Traditionally, neutron scattering is an essential method for the analysis of spin structures and spin excitations in bulk materials. Over the last 30 years, polarized neutron scattering in terms of reflectometry has also contributed largely to the analysis of magnetic thin films and magnetic multilayers. More recently it has been shown that polarized neutron reflectivity is, in addition, a suitable tool for the study of thin films laterally patterned with magnetic stripes or islands. We provide a brief overview of the fundamental properties of polarized neutron reflectivity, considering different domain states, domain fluctuations, and different domain sizes with respect to the neutron coherence volume. The discussion is exemplified by a set of simulated reflectivities assuming either complete polarization and polarization analysis, or a reduced form of polarized neutron reflectivity without polarization analysis. Furthermore, we emphasize the importance of the neutron coherence volume for the interpretation of specular and off-specular intensity maps, in particular when studying laterally non-homogeneous magnetic films. Finally, experimental results, fits, and simulations are shown for specular and off-specular scattering from a magnetic film that has been lithographically patterned into a periodic stripe array. These experiments demonstrate the different and mutually complementary information that can be gained when orienting the stripe array parallel or perpendicular to the scattering plane. Nanomagnetism is a key field for fundamental studies and technical applications of magnetic materials on the nanometer scale. In particular, in spintronics and magnonics, nanomagnetic systems are widely used as sensors, for logic circuits, and for data storage ,2,3,4. NThe main aim of this contribution is a discussion of polarized neutron reflectivity (PNR) on flat magnetic thin films as well as off-specular scattering (OSS) on thin films patterned into a periodic array of micro-stripes. We start by recalling some basic properties of neutron reflectivity on flat magnetic films. Some earlier reviews of the theoretical background of PNR can be found, for instance, in Ref. ,6. MoreoY-direction and perpendicular to the Z-direction.In the most common experimental geometry shown in Q by flipping the direction of the polarization of the incident beam and by analyzing the polarization of the reflected beam. Here Q is the modulus of the scattering vector Z-direction, and N is the nuclear number density. For a homogeneously magnetized sample with magnetization vector tilted on the angle Y-axis and assuming ideal neutron polarization, the non-spin-flip (NSF) reflectivities are defined by the equations [With this set-up, polarized neutron reflectometry allows to determine four specular reflectivities quations :(1)R++=1B [C is expressed byHere uction B :(3)Nbm=CHere, Y-axis and the sample magnetization vector Y-axis, while the SF reflectivities are due to the component Y-axis.In contrast, the reflectivities in Equations  and 2)  2) depenX-axis (Z-axis (perpendicular anisotropy). In the latter case, the magnetic induction does not cause any reflection contrast at the surface of homogeneously magnetized film, and therefore PNR is not sensitive to the magnetization. If, alternatively, the magnetization vector Y axis, then From Equation  it folloIn accordance with Equation , the dif−R−−=|r+|−|r−|2cosIn case of domains, Equations –5) are  are 5) aTaking into account that independent of the magnetization direction, as is illustrated in It is also important to note that the averaging procedure over large domains does not affect the reflection amplitudes quations –5) are  are r±, Equation . TherefoY, (b) tilted to the angle Y axis. All reflectivities are shown on a linear scale for better visibility of the behavior close to the critical edges, instead of the more common logarithmic scale.To exemplify our discussion so far, in In quations  and 7)..R−− steeY-axis totally independent of the angle If, on the other hand, the magnetic domain sizes turn out to be much smaller than the coherence volume and the latter comprises a number of domains, then two prominent effects emerge. The first and the most distinct one is off-specular scattering (OSS) , which aFor domains smaller than the coherence volume, the reduction of the magnetic SLD also affects the positions of the critical edges Equation  the mSLDIn neutron reflectometry the coherence area, which is the intersectional area of the coherence volume with the sample surface, usually covers only a very small fraction of the sample surface illuminated by the neutron beam, as illustrated in quations  and 2),,M¯ is thY-axis, but tilted by the angle If the vector quations  and 2)  M¯=|M¯|cWhen, alternatively, the angle quations  and 2)  γ is notThe PNR mode discussed so far is referred to as full one-dimensional (1D) analysis of the neutron polarization, which yields the most complete information for flat samples with in-plane magnetization. In the reduced mode of PNR, the incident neutron beam can be polarized but polarization analysis of the reflected beam is not available. Then the reflected beam is a sum of both contributions NSF and SF reflectivity and therefore only two reflectivities, Equation , while aEquation  is missiFor the trivial case of a homogeneously magnetized sample in a single domain state, both modes of PNR are equivalent and yield the same information as both contain the same set of parameters. In either case, the main information to be extracted is the SLDs Even in the reduced mode of PNR, one may clearly discriminate between a magnetization that is averaged over small domains and a magnetization averaged over dimensions larger than the coherence length. In the first case, the positions of the critical edges for total reflection are affected, while in the second case the critical edges are unaffected while the intensities in the plateau region are different for both spin components.For illustrating the reduced reflectivity mode, we have calculated reflectivity curves in In unpolarized