as the laboratory setup and basic chemical reactions are described in Materials and Methods. In Results and Discussion, initial results obtained by performing a synthesis process within the INSPECT reaction chamber are shown. A brief conclusion concludes this contribution.Synthesis research is a time-consuming and difficult task because there has been no real-time control of the synthesis process so far. The control of the synthesis parameters has been carried out in the case of practical blindness, as there was no insight into the current synthesis situation. The aim of this research is to develop a spectrometer for the real-time monitoring of the changes in the magnetic properties of the SPIONs during synthesis. To achieve this, a novel This section deals with the basics of field strength calculations in a solenoid and the induced signal in the receiving coil. Moreover, a brief introduction to the chemical nanoparticle synthesis process including nucleation and particle growth is given.L, number of windings N, the applied current I, μ0 is the permeability of vacuum and n the density of turns (turns per meter length of the solenoid) can be determined byThe magnetic field strength produced by a solenoid can be determined with the Biot-Savart law and Ampere’s law. In particular, the axial magnetic flux density near the center of the solenoid with length u(t) in a receiving coil due to a temporal change in the particle magnetization M can be calculated by using the reciprocity principle for magnetic recording34V is the sample volume, p0 is the spatial sensitivity of the coil, and x denotes the x-direction of the magnetic field. For simplicity, only a homogeneous generated magnetic field is considered. The average coil sensitivity p0 is given as p0(x) = Hr(x)/i0 and Hr(x) is the magnetic field generated by the receiving coil due to current i0. Using the above equation, the frequency spectrum of the magnetization 21f the frequency, T = 1/f0 and f0 the fundamental frequency.The induced voltage 35c is the concentration of the particles. The Langevin parameter ξ can be defined as kB is the Boltzmann constant and T is the absolute temperature. The Langevin model is rather simple and neglects the size distribution of particles. Therefore, there are different saturation magnetizations Ms of the particles in suspension. Hence, the magnetizations MD(t) have to be averaged. The resulting bulk magnetization ρ(D) is the probability density function of the particle size distribution. Using the bulk magnetization u(t) and the magnetic moment spectrum 21. But the Langevin model just provides a static model for the magnetic response of SPIONs. The Debye model corresponds to the magnetic response of SPIONs to a weak excitation field of frequency ω. According to the model, the real part of the magnetic susceptibility χ′(ω) monotonically decreases with the increase in frequency. Therefore, by using the value of ω it is possible to ascertain the mean value of Brownian relaxation time (τB) more precisely38.The non-linear magnetization curve of a SPION is typically modeled by the Langevin theory of paramagnetismGV, which depends on the concentration of the solute and can be described asc is the concentration of the solute, c0 is the equilibrium concentration or solubility, Ω is the atomic volume of the solid phase and σ is the supersaturation (when the solution contains higher volume of the solute which can be dissolved in a solvent) defined by (c − c0)/c0. If there is no supersaturation (σ = 0), then no nucleation will happen and hence no particle growth. For the stability of the nuclei, it is important that they reach a critical size or they would dissolve back into the solution causing a reduction of the Gibbs free energy39. After the attainment of a critical radius the growth process will continue without any barrier. To achieve monodispersity, it is important that no new nuclei are formed after the initiation of the growth process. Therefore, the ideal condition for synthesizing monodisperse SPIONs is the formation of the nuclei in a very short time interval subsequently leading to a coherent growth process, which is highly dependent on the kinetics and change of the surface energy and can be controlled by changing different parameters of the chemical reaction such as temperature and the amount of solutes and solvents39. In the case of the alkaline co-precipitation synthesis process, the nucleation is an unending phenomenon. This was observed by Baumgartner et al. with the help of real-time TEM. They reported that in such synthesis processes the nanostructures are formed due to the aggregation of the primary particles rather than atomic accretion, hence suggesting a secondary nucleation and growth mechanism40.Nucleation and growth of nanoparticles are a very complex multi-step process. Here a simplified model for the nucleation and growth is explained. The most important factor responsible for the nucleation and growth is a change in Gibbs free energy Δ23.In this section, the hardware and software realization of INSPECT is described. The diameter of the measurement chamber is 72 mm and can fit a 100 ml reaction flask for synthesis  is used41. The X3-25M features two 16-bit ADC  channels and two 16-bit DAC  channels. Moreover, the ADC has programmable input ranges and a bandwidth of 75 MHz. The DAC channels have an output of ±2V. One of the DAC channel is used for generating the transmitted signal for the transmitting coil. The resulting voltage across the transmitting coil is fed back to the I/O card to control the flux density inside the measurement chamber. The X3-25M generates the sinusoidal Tx signal at 23 kHz with a sampling frequency of 5.06 MSPS (mega samples per second), ensuring a high signal quality. Rx is also acquired with the X3-25M at a sampling rate of 5.06 MHz. Both signals are synchronized with the help of the timing unit present on the I/O card itself. The total measurement time for one measurement is about 1 s acquiring 10 periods with 2300 averages. The same measurement parameters are used for analyzing the sensitivity of INSPECT as well as for monitoring the synthesis process described in results and discussion.For the generation of the transmitted signal Tx signal generated by the I/O card has to be amplified by a power amplifier  to provide the necessary magnetic field of up to 10 mT42. This amplifier has a low total harmonic distortion (THD) of less than 0.1% at f0 = 23 kHz  with a maximum gain of 20. This reduces the need for analog filter stages, which are usually employed for the send chain in a conventional MPS setup. To ensure that the maximum power is transferred to the transmitting coil and to minimize the reflections between the AC power amplifier and the transmitting coil, a capacitive impedance matching is used. The circuit diagram for the impedance matching