neutron reflectometry is able to measure the mean magnetization averaged over small domains delivering information that is hardly accessible with bulk magnetometry, and different of that provided either by magneto-optic Kerr effect (MOKE), or Lorentz microscopy and magnetic force microscopy (MFM). Indeed, for an unpolarized neutron beam the reflection coefficient Even Y-axis characterized by the parameter As already mentioned, using unpolarized neutron reflectivity we cannot determine the magnetization tilt angle It is worth mentioning that PNR is usually considered as a unique method to be applied to record magnetization depth profiles in e.g., multilayers. This is of particular interest in case of a complex noncollinear depth distribution of a 3D magnetization vector. Despite of appreciable progress in this direction, currently, only 1D polarization analysis is experimentally established, but obviously not sufficient to reliably recover depth profiles of all three components of the magnetization vector. Here we do not discuss advantages of the vector  polarization analysis techniques and just mention that theoretical tools for description of such experiments are available .Y-direction shows three salient features, as illustrated in A flat intensity plateau due to total reflection for wave-vectors Equation .Y-axis. In contrast, the angle If the sample is homogeneously magnetized or decomposed into a set of large magnetic domains then the critical edges, which separate the total reflection region from the rest of reflectivity, are distinctly different for up and down polarized neutrons. The difference In case of domains smaller than the coherence area the difference between For For a full treatment of the theory of neutron reflectometry we refer to the reviews listed in Refs. ,16,19. IIn what follows, we will focus on magnetic thin films broken down into periodic stripe arrays in the lateral direction. But before going into further details, the concept of the coherence volume for neutron reflectivity and its application to stripe arrays will be emphasized, which is essential for interpreting experimental results of patterned samples.Z-X plane, the short side of the entrance slit is oriented along the Z-direction as seen in Z-Y plane and the pixel position on the PSD defines the exit  angle Y-direction enhances the intensity, which is often additionally enhanced by focusing the incident beam onto the sample surface. By geometry, the angular resolutions (uncertainties) are drastically different for both directions: amplitudes are added up and constructive/destructive inference takes places. Beyond the coherence volume, reflected intensities from different coherence volumes are added up to yield the total reflected intensity from a sample surface. The coherence ellipsoid is defined by the uncertainties Most modern neutron reflectometers, steady state or time-of-flight, are equipped with a rectangular narrow entrance slit for the incident beam and with a position sensitive detector (PSD) measuring the exit beams. Assuming that the scattering plane is the horizontal eriments .Q and can be estimated as:Coherence properties are usually quantified by the coherence ellipsoid reciprocal to the resolution ellipsoid with principal axes Under experimental conditions, the coherence length The shortest axis of the coherence ellipsoid is determined by the uncertainty The length of the third axis is determined by the uncertainty n numerates the order of Bragg reflection in the lateral direction, and d is the stripe array period of a few micrometers.For orientation of the micro-stripes perpendicular to the scattering plane, as depicted in At a given shallow incident angle H applied parallel to the Y-axis is high enough to saturate the magnetization within the stripes for the parallel and the perpendicular orientation. As the field H is homogeneous along the Y-axis, it does not cause scattering contrast. Therefore we conclude that for perpendicular orientation of the stripes, the mean SLD value is given by amplitudesNow we consider the case that an external field inter-space [Thus, in perpendicular orientation, specular reflection and Bragg diffraction at shallow angles provides information on width and periodicity of stripe arrays and on magnetic induction within the stripes. In this orientation OSS is, however, less sensitive to the possible inhomogeneity of magnetic flux in stripes with not ideal edges and, especially, in the er-space . TherefoIn parallel orientation, the stripe pattern is rotated by 90This relation holds independent on whether the magnetization of the stripes is in a saturated state parallel or perpendicular to the stripes. But the magnetization orientation will affect the proportion between NSF and SF specular reflectivity and off-specular scattering. If, on the other hand, the magnetization is not constant and split up into domains, in addition off-specular diffuse scattering will be observed that yields information on the domain size and distribution, as already discussed for homogeneous flat magnetic films.inter-space region.Summarizing, both orientations of stripe arrays, parallel and perpendicular to the scattering plane, are needed for a complete analysis of the stripe patterns. The perpendicular orientation yields information on the geometry of the stripe array, including stripe width and periodicity, whereas the parallel orientation adds information on the magnetic flux distribution in e.g., the In the following we present PNR and OSS measurements performed on a magnetic stripe array. The sample with the size of 8 × 12 mmPNR and OSS experiments were performed using the Super-ADAM polarized neutron reflectometer  situatedanalyzer .As introduced above, two complementary sample orientations with respect to the stripes were probed. An external field of 5.2 kOe was applied perpendicular to the scattering plane along the stripes for the perpendicular orientation of the stripes and across the stripes for the parallel orientation with respect to the scattering plane. In both orientations the applied field exceeded