and the overall design is shown in Fig. The The transmitting coil is responsible for the generation of the required field to excite the SPIONs. The gradiometer coil cancels the transmitting signal out and provides the response of the nanoparticles for further analysis. The entire design of the transmitting coil and the gradiometer coil with the housing is shown in Fig. The transmitting coil generates a magnetic field and consists of a solenoid. The outer diameter of the transmitting coil is 121 mm and the inner diameter of the coil is 92 mm, which can easily fit a glass flask of 100 ml for synthesis. The height of the transmitting coil is 93.75 mm. The transmitting coil consists of 40 × 4 windings realized with 2000 × 5 μm Litz wire. The inductance of the coil is approximately 229 μH at 23 kHz (which is the excitation frequency of INSPECT). The air cooled transmitting coil is able to produce a field of up to 10 mT.22. Other techniques have been employed such as a dedicated cancellation unit21 and perpendicular sensing43. A comparison between some of these techniques can be found in Graeser et al.44. For the MPS presented here, a one-dimensional gradiometer coil is used, which is a receiving coil including a cancellation coil (with reverse winding). Gradiometer coils have been used in a number of fields like for constructing magnetometers45, in geology46 as well as in medical science47. For fine tuning, the gradiometer coil holder, as well as the flask holder . The printed material can withstand temperatures up to 80 °C. The cooling of the coils is realized by pressurized air. Through the cooling channel the air is channeled over the transmission as well as the receiving coil. For chemical reactions requiring higher temperatures such as organic synthesis, the housing can be manufactured with the help of high-temperature resins which can withstand temperatures of up to 238 °C. One such example of a material for additive manufacturing is from Formlabs  called High Temp Resin (FLHTAM02) offering a heat deflection temperature (HDT) of 238 °C at 0.45 MPaAs the signal could lose its fidelity while passing through the receiving chain or the sending chain, it is important to calibrate the system. As INSPECT has no filters or pre-amplifiers in either the sending chain or the receiving chain, the transfer functions of the chains are free of heating effects and can easily be determined.uTcal) made up of a copper wire and a voltage divider circuit is used. This coil is mounted on a cylinder of 51 mm in diameter with 21 windings of 0.40 mm pure copper wire. The transmitting calibration coil  detects the magnetic field generated by the transmitting coil. With the known geometry and the received voltage, the magnetic field can be easily calculated by using the Faraday’s law of induction. The applied magnetic field is given byBTx(t) is the generated magnetic flux density, f0 is the fundamental frequency of 23 kHz, A is the cross-section area, and N is the number of turns in the transmitting calibration coil. Using Eq. For calibration of the transmitting chain, a transmitting calibration coil  and iron (II)  plus Dextran T70  are placed in a round bottle flask inside INSPECT and ammonia as a base is dropped into the solution. The flow rate of the base is controlled with an infusion pump , which is set to 20 ml/h. After the addition of the base, the mixture is slowly heated to a temperature around 80 °C under ultrasonic control50, where the growth of SPIONs takes place.For the synthesis the iron salts, iron (III) (1.32 g of NH3) for the current research) is dropped controlled by an infusion pump at 20 ml/hr. After the addition of all the required base, the mixture is slowly heated to approx. 80 °C for 100 min. Measurements are taken every 5 min over the entire duration of the synthesis process. For determining the sensitivity of INSPECT a 10 μL Resovist sample is used.Before initiating the synthesis process, an empty measurement is taken, which is subtracted from the subsequent measurements to correct the background noise. The second measurement is taken after putting the iron salts and dextran and water in the flask. Then, 25 ml of base  are used. Resovist is a multicore particle with an iron oxide core consisting of multiple single crystals of approximately 4.2 nm in diameterFigure et al. secondary nucleation and growth mechanisms widely occur in the co-precipitation synthesis process40. Therefore, it is very difficult to estimate when the nucleation phase has completely ended and growth has started.This particular synthesis can be divided into three different intervals. The first interval consists of the nucleation process, in which, for a long period of time, there is no or negligible formation of superparamagnetic material. As stated by Baumgartner −7 Am2 to 1.99 × 10−5 Am2, which is approximately a factor of 37.4. This happens because the nuclei that had already formed in the nucleation phase reach a reasonable size to show a magnetic moment. In the last interval, there is a very slow growth and a small change by a factor of 1.45 in the magnetic moment from 1.99 × 10−5 Am2 to 2.887 × 10−5 Am2. At this point, the synthesis process is stopped as no further changes in the magnetic properties occur. For this particular synthesis, 46 measurements are taken in the total time span of approximately 225 min. In Fig. This interval ends with the initial particle formation, which shows a weak magnetic moment. In the second interval, a sudden growth of the nanoparticles occurs. In a span of 30 min the magnetic moment increases from 5.32 × 1052. Some of the measurements taken during this interval are shown in Fig. 53. Followed by measurements after addition of base, which leads to the initialization of the nucleation. However, there are no significant changes in the magnetic properties observed, as supersaturation occurs locally, at the position where the base is being dropped. However, after a few seconds of ultrasonic stirring, the base is mixed into the solution. As shown at 0 min, there is a negligible change in the amplitude of the signal received, but there is a prominent change in the phase and the shape of magnetization curve for the subsequent measurements at 50 min. This process continues for measurements till 50 min and 90 min, leading to phase changes and shifting of the magnetization curve. In the measurements at 50 min and 90 min, respectively, it can be seen that the magnetization curve is changing. Moreover, the amplitude of the third harmonic increases for the measurement at the time stamp at 50 min to measurement at 90 min. In the nucleation phase, the detection of SPIONs is challenging as the magnetic moments of the particles are small.In the nucleation phase, there is no visible change in the magnetic properties of the nanoparticles as it takes some time for the solution to reach the supersaturation state to start the nucleation process. The nucleation occurs only when the concentration of the growth species increases above the equilibrium concentration, i.e. the concentration of the base increases in the solution. The base is being dropped slowly, therefore it takes around 80 min for completion. The slow addition of the base is important, because a sudden increase in the concentration of the base may lead to the formation of different sizes of nuclei and hence, leads to a broader size distribution39. For alkaline based synthesis this is not the case. Figure 3.When the required quantity of the base is added to the solution, the heating is started. This leads to a decrease in the concentration of the growth species and the change in the volume Gibbs free energy decreases. Ideally, this should not lead to further nuclei formation and a coherent growth process continues until the concentration of growth species reaches the equilibrium concentrationThe next measurement is taken at 165 min. In the amplitude spectrum, the first nine odd harmonics can be observed and the phase spectrum shows linearity. The magnetization curve shows no saturation at field strengths of 10 mT. This situation changes in the following 10 min as shown in measurement at the time stamp at 175 min. There is a sudden growth of the particles, which significantly increases the magnetic moment. The magnetization curve also shows a saturation effect indicating larger particles and the phase spectrum is almost constant.The third interval is the growth phase, where the SPIONs show a linear growth behavior. The results are shown in Fig. in-situ MPS is presented, which is capable of tracking the growth of the nanoparticles in an ongoing synthesis process. The hardware realization is compact, consists of few hardware components, and is well suited for the chemical laboratory. The whole setup consists of a housing consisting of a transmitting coil and gradiometer coil. In addition, there is an impedance matching module and a power amplifier. There are no additional band pass or band stop filters for either the transmitting signal or the receiving signal. The excitation frequency can easily be changed by switching the capacitors in the impedance module. As the gradiometer coil is vertically movable, it can be adapted to receive the maximum signal and to cancel out the excitation signal.An 54. Ostwald ripening leads to dissolving of the smaller particles back to solution and a redeposition on the larger particles. On the other hand, digestive ripening leads to shirking of the larger particles leading to further growth of the smaller particles. These processes can cause changes in the Brownian relaxation and in turn affect the magnetic moment of the particles. With INSPECT we will be able to study these phenomena in more detail. In the sudden-growth interval, as the SPIONs become bigger in size, the spectral magnetic moment changes significantly. This phase ends in the growth interval, where particles grow slowly and finally due to no significant changes in the spectral magnetic moment, the synthesis process is stopped. INSPECT is able to monitor these changes and can be used to optimize the synthesis process, because it allows for an insight into the growth dynamics and reveals the direct effects of synthesis parameter tuning. This device provides a platform to study the dynamics of different synthesis processes to improve SPIONs for applications in both MRI and MPI.The measurements presented here have been acquired every 5 min, however, the sampling rate is adaptable to real-time measurements with increasing cooling effort. To demonstrate the usefulness of the device a complete alkaline precipitation based synthesis process of SPIONs has been monitored with INSPECT. A series of data sets have been acquired over a time span of 225 min. From the results it can be deduced that the synthesis process can be divided in three main intervals, i.e. the nucleation phase, the sudden growth phase, and the growth phase. In the nucleation phase there is no significant change in the amplitude spectrum. In the nucleation phase, there is a decrease in the coercivity for the measurement at time stamp 90 min to 55 min. As in the nucleation phase there are a lot of competing processes happening such as particle coarsening  and digestive ripening


An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system–environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space). We conduct simulations and experiments that involve standing balance control against disturbances with a two-degree-of-freedom inverted pendulum and bipedal walking control with humanoid robots. In the simulations and experiments on standing balance control, the pendulum can become upright after a disturbance by adjusting a few signals in MRM-space with tacit learning. In the simulations and experiments on bipedal walking control, the robots realize a wide variety of walking by manually adjusting a few signals in MRM-space. The results show that transferring the signals to an appropriate control space is the key process for reducing the complexity of the signals from the environment and achieving diverse behavior. Can robots be good neighbors? Despite much effort by many researchers to make robots be good partners, robotic systems remain limited to being merely useful tools in factories and housesWe think that an important function that is currently missing from robotic systems is a way to create behavior from symbolized information. For instance, when walking, we deliberately attend to symbolized behavioral purposes such as “walk faster” and “turn right” or more symbolic forms such as “go to the station.” The detailed control signals that create such motion, such as joint trajectories and muscle activations, are then generated automatically.It is said that these automatic control signals are created by the activities of local neural systems including the cerebellum and spinal cord. In our daily lives, we attend only to symbolized behavioral purposes that are highly specialized. The appropriate behavior and detailed control signals that achieve those purposes are then chosen according to the prevailing situation and the surrounding environment. If we could share such symbolized behavioral purposes with robots, and if the robots and we could create the appropriate behavior independently according to not only the surroundings but also the features of our respective functions, then we would feel that the robots are really our partners. Therefore, generating behavior from symbolized purpose could be an important way to assess the extent to which robots could be our partners with human-like behavior.In this paper, we discuss the process of creating behavior from symbolized behavioral purpose, focusing on creating motor commands from simple