the saturation field. In addition, we present also results for the parallel orientation with varying fields from remanence to the saturated state.The specular NSF reflectivity curves In the maps shown in panel (b) one can clearly distinguish the off-specular Bragg bands up to third order embracing the specular ridge from both sides as expected due to Equations  and 17)17). NoteThe overlap of OSS and specular reflectivity usually causes a phase shift of the Kiessig fringes. In the present case, the effect is not dramatic and barely seen. In any case, theoretical models for the reflectivity fit cannot describe the phase shifted Kiessig fringes. Reliable results can be obtained only when data in the region Z-direction, while further fitting of the intensity distribution over angles w and the array period d.The simulated maps in The maps in panel (b) show the specular ridge and negligible diffuse scattering, but no trace of Bragg bands. This is expected and accounted for by a coherence ellipsoid precisely oriented parallel to the stripe array with a coherence length X coordinate and quite homogeneously distributed with no appreciable random gradients in the X direction along the stripes and the incident beam. The presence of such a homogeneous field immediately follows from the fit of the specular reflectivity to the incoherent sum of reflectivities from stripes and from inter-stripe regions, where magnetic induction between the stripes is determined to be almost equal to that of the saturated stripes. Field gradients in two orthogonal Z and Y directions cannot cause off-specular scattering in this sample orientation. Hence the absence of off-specular diffuse scattering means that the system is translational invariant with respect to its shift along the stripes (X-axis) over a distance smaller than, or comparable with the long axis of the coherence ellipsoid. This is possible if edge roughnesses of stripes and, hence, inter-stripe field inhomogeneities are both absent. Or, alternatively, the edge roughness correlation length in the X-direction is larger than the coherence length According to these maps one can conclude that this sample does not show any detectable off-specular diffuse scattering, which otherwise might originate from fluctuations of the magnetic induction between stripes . This coY-direction and stripes either parallel, or perpendicular to the same axis. The orientation of the stripes affects the presence/absence of Bragg-bands, while SF scattering is absent in both cases. For the occurrence of SF scattering, a magnetic induction component parallel to the X-axis is required. But the application of an external magnetic field along the X-direction is not possible, as this would depolarize the neutron beam, the initial polarization of which is maintained by a guiding field of minimum 5 Oe applied along the Y-axis. One way out is the use of the high aspect ratio of the long stripes combined with the large domain size in Py films. Once magnetized in a vertical saturating field parallel to the the stripe axis, the magnetization is maintained even when this field is reduced down to small fields sufficient to preserve the neutron polarization, while the sample is then rotated by 90So far we have discussed two orientations of the stripe array in a saturation magnetic field of 5.2 kOe parallel to the Polarized neutron reflectivity on magnetic thin films is well described and often practiced to extract the nuclear and magnetic scattering length density profile normal to the film plane. This includes information on the magnetization profile across interfaces, mean magnetization vector in the film plane, and magnetic domain distribution (dispersion) for varying external magnetic fields. In the case of periodic multilayers stacked in the direction normal to the film plane, with PNR methods, one can distinguish between ferromagnetic, antiferromagnetic or non-collinear correlation between consecutive magnetic layers. Off-specular diffuse scattering adds information on nuclear and magnetic roughness, and in particular, on magnetic domain fluctuations in the longitudinal and transverse direction. All this can experimentally be probed and theoretically be analyzed; only a few aspects of this information could be discussed in this contribution in any more detail. Specifically, we have described polarized neutron reflectivity on a thin magnetic iron film and the information that can be gained with a one-dimensional polarization analysis in comparison to an experimental situation without polarization analysis, or one with unpolarized neutrons. Considering laterally patterned magnetic films instead of flat samples, it is important to take into account the spatial orientation of the coherence volume. In the case of stripe arrays, two main orientations with respect to the neutron polarization and the scattering plane are to be probed: stripes parallel and perpendicular to the scattering plane. The perpendicular orientation reveals the periodicity of the stripe pattern. In the parallel orientation, the stripe edge roughness and the flux distribution in the interspatial region can be probed by the off-specular diffuse scattering.More generally, if the stripes were arranged randomly, they could be taken as a rough magnetic film, featuring randomly separated islands with flats in between. This topology would give rise to off-specular diffuse scattering close to the total reflection regime without featuring Bragg reflections. The other extreme, a perfect periodic arrangement of stripes would act as diffraction grating on neutrons and causes lateral Bragg reflections. Edge roughness, waviness of the stripes, variations of stripe separation and stripe thickness, magnetic flux distribution in and between the stripes are all topics that can be addressed via specular polarized neutron reflectivity and off-specular Bragg and diffuse scattering. Clearly, periodic stripe arrays is the most basic realization of a lateral periodic structure. Any other lateral pattern, such as artificial spin ice ,25, PenrFinally we would like to emphasize that PNR on thin magnetic films and multilayers is an indispensable, highly reliable, and a straight forward tool for gaining essential information on spintronic and magnonic materials. It is complementary to X-ray resonant magnetic scattering (XRMS) in the hard and soft X-ray regime. Nowadays, XRMS is more frequently available for experimentalists because of the higher number of synchrotron radiation sources compared to neutron sources. On the other hand, the analysis of XRMS spectra is more intricate than the analysis of PNR scans because the latter method is based on resonance absorption of circular polarized X-rays, which cannot be treated by the more simple Born approximation or distorted wave Born approximation. For more information on the fundamentals of PNR and XRMS we refer to a number of excellent reviews articles ,29,30,31