behavioral targets through body–environment interactions. There have been various attempts to clarify the mechanisms that generate automatic motor commands from symbolized purposes, an important approach being a physiological one. Recently, Takei et al.  reportedModel-based approaches provide the conceptual basis for the aforementioned physiological approach. A bow-tie structure . In MRM-space, the signals are used to control the mechanical resonance modes of the robot. This makes it easy to understand how the robot behavior changes when the control signal is changed in MRM-space. In sections 3 and 4, we propose an adaptation method in MRM-space using a two-degree-of-freedom (2DoF) inverted pendulum, 27DoF humanoid robot, and the NAO humanoid robotTransfer to MRM-space and behavior adaptation by tacit learning are the key analytical methods of the present study. Because both methods are discussed in detail elsewhere  of M−1K:where M ∈ R2 × 2 is the inertia matrix of the pendulum linearized around θ = 0 and K ∈ R2 × 2 is a stiffness matrix that has the spring coefficients of the joints on its main diagonal. The first mode (v1) corresponds to the smallest eigenvalue (λ1) and the second mode (v2) corresponds to the next-largest eigenvalue (λ2). The mode vector represents the shape of the pendulum oscillation.where θ of the pendulum can be represented as a superposition of the mode vectors as follows:The state variable w1, w2 represent the weights of each mode vector and T can be defined as a transfer matrix. We can define the weights of the mode vectors as symbolized state variables in MRM-space.where The adjustment of the symbolized state variables is reflected in the movement of individual joints by the transfer matrix. This is much like the top-down process in people, namely changing one's behavior by means of symbolized information without having to attend to the actions of individual joints.Tacit learning is an adaptive learning method inspired by two features of living beings. First, living beings can perform adaptive behavior globally even though control is realized by only a summation of local nerve-cell firings. Second, adaptive learning and behavioral control are calculated in parallel; this is unlike machine learning, whose calculation is divided into a learning phase and an action phase.To apply these features to artificial controller, action targets and the concept of “reflex” are used in tacit learning. The reflex plays a role in directing the movement of the controlled system toward a state in situations in which the system does not receive many environmental stimuli from a global perspective. By enhancing the reflex by accumulating reflex commands, the system can acquire a state autonomously through system–environment interactions, where there are fewer environmental stimuli without having to distinguish between the learning phase and the action phase.Other learning methods use behavioral functions or teaching signals to adjust the controlled system behavior and achieve adaptive behavior in a top-down manner. In that sense, tacit learning can be defined as a bottom-up learning process, adjusting the behavior through system–environment interactions. However, it can control the system to achieve adaptive behavior from a global perspective. Herein, we use tacit learning to develop a bio-mimetic adaptation process.In this section, we introduce the controller with tacit learning in MRM-space and apply it to standing balance control of a 2DoF inverted pendulum. We show that the tacit learning controller can maintain balance against larger disturbances than the case without learning.Figure θ is a 2 × 1 vector consisting of the joint angles, τ is a 2 × 1 torque vector that affects each joint, and M(θ) is a 2 × 2 inertia matrix given bywhere a1 = l1/2, a2 = l2/2, ξ = l1m2a2, and where g1 = (m1a1 + m2l1)g, g2 = m2a2g, and g = 9.81 m/s2.where T described in 4 and tacit learning. Terms θi and τi are the angle and torque, respectively, of joint i. The torque vector τ for each joint isFigure θ and Δθ and where θref is a reference for each joint:A and B are diagonal matrices:Terms kp1 and kp2 are proportional (P) gains of the proportional-derivative (PD) controller in MRM-space, kd1 and kd2 are derivative gains of the PD controller in MRM-space, and T−1 is the transfer matrix from joint space to MRM-space.where ζ is a vector that consists of the integration of τ as follows:Term k1 and k2 are the coefficients of the integrators that accumulate the joint torques and output the integrated values. These accumulations correspond to tacit learning, and these integrators adjust the individual joint torques and work to keep the pendulum upright after disturbance through pendulum–environment interaction, as in Shimoda et al.  and (8) as follows:v1, v2 of the pendulum defined in Figure The two mode vectors v1 represents same-phase posture and the second mode v2 represents anti-phase posture. The transfer matrix T isAs shown in Figures Standing balance control simulations are conducted as follows.The pendulum is placed upright on a board that can move horizontally.f [N].The board is moved for 0.2 s with the disturbance A simulation is ended once the height of the center of mass(CoM) of the pendulum falls below 0.2 m or the pendulum become upright.f = 170,… ,210 N. The gains are ref1 = θref2 = 0.0.We conduct simulations with each of Table Figure E per unit time, which is calculated fromFigure T is the time until the pendulum becomes upright. We also calculate E when the simulation is conducted with tacit learning in MRM-space by using different integral coefficients, namely −5.where km2 for tacit learning. However, E also increases with km2.The pendulum can become upright against a larger disturbance with learning in MRM-space than without learning in MRM-space. The size of disturbance that the pendulum can withstand without falling over increases with the integral coefficient We conducted an experiment on a real 2DoF inverted pendulum with the same block diagram as in the simulation. The transfer matrix was calculated based on the physical parameters of the pendulum, whereas the gains in the block diagram were determined by trial and error. The experimental conditions can be seen in the The pendulum can remain upright against larger disturbances as the coefficient of tacit learning in MRM-space is increased . We show through simulation and experiment that the signal added to the controller in MRM-space plays the role of behavioral intentions to change walking direction while maintaining walking balance.T and tacit learning. Terms θ, τ ∈ R27 for each joint is represented asFigure ζ is a vector that consists of the integral values of tacit learning, and tacit learning is applied to only the left and right ankle joints. The balance in the sagittal plane is maintained by tacit leaning, as in Shimoda et al.  Walking forward and backwardkp1 used to adjust the movement of the first mode. In the early stage of walking, the P gain is set as kp1 = 270.0. It can be seen that the robot walks forward and backward according to the adjustment of the P gain. An overview of the simulation can be seen in the Figure (ii) Turning left and rightkp1 to walk forward. The robot can be seen turning left.Figure kp1. From Figure Figure 8 = 4.5e3. From Figure kp1 is a key factor that affects the velocity in turning behaviors, and the walking direction is changed.Figure T.We conducted not only simulations but also walking experiments by using an actual robot, namely the humanoid robot NAO made by Aldebaran. This has 25DoF, and we can control the angle of each of its joints. NAO is controlled by angle inputs rather than torque inputs. We designed a control structure that generates reference joint angles by using the transfer matrix The reference joint angle for a wide variety of walking can be described asϕ ∈ R25 is a vector that consists of joint-angle references for walking, and ϕ′ ∈ R25 is an adjusted vector. Term T† is a transfer matrix modified from the transfer matrix T to fit the DoF of NAO. Term kp1 works like the P gain in the simulation of walking forward and backward. Term α8 works like the constant value in the simulation of turning. Walking balance is acquired by applying tacit learning to joint space in the same way as 2DoF inverted pendulum postural control.where Experiments are conducted using the same scheme as that shown in Figure (i) Walking forward and backwardNAO can walk forward and backward when we adjust only the movement of the first mode by adjusting its gain. An overview of the experiment can be seen in the (ii) Turning right and leftkp1 to walk forward. Figures Figure 8 = −25.0, whereupon NAO turns left. The converse holds in Figure NAO can turn left and right by adjusting the movement of the eighth mode, as in the simulation. As shown in Figure The robot NAO could turn left and right by adjusting the movement of the eighth mode, that is, adjusting the bending of the robot and NAO at the waist on the frontal plane by adjusting the constant value. Adjusting the movement of the eighth mode deflected the CoM to the left-hand or right-hand side of the body, whereupon one foot took a larger step than did the other foot. This phenomenon can be confirmed from the fact that the amplitude of the left knee joint angle was bigger than that of the right knee joint angle (see Figure The robot and NAO could walk forward and backward by adjusting the movement of the first mode. The robot and NAO differed in how the gain was adjusted, but we consider this to be because the gains of the other modes differed between the robot and NAO. When the movement of the first mode is adjusted, the degree to which the legs are opened is adjusted, whereupon the position at which the foot touches the ground can be adjusted. This can be confirmed from the fact that the hip joint angle decreases and the knee joint angle increases when walking switches from forward to backward (see Figure It is normally difficult to make a robot walk in a wide variety of ways because that necessitates designing a plurality of controllers and preparing references concerning each combination of individual joints. Instead, our method realizes turning and walking forward and backward smoothly by adjusting a few symbolized parameters in MRM-space without having to care about each combination of 27 joints and switching controllers. This is because there is a pattern of movements according to the mode, and the pattern to adjust is easy to understand visually.A person can change behavior while caring intentionally only about “turn left and right” or “forward and backward.” Likewise, our method can adjust signals and realize behavior without caring about each joint, which is important in considering human-like movement in the robot under consideration.As discussed in section 1, the aim of this paper is to clarify the mechanisms of generating automatic motor commands to achieve a symbolized behavioral purpose. Our approach is to develop an artificial controller that embodies those mechanisms and derive the important features of that function to assess the extent to which the robot behavior is human-like.We reasoned that the key problem in developing a controller with those mechanisms would be realizing bio-mimetic adaptation with two-way behavioral adaptation. The first way is selecting the appropriate behavior that progresses in a top-down manner, and the second is adjusting the behavior according to the environment, which is a bottom-up process that progresses through body–environment interactions.In the preliminary study, we discussed the standing balance control of a 2DoF inverted pendulum to introduce our control strategies, focusing only on the bottom-up adaptation process. In our control strategy, the control signals to each joint are transferred to another space computed by using the mechanical resonance modes, whereby we can easily understand the behavioral pattern that each control signal creates. We applied tacit learning in MRM-space and developed the controller to maintain standing against various levels of disturbance. The simulation and experimental results showed that the standing balance control capability was increased when the 2DoF inverted pendulum was controlled using MRM-space, suggesting that the simple adaptation mechanism is enough to improve the behavioral performance of standing balance when behavior control is conducted in a space where we can set the direction of behavior by adjusting to the change of the disturbance. The similarities between the mechanical resonance modes used in this system and the hip and ankle motion strategies of people must be another indication of the importance of reacting to a wide range of disturbances in a space in which the body parameters are well represented.In the bipedal walking study using NAO, we discussed two-way adaptation. To represent the top-down process in which the behavioral purpose is selected, we chose the appropriate resonance mode with which to adjust the behavior to the desired one. As described in Figure We consider that the signals added to the specified mode control can be treated as symbolized behavioral purpose in our study. The detailed commands to the joint control were created in two processes, namely, transferring the control signal from MRM-space to joint space, and tacit learning for maintaining walking balance while reacting to environmental inputs. Therefore, these results suggest that one-dimensional signal change can create the complicated combinations of the 25DoF of NAO required to change the behavior when the appropriate mode is stimulated.Joint control is much more complicated in people than it is in robots because