Introduction: Gait velocity in spastic patients after stroke is both a life quality and mortality predictor. However, the precise biomechanical events that impair a faster velocity in this population are not defined. This study goal is to find out which are the gait parameters associated with a higher velocity in stroke patients with spastic paresis. Methods: The registries of a Gait analysis laboratory were retrospectively analyzed. The inclusion criteria were: trials of adult stroke patients with unilateral deficits. The exclusion criteria were: trials when patients used an external walking device, an orthosis, or support by a third person. Of the 116 initial patients, after the application of the exclusion criteria, 34 patients were included in the cohort, all with spatiotemporal, static and dynamic kinematic and dynamometric studies. Results: There was a correlation of velocity with cadence, stride length of the paretic (P) limb, stride length, and time of the P and non-paretic (NP) limb, double support time, all the parameters related to hip extension during stance phase, knee flexion during swing phase, and parameters related to ankle plantarflexion during stance phase. Conclusions: The main gait analysis outcomes that have a correlation with speed are related to the formula velocity = step length × cadence or are related to stance phase events that allow the anterior projection of the body. The only swing phase outcome that has a correlation with speed is knee flexion. More studies are needed from gait analysis laboratories in order to point out the most relevant goals to achieve with gait training in spastic stroke patients. Gait is an essential human function that allows us to freely move in our surrounding environment. Because of its implications, gait is provenly an important marker of quality of life  and evenThis study aims to find out which are the gait parameters associated with a higher velocity, in stroke patients with spastic paresis. It aims to provide new information regarding specific targets, towards which rehabilitation programs could be designed.Patients records of a gait analysis laboratory were retrospectively analyzed. The inclusion criteria were: trials of adult stroke patients with unilateral deficits. The exclusion criteria were: trials when patients used an external walking device, an orthosis, or support by a third person. The retrospective analysis of the patient files was authorized by the Ethical Committee of Centro de Medicina de Reabilitação de Alcoitão (project identification number 14_2020) in 05/06/2020. The data collection didn’t involve any additional action beside the standard gait analysis exam and the personal information of the patients was blinded to the investigators so an informed consent wasn’t necessary.The trials were captured using 6 infrared cameras Vicon T-series T10, 2 digital video cameras Basler piA1000-48gc, 4 force platforms AMTI OR6-7-2000, and 16 channel electromyography system Cometa Mini Wave Infinity.A total of 116 patients were screened, with the inclusion of all the stroke patients admitted from 2013 to 2016. Of those, 15 were excluded for walking with the aid of an external individual and 67 were excluded for walking with an orthosis or walking device, resulting in 34 eligible subjects .Several outcomes were extracted from patients’ gait analysis records ; namely,t-test.Statistical analysis was made using SPSS v.25 from IBM. Correlations of continuum variables were made using Pearson’s bivariate coefficient of the different variables and speed. Correlations with nominal variables as contact type were made using a The correlations between continuum variables and velocity are displayed in n = 17) and 50% with rearfoot initial contact (n = 17). The correlations of the variables with initial contact type are shown in There were 50% of the patients with forefoot initial contact .Regarding the hip extension, during stance phase was fundamental, with all hip extension-related outcomes showing significant correlations . Pelvic rotation is a known mechanism of compensation for the lack of hip extension during stance phase. On the other hand, maximum hip flexion during swing phase seems not to be relevant enough.Regarding the knee, only maximum knee flexion angle during swing phase was correlated with velocity. This was the only joint with a significant correlation with velocity in which swing phase was concerned, suggesting an important role during this phase, since it is the joint with a higher sagittal range of motion during this phase of the gait cycle, in normal gait .Regarding the ankle, all the variables that correlated with velocity concerned late-stance plantarflexion, also known as push-off angle. The type of initial contact (forefoot vs. rearfoot) did not seem to have any repercussion on gait velocity.Consequent to the above-mentioned importance of the push-off, inferred from the kinematics outcomes of the ankle, the power produced at the ankle in pre-swing was also associated with velocity.Moreover, the percentage of the gait cycle (stance phase) at which the first peak of the vertical ground reaction force occurred was also associated with velocity (the earlier the better), stressing the importance of the loading response