complicated combinations of muscles are required in the former. The notion of muscle synergy introduced in section 1 shares the same features as those of the mechanical resonance mode used in our method for robot control because the muscle-synergy space represents the behavioral features of body mechanisms and contributes to adjusting behavior in our case by using lower-dimensional signals.These results and discussion suggest that transferring the signals to the appropriate control space is the key process for reducing the complexity of the signals from the environment. Discussing muscle synergy in a human controller and the mechanical resonance mode is only one step to the final output. If such steps were to be accumulated to form a larger network as described in Figure We reason that the extent to which a symbolized target that is represented by the lower-dimensional signals is used to create the robot behavior is the critical assessment for evaluating the extent to which the robot behavior is human-like. The results in this paper are just one step up from pure motor-control signals, implying far from human-like behavior. Further discussion is required to elevate the proposed system to using more-symbolized behavioral targets such as “go to the station.” A key problem is automatic creation of the mechanical resonance mode. Non-linear transfer for more complicated environment is another important problem. Even in the control of human behavior, the process of creating muscle synergy remains mysterious. We are now on the way to clarifying the process to a morehierarchical system in both physiological and artificial ways.MT explained the concept of mechanical resonance mode of the robot to SO. MT made a model of robot control structure using the mechanical resonance mode. MH and SS explained the concept of a biological learning method called tacit learning and gave advices on how to use tacit learning to SO. To clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior, SO made the concept of adaptive control method using mechanical resonance mode and a biological learning method called tacit learning, designed a control structure to realize symbolized behavior purpose in discussions with FA, MH, YH, MT, and SS. SO conducted standing balance control simulations and bipedal walking control simulations with a 2DoF inverted pendulum and 27DoF humanoid robot, respectively. In discussions with FA, MH, YH, and SS, SO conducted standing balance control experiments and bipedal walking control experiments with an actual 2DoF inverted pendulum and an actual humanoid robot, respectively. SS helped in using the actual pendulum and humanoid robot in experiments.The 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.


Over the past decades many innovations were introduced in total knee arthroplasty (TKA) focusing on implant longevity and higher procedural precision; however, there are still a high number of dissatisfied patients. It was reported that better anatomical alignment may result in improved patient outcome; however, current technologies have limitations to achieve this. The aim of this video article is to describe the technique of individualized alignment in TKA with the use of image-based robotic assistance.The technology is based on an individual patient knee model computed from segmented computed tomography (CT) scans. A preoperative planning of prosthesis position is conducted following the principle of kinematic alignment. Intraoperatively the soft tissue envelope is recorded and the computer predicts the gap balance based on the virtual planning. The prosthesis position is then adapted to achieve balanced gaps and to avoid soft tissue release. This technique is shown in a cadaver operation and clinical examples of two patients are described.With the combination of anatomically oriented prosthesis positioning and minor adaptations with respect to the soft tissue, an individualized alignment is achieved with reduced need of soft tissue release. The robotic-assisted surgery guarantees a precise implementation of the planning. The initial experience showed a promising outcome in short-term follow-up.http://www.springermedizin.de/der-orthopaede. The video can be found at the end of the article as supplementary material.The online version of this article (10.1007/s00132-018-3637-1) contains a video on patient individualized alignment in total knee arthroplasty. The article and video are available in the electronic full text archive at SpringerMedizin.de under  There is currently a widespread consensus that the optimal alignment in total knee arthroplasty (TKA) is with reference to the mechanical axis of the limb, correcting any deformities to neutral and defining component position with respect to a theoretical mechanical ideal; however, it has been shown that a neutral alignment is physiological only for a minority of patients with the average having a constitutional varus alignment by 1.2° . There iFollowing the idea of mechanical alignment with the established standard surgical techniques and prosthetic designs, there are still a high number of dissatisfied patients after TKA . RecentlComputer-assisted surgery was introduced into hospitals in the late 1990s in the form of passive navigation systems . Today, There are therefore some reasonable arguments to follow the idea of a patient individual alignment in TKA; however, the solely anatomical approach in KA lacks the inclusion of the individual soft tissue situation, plus a high accuracy is required to conduct this virtual hybrid plan precisely during surgery. A technology combining these characteristics is the image-based semi-automated robotic-assisted surgery systems that were very recently introduced for TKA (2016). The focus of this video article is to describe the general concept of the hybrid technique to achieve patient individualized alignment in TKA on a cadaveric example. Additionally, two clinical cases are described and initial clinical evidence is discussed.Both cadaveric and clinical cases described here were operated on using the semi-automated haptic Stryker MAKO® robotic system  with the TKA application together with the Stryker Triathlon® knee.Prior to surgery a computed tomography (CT) scan of the hip, knee and ankle is performed according to the manufacturer’s standard protocol. This is uploaded to the proprietary software platform and segmented to an individual three-dimensional model of the knee. Based on this, an initial preoperative planning is conducted following the principle of KA for restoring the original natural femoral surface. This means that the distal and posterior femoral resection levels are set to 7 mm bone resection independently from the resulting alignment angle. Therefore, the physiological valgus of the distal femur is restored as well as the rotational alignment with reference to the posterior condylar line (PCL). As it is a CT-based planning, cartilage thickness was calculated to 1 mm, taking the elasticity of the cartilage and meniscus into account. If necessary, femoral size is adapted to best fit the sagittal profile (anterior offset and condyle radii) and flexion of the distal femur. If flexion of the component is changed to avoid notching or anterior lift-off, the pivot point is set to the transepicondylar axis (TEA) so as not to affect the mid-flexion stability. The only limitation in sizing is not to produce a mediolateral overhang.The tibial varus-valgus alignment is pre-set to a tangent to the medial and lateral tibial plateau. A varus angulation of a maximum of 4° is allowed. The rotational alignment is kept to the anteroposterior axis of the plateau. The tibial slope is orientated to the natural slope and reduced by 2–3° to compensate for the resection of the ACL. If the slope is changed the pivot point is set to the posterior third of the plateau, where the ideal biomechanical contact point of the prosthesis is meant to be, to minimize the effect on the flexion stability of the knee. The tibial resection level is pre-set to a maximum of 7 mm laterally or 5 mm medially. A clinical example of a preplanning is displayed in Fig. The robotic system is set up and calibrated according to the standard protocol. Tibial and femoral trackers are fixed to the bones and a standard parapatellar incision to the knee is carried out that follows the bone registration according to the standard protocol capturing 40 random points on the surface of each bone. As it is an image-based system, bone registration is made with a sharp probe to guarantee bone contact but capturing the cartilage surface or soft tissue coverage. The required overall accuracy of the bone registration is defined below 0.5 mm deviation. Osteophytes are then removed and the ACL is sacrificed.Next is the recording of the soft tissue situation. With the use of lollipop spacer blocks according to unilateral knee arthroplasty, the collateral ligaments are broad to tension trying to replicate the physiological length of the ligaments with no overstrain. In the case of unilateral osteoarthritis or in mild wear situations a manual varus-valgus stress is applied. The resulting gaps are captured at full extension and 90° flexion and measured in millimeters.The prosthesis position is adjusted to achieve symmetrically balanced gaps and to avoid or minimize soft tissue release as described in more detail below. The target for the optimal gap balance is a perfectly symmetrical extension gap at 18–19 mm, depending on the individual laxity . In the flexion gap the target is to have the same balance and gap width in the medial compartment, whereas a physiological lateral laxity up to 21 mm is acceptable. Also, it is acceptable if the flexion gap is 1 mm tighter than the extension because usually it is technically more difficult to correctly record the flexion gap, tending to capture it tighter than it actually is.The first parameter to address for balancing is always the tibial cut because the basic philosophy of individualized alignment is to closely restore the femoral anatomy. Thus, distal and posterior femoral resections are critical for adaptations because alterations in these parameters massively affect knee kinematics. If a gap asymmetry requires adaptation , the femoral cuts are limited to a maximum of 9 mm (maximum 1 mm difference to prosthesis thickness). An alternative target for imbalanced gaps is the tibial slope; however, the physiological slope should not be exceed. If there would be still mismatch between the flexion and extension gaps, for example, the distal femoral resection is reduced first, instead of increasing the posterior cut thickness. If the resulting gaps are then to small, the tibial resection level is lowered. In varus cases the varus-valgus orientation or rotation of the femur is adapted for balancing the gaps with currently a resulting overall varus alignment up to 3–4° is acceptable. If there is still an imbalance present and the resulting alignment would be beyond 4° off the mechanical axis, a controlled soft tissue release is carried out until a symmetrical extension gap is achieved. This is again recorded into the system and after that the flexion gap is adapted via changing the femoral rotation. In Fig. When the prosthetic position is optimized to achieve the desired gap balance and alignment, the resulting hybrid virtual plan is conducted by the use of the haptic robotic-assisted system. The robotic system defines the cutting plane and only allows the surgeon to move the saw within the given boundaries see Fig. . When thFollowing the robotic-assisted execution of the cuts the prosthesis is implanted manually.As examples two consecutive patients operated on with the robotic-assisted technology are included in this article. Both patients are enrolled in an ongoing prospective randomized controlled trial comparing robotic-assisted TKA with individualized alignment versus conventional manual technique. They gave informed consent to being enrolled in this clinical trial. The study protocol was approved by the local ethics committee. The study is registered in the German Clinical Trial Register under DRKS00012390.Inclusion criteria are osteoarthritis of the knee with the indications for TKA and a University of California Los Angeles (UCLA) activity scale of more than 4 points. Exclusion criteria are defined as body mass index (BMI) >40, history of infection, osteotomy, or osteosynthesis around the affected knee, chronic rheumatic disease, neurological diseases affecting mobility of the patient or an American Society of Anaesthesiologists (ASA) risk classification of 3 or more. For outcome measurement, the standard 2011 Knee Society score (KSS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Oxford knee score (OKS) are collected the day before surgery and at 3 months postoperatively. Major and minor complications or revision surgeries during the follow-up are also recorded. On preoperative and postoperative (5–7 days) long leg standing radiograms standard mechanical tibiofemoral angle (OLA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA) are determined.The video demonstrates that a purely anatomical position of the prosthetic components might not automatically lead to perfect soft tissue balance. Also, with different cartilage thickness for example in the medial and lateral compartment, the overall limb alignment is off the physiological orientation, when only a bone scan is taken into account. Therefore, minor adaptations of the prosthesis by approximately 1–2 mm are necessary to perfectly balance the gaps. The cadaver example shown in the video demonstrates that not only these adaptations have great effects on the soft tissue balance but also the