and limb control to the efficacy of gait (velocity).During the loading response phase, immediately after the initial contact, there is a slight plantarflexion of the ankle in order to fully contact the ground. After this event, most of the stance phase is marked by a gradual extension of the hip with a concomitant passive dorsiflexion of the ankle in order to allow an anterior projection of the body. This seems to be the first, most important mechanism that has relevance for gait velocity. During pre-swing, there is an active plantarflexion of the ankle (push-off) that projects the body forward. This seems to be the second most important mechanism that has relevance for gait velocity. Both events are schematized in During swing phase there is a triple flexion of the limb (hip and knee flexion with ankle dorsiflexion), that allows clearance in aerial limb progression. Only the knee flexion during this phase was correlated with velocity, which is in accordance with the fact that it is also the joint that has an larger sagittal range of motion during swing phase.Cadence and non-paretic stride length. The latter probably relates to the compensations accomplished by the healthy side of the body, possibly reflecting the muscle strength and motor control of the areas non affected by the stroke, or even the cardiorespiratory fitness of the subject . The clinical implication of this finding is that the strength and motor control of the non-paretic limb may be a target for rehabilitation programs in order to better compensate for the deficits of the paretic limb and, consequently, allow for higher gait velocity.Maximum hip extension during the stance phase of the paretic limb: the hip is the main driving force responsible for the anterior projection of the body during early stance phase . Considering this, the correlation found is actually plausible. However, slower gait patterns are usually associated with a shorter step length and, consequently, with a limited hip extension (there is not enough time for the hip to extend fully). Hence, a limited hip extension may be at the same time a cause and a consequence of slower gait. The clinical implication of this finding is that a spastic Iliopsoas muscle  should be carefully tracked.Knee flexion of the paretic limb during swing phase: the knee is the lower limb joint that shows a larger sagittal range of motion during swing phase; consequently, an impairment at this level would have a greater impact on aerial limb progression. The clinical implication of this finding is that spasticity of the Quadriceps muscle (which can contribute to a stiff-knee pattern in extreme situations) should be carefully tracked.Ankle plantar flexion of the paretic limb during pre-swing (which enables the push-off): The ankle is the main driving force responsible for the anterior projection of the body during the late stance phase, after the hip stops extending. The clinical implication of this finding is that the maintenance of Gastrocnemius and Soleus normal activation (timely) and muscle strength is relevant to gait velocity.Taking into consideration the results, we hereby summarize the main parameters of gait, that have a correlation with velocity:The strength of this study is in its relatively high number of participants with the same pathology, and that it relies on instrumented gait analysis data. The weakness may be in the outcomes that were chosen according to the clinical experience of the authors .We must point out that the described correlations do not specify if the outcomes associated with higher velocity are a cause  or a consequence (the outcome is a characteristic of gait at higher velocities).Human gait is a complex sequence of events. There are two main characteristics of gait that are goals in rehabilitation programs: safety  and velocity (efficacy). Regarding the latter, to which this paper is dedicated, there seems to be a correlation of gait velocity with: (1) spatiotemporal parameters that are related to the formula velocity = step or stride length × cadence; (2) stance phase events that allow the anterior projection of the body; (3) knee flexion during swing phase. Further studies based on data from gait analysis laboratories are needed, in order to determine with more certainty the characteristics of gait that allow for higher velocity. This will point out the most relevant goals to achieve with gait training in spastic stroke patients, aiming to optimize custom rehabilitation programs.


The characteristic depth dose deposition of ion beams, with a maximum at the end of their range (Bragg peak) allows for local treatment delivery, resulting in better sparing of the adjacent healthy tissues compared to other forms of external beam radiotherapy treatments. However, the optimal clinical exploitation of the favorable ion beam ballistic is hampered by uncertainties in the in vivo Bragg peak position. Ionoacoustics is based on the detection of thermoacoustic pressure waves induced by a properly pulsed ion beam  to image the irradiated volume. Co-registration between ionoacoustics and ultrasound imaging offers a promising opportunity to monitor the ion beam and patient anatomy during the treatment. Nevertheless, the detection of the ionoacoustic waves is challenging due to very low pressure amplitudes and frequencies (mPa/kHz) observed in clinical applications. We investigate contrast agents to enhance the acoustic emission. Ultrasound microbubbles are used to increase the ionoacoustic frequency around the microbubble resonance frequency. Moreover, India ink is investigated as a possible mean to enhance the signal amplitude by taking advantage of additional optical photon absorption along the ion beam and subsequent photoacoustic effect. We report amplitude increase of up to 200% of the ionoacoustic signal emission in the MHz frequency range by combining microbubbles and India ink contrast agents. The deceleration of ions penetrating a medium results in energy deposition per unit of mass, i.e., dose which generates pressure waves if the amount of energy is delivered over a short period