center of rotation about which the positional change is made. If the pivot point is in the center of the knee both lateral and medial compartments are affected. If the pivot point is shifted to the already balanced medial compartment, for example, changes of the angulation only influence the contralateral compartment.The two individual patients displayed in more detail were 67 and 58 years of age and both presented a high activity level of 7 at the time of surgery. The preoperative alignment, the targeted alignment after adaptation to soft tissue, and the final postoperative radiographic outcome are displayed in Table Regarding the clinical outcome the greatest improvement was seen in the WOMAC starting at 53 and 20 points for the two patients and going down to 5 and 3 points, respectively . Interestingly, both patients reported that the pain level and the activity level were much better than expected at 3 months, and one patient already returned to work within 3 months follow-up. As in other clinical studies the postoperative pain level was relatively low, potentially due to the soft tissue protection with the use of the robotic device.This study reports on the initial experience and outcome of image-based robotic-assisted TKA aiming for patient individualized alignment. The majority of knees that were operated on based on this algorithm were aligned within the accepted range of 3° to the mechanical axis; however, individualized component position is implemented in every case with promising clinical results.In the varus patients the basic principle of kinematic alignment is followed for restoring the articular surface of the knee with the prosthetic components. This means a purely symmetrically measured resection on the femur according to the thickness of the implant and a corresponding anatomical resection of the tibia plateau; however, KA is often criticized for this purely anatomical approach. First, the resulting soft tissue balance is only a theoretical assumption and later adaptions of the prosthetic position are difficult to control with respect to overall alignment. The great benefit of the presented robotic system is the inclusion of the soft tissue situation into the virtual planning. Thus, adaptions with respect to the soft tissue can be made, while having the effect on the overall alignment visible. The maximum overall varus alignment in the algorithm has been set to 3–4°, as this is still the accepted range. On the tibial cut a maximum of 4° tibial varus is currently accepted. This is based on several papers outlining that larger deviations may lead to a significant increase of the medial compartment load on the tibia with the potential risk of early implant failure , 26, 28.Third, a potential internal rotation of the femur is sometimes criticized even in the kinematic concept, as the femoral rotation is set to the PCL. This may result in a relative internal rotation to the TEA, as seen in patient No. 2; however, in our experience this does not automatically mean an internal rotation to the patient’s individual trochlear orientation and can be visualized on the CT model-based virtual planning. As the femoral rotation is not a major parameter to balance the knee in this algorithm, the necessity to set a limitation on the rotation has not been seen. An internal rotation relative to the native trochlea should potentially be avoided, as most current knee designs have the concept of external rotation implemented in the trochlear geometry.In summary, in all knees that were initially planned for KA minor adaptions were necessary to achieve the set goal of symmetrically balanced gaps and to account for general biomechanical principles in TKA. Regarding patient outcomes, statements must be made very carefully. This is because of the unrepresentative sample group as well as the still limited overall experience and short follow-up period. Up to now, there is only one publication available comparing the short-term clinical outcome (6 months) of robotic-assisted versus manual TKA of this specific system. As a result, a significantly better outcome in short-term pain, physical function and total satisfaction was seen in favor of the robotic group . A recenThe robotic technology introduced here means relevant technical overheads and additional costs that need to be put into the context of competing approaches. Compared to traditional navigation the major disadvantage is the higher costs for the system, technical support and a longer set-up time prior to surgery. Also, the necessity of a preoperative CT scan results in extra cost; however, the CT-based bone model utilizes a higher precision in the planning and intraoperative navigation and in addition, the robot means an active real-time control on the definite cuts whereas in navigation these are only passively checked after the cuts have been made and the robot enables active soft tissue protection that has also been shown in in vitro studies. Compared to patient-specific instruments (PSI templates) there is the same need for a preoperative CT scan. In theory the same planning algorithms can be conducted as introduced in this article; however, in the PSI technology there is no information on the soft tissue and no virtual intraoperative adaptations can be made. Moreover, the segmentation and prosthesis planning is made on side in the hospital and thus can be much better controlled by the surgeon than in PSI.The intraoperative time is comparable to the manual standard procedure or navigation, where most time is needed for the navigation part and the virtual planning. Once the plan is approved the time for cutting the bones with the robotic device is less compared to conventional instrumentation as no cutting guides are needed. A precise cost and time analysis comparing these approaches is currently being carried out in a multicenter study.Based on initial experiences with image-based robot-assisted TKA this technology is judged to enable the surgeon to overcome the limitations associated with standard mechanical alignment in a controlled and objective manner. With the combination of an anatomical approach to preposition the components and the adaptation of this planning with respect to soft tissue tension, it is possible to create an individualized, optimized alignment with symmetrical gap balance and a reduced need for soft tissue release. The current clinical results, in this study as well as the literature, are promising but only a very short follow-up is currently available. Further studies and longer follow-up are necessary. Critical success factors are the underlying algorithms to balance and adjust the components that are discussed in detail here. This is the starting point for further evaluation and optimization.Video 1 Patient individualized alignment in total knee arthroplasty using image-based robotic assistance