of time. In stress-confinement regime, i.e., when the duration of the ion beam pulse is shorter than the time required for the acoustic wave to travel across the heated volume, the evolution of the pressure wave over time describes the dose variation along the propagation axis2. Therefore, this distinctive feature can be used to monitor the dose distribution which is of particular interest in ion beam therapy. In the following this will be discussed in more detail in the context of proton beam therapy. Unlike photons, protons offer a characteristic inverse depth dose profile with a pronounced maximum at the end of their range, the so-called Bragg peak. The direct benefit of proton beams in external beam radiotherapy is a better sparing of the healthy tissue surrounding the tumor due to the localized dose deposition inside the tumor volume. Herewith, negative treatment side effects and risk of secondary cancer development are reduced, but full clinical exploitation of the ballistic advantages for improved treatment outcome requires a precise knowledge of the in vivo Bragg peak position. Nowadays, the targeted dose obtained from the treatment planning system is affected by uncertainties in the knowledge of the patient position at the actual day of treatment, possible anatomical changes within and between treatment fractions and inaccuracies in the derivation of stopping power ratios of protons in tissue relative to water from the underlying X-ray computed tomography images, reflecting the attenuation of photons5. Although range verification techniques based on the detection of proton-induced secondary photon emissions are being well-established and reached the stage of clinical translation7, they rely on nuclear-based processes and thus only offer indirect information of the dose distribution. Moreover, without supplemental in situ imaging devices, such techniques are not providing any in vivo information on the patient anatomy. Therefore, it is not possible to confirm with certainty whether the Bragg peak position is inside the tumor volume or in an adjacent organ at risk. In contrast, ionoacoustics is based on the detection of the direct acoustic signal resulting from the energy deposition which is particularly enhanced in the Bragg peak region. Therefore, ionoacoustic signals encode all the information on the dose, allowing either to determine the proton beam range from time-of-flight measurements8 or to reconstruct the full-dose distribution9. It is worth noting that the dose dependence of the ionoacoustic signals also implies change of the pressure wave frequency and amplitude within the treatment field as the dose distribution could be distorted due to heterogeneous tissue composition or variation in the proton beam energy (adapted during the treatment to reach a specific penetration depth). As ionoacoustics is based on ultrasonic wave detection, it can readily be combined with ultrasonic images of the anatomy providing a simultaneous monitoring of the proton beam and patient anatomy.Ionoacoustics relies on the detection of thermoacoustic waves generated when charged particles from pulsed ion beams stop in matter10, make the detection of ionoacoustic signals challenging and compromise the co-integration with ultrasound imaging systems which operate in the MHz range. Increasing the detection sensitivity is difficult, which remains the main challenge to apply ionoacoustics for in vivo online range monitoring in proton therapy. Therefore, any enhancement of the ionoacoustic signals would facilitate the integration of ionoacoustics for pre-clinical and clinical applications. In this context, spherical gold markers were recently proposed11. Exploiting the large difference in acoustic impedance between the metal and tissue, the irradiated marker acts as an acoustic resonator, resulting in frequency-modulated ionoacoustic pulses for which the modulation frequency depends on the marker dimension and can reach the MHz frequency range for millimeter-sized spheres. However, the method is limited to proton beams passing through the millimeter-sized maker whereas pencil beams are usually moved in the centimeter scale during the treatment to cover the full tumor volume. Furthermore, implanting a marker is invasive and the metallic object could distort the dose or cause artifacts in the pre-treatment imaging.However, the low pressure amplitudes and the low frequency range, in the order of some mPa in the 10 to 50 kHz range for clinically relevant proton beam energies14. The microbubble response to an incident pressure field (expansion and contraction of the bubble volume) leads to additional scattering of the acoustic waves and harmonic generation, for incident peak negative pressure inferior to the MPa level in the MHz frequency range. Injected intravenously, the insonicated microbubbles increase the contrast of ultrasound images. The micrometric size of the bubbles  depending on the time structure of the pulsed beam  and the dose distribution  for low-energy mono-energetic proton beams.Hereby, we investigate for the first time the use of contrast agents (CA) to either enhance the signal amplitude or its frequency. The ionoacoustic signals generated in SonoVue microbubbles, India ink and a mixture India ink/microbubbles are assessed for a 22 MeV pulsed proton beam. Gas-filled microbubbles are well-established in ultrasound imagingThe experiments were performed at the Tandem accelerator of the Maier-Leibnitz-Laboratory in Munich (Germany) with a 22 MeV pulsed proton beam irradiating a water tank. Figure 20. However, clinical proton beams result in larger axial dimensions due to range straggling. Therefore, we used a ripple filter to spread-out the proton beam, in order to be closer to pre-clinical and clinical scenarios. The ripple filter is made of a sawtooth structure in aluminum alloy . As the accelerated protons penetrate different thickness of aluminum, exhibiting higher stopping power than water, they stop at different ranges in the water tank, resulting in an axial expansion of the Bragg peak volume. Furthermore, protons scatters more in aluminum than in water, leading to a subsequent increase of the proton beam lateral dimensions, as illustrated in Fig. In water, the 22 MeV mono-energetic proton beam leads to a sharp dose deposition . Figure For the considered pulse period in water, the maximum/minimum amplitude successively decreases and increases as follow: it decreases from With CA, the signal amplitude remains almost constant over the excitation burst, as showed in Fig. 20. Therefore, the maximum of amplitude is obtained for proton pulse duration around the so-called stress-confinement limit i.e., the time it takes for the acoustic wave to propagate through the heated volume, here around Figure Figure Similar to the mono-energitic case, here also the microbubble resonance was investigated by increasing the number of cycles to 25 and varying the proton pulse frequency. Figure mentclass2pt{minimThe evolution of the signal amplitude with the proton pulse period is showed in Fig. CA have the potential to increase the amplitude and frequency of ionoacoustic signals. This could be a breakthrough in the development of ionoacoustic range verification for pre-clinical and clinical proton beam diagnostic currently hindered by the detection limits. Our different measurements reveal evidences of microbubble-based CA excitation with, among others, additional oscillations, phase shift and different evolution of the amplitude with the proton pulse period compared to measurements in water alone. However, our results do not show an enhancement of the signal amplitude due the presence of microbubbles themselves. When the temporal and spatial excitation frequency of the ionoacoustic signal are in agreement, i.e., measurements without ripple filter, the amplitude is reduced in the presence of microbubbles. This could be explained considering the acoustic impedance mismatch between the microbubble CA and water which results in an amplitude decrease of the pressure received by the ultrasonic sensor compared to the pressure amplitude in the vicinity of the Bragg peak. In other words and especially for the shortest pulse duration for which similar amplitudes are measured in water and with the microbubbles, the pressure might actually be enhanced by the microbubbles, but we cannot measure it due to reduced acoustic transmission between the phantom and the surrounding water.Moreover, we showed that combining microbubbles and India ink results in an amplitude increase of up to 200% of the signals obtained from spread-out dose distribution i.e., measurements with ripple filter, originating from the entrance of the CA phantom. For the mono-energetic proton beam, the signal amplitudes are lower for the microbubbles and India ink than the microbubbles alone for a proton pulse period up to 430 ns and increase for longer pulses. This noticeable discrepancy in the microbubble behavior might either be explained by an intrinsic change in the microbubble oscillation response due to the additional ink in the surrounding medium or additional contributions such as photoacoustic effect. Photoacoustics could be induced by the energy deposition, resulting from the absorption of optical photons produced in water along the proton beam axis.21.Water luminescence has already been reported for proton beams at energy lower than the Cherenkov light threshold , but is only using the equivalent model proposed by Yabe et al., which has been shown to give good agreement between simulations and experiments for a given setup and imaging modality. Figure 22). From 0–1.6 mm, the energy deposited in water by the optical photons is negligible compared to the energy deposited above 1.6 mm (in India ink) which follows the proton dose distribution showed in Fig. A simulation study was performed in order to assess if the postulated photoacoustic effect is the origin of our experimental observations. Figure Figure entclass1pt{minimaThe discrepancies in the shape of the signals between the simulations and experiments might be explained by an inaccurate model of the CA properties. Indeed, for the simulations we assumed the CA to have the same characteristics as water. In reality, the additional India ink likely alters the medium properties  sufficiently to notice the changes compared to the measurement in pure water. The acoustic impedance mismatch between CA and water was also not modeled in the simulations. This can partially explain the observed discrepancy in the direct signal amplitude, i.e., the increase of the direct signal amplitude which is not observed from the experiments. However, from the ultrasound pulse-echo measurements and evolution of the ionoacoustic signal time-of-flight, the acoustic property changes do not seem to be large enough to entirely explain the observed difference in the signal shape and do not justify the 5–6 orders of magnitude difference on the postulated light yield compared to the value previously reported by Yabe et al.18. Therefore, the increase of optical photon production in India ink compared to water would lead to an increase of the energy absorption and distortion of the sharp gradient at the CA phantom entrance. A similar effect would be also expected for the polyimide foil at the entrance of the CA phantom which is known to produce scintillation light with a sufficiently high light yield to explain the 5–6 orders of magnitude difference observed. Hence, our experimental observations may result from a mixed contribution of proton-induced luminescence and scintillation light from the polyimide film.For a given proton pulse time profile, the shape of the ionoacoustic  signal mostly depends on the spatial gradients of the absorbed energy, whereas the amplitude varies to a lesser extent with the gradients and increases linearly with the energy deposition. Therefore, the disagreement between the simulations and experiments may suggest that the approximate water luminescence using a scintillation model as previously suggested does not accurately reproduce the overall optical photon distribution, which could actually be more localized in space than what is obtained from scintillation simulations. Note, the simulation studies previously reported have been focused on modelling the portion of the optical photons propagating on an axis perpendicular to the proton beam axis and detected on a plane parallel to the beam axis, but did not investigate the optical photon three-dimensional distribution. Hence, assuming a certain directivity for the optical photons from water luminescence, which could be different to the isotropic propagation of the photons modelled using scintillation, the light yield would need to be changed to reproduce the same optical photon fluence at the detector surface. For instance, if the photons from water luminescence mostly propagated forward as for the Cherenkov light, the light yield would need to be increased in order to obtain the same amount of photons propagating on an axis perpendicular to the proton beam axis. Such a directivity could then explain the discrepancies we observe. Furthermore, we assumed the optical photon production to be the same in India ink as in water, but it is more realistic to consider the light yield in ink to be higher than in water8. Second, for synchrotron accelerators providing continuous proton beams that are consequently, in first approximation, not suitable for ionoacoustics. However, they still have a temporal micro-structure in the 5–10 MHz range, which could be enhanced thanks to the CA.The investigated CA mainly enhance the acoustic emission at the interface between the targeted volume (where the CA is concentrated) and the surrounding medium. However, accurate proton dose monitoring requires precise measurement of the direct signal from the Bragg peak, in order to deduce the distance from the sensor to the dose maximum and the spatial distribution of the proton dose deposition. Therefore, the clinical benefits of CA and new opportunities they may bring for ionoacoustics-based range verification remain to be investigated. Assuming light yields similar to those deduced from our experiments, the enhanced ionoacoustic signals were investigated for clinical proton beams delivered from two different particle accelerator technologies. First, for synchro-cyclotrons which result in a pulsed proton beam, with Gaussian proton pulses in the order of a few Figure 23, the ambiguous signal shape and limited possibility to average may hamper the time-of-flight analysis generally used to retrieve the Bragg peak position relative to the sensor. Alternatively, long continuous bursts are favorable to accurately analyze the data in the frequency-domain, offering both high frequency resolution and high signal-to-noise ratio .The ionoacoustic signals obtained considering the temporal micro-structure of synchrotrons are shown in Fig. Therefore, the CA which induces spectrum beats due to the additional pressure generated at the target entrance seems beneficial to ionoacoustic range verification using a continuous beam. Indeed, this information can be used to retrieve the distance between the Bragg peak and target entrance. Figure In conclusion, we experimentally showed that combining India ink to microbubbles increases the amplitude of the ionoacoustic signals produced when a proton beam enters the targeted volume (CA phantom). The effect may reveal the absorption of optical photons along the proton beam axis. It is assumed that, in water, the optical photons generated are spread-out because of the low absorption in the medium. With optical absorbers, such as India ink, the energy resulting from their absorption is more localized, giving rise to acoustic pressure in the same order of magnitude as the one resulting from the energy deposition of the primary proton beam, the latter being enhanced in the presence of microbubbles.10 or synchro-cyclotrons8. To the best of our knowledge, our results show for the first time that accurate ionoacoustic range verification can be achieved at synchrotron facilities, using photo-enhanced ionoacoustics and frequency-domain data analysis.Further studies are required to elucidate the origin of the optical photons, which may arise from a mixed contribution of water luminescence and scintillation light, specific to the experimental setup used. Assuming sufficiently high optical photon fluence to produce relevant photoacoustic pressures consistent to our experimental observations, we showed that photo-enhanced ionoacoustics is beneficial for accurate range verification in proton beam therapy, resulting in a higher signal-to-noise ratio (due to the pressure enhancement) and intrinsic co-registration with the underlying anatomy . More interestingly, this finding can also extend the use of ionoacoustics to a broader range of clinical accelerators. Currently, ionoacoustics is restricted to 24 due to plasmonic resonance, contributing to photoacoustic signal enhancement. In this regard, gold nanoparticle-templeted microbubbles were investigated and shown to further enhance the signal dynamics25. Moreover, gold nanoparticles used as radiosensitizers also raised an interest in radiotherapy as they can enhance the treatment biological effectiveness26. Therefore, such agents already foreseen to be used in clinical routine could be combined for local treatment efficiency enhancement and accurate treatment monitoring, both beneficial for improving the clinical radiotherapy outcomes.Therefore, optimal ionoacoustic CA/signal enhancers could be based on functionalized microbubbles. In this case, the microbubbles will promote high frequency signals and the functional bubble shell could be optimized to enhance the photoacoustic effect and consequently the ionoacoustic signal amplitude. This can be done by incorporating both absorbing materials  and scintillation materials (enhancement of the number of photons produced). Furthermore, there is a growing interest for gold nanoparticles in photoacoustic imaging27 was recorded using a 4 GHz oscilloscope  and time profiles were determined by deconvolution of the detector response function28. The pulse width at the function generator was varied from 140 ns to 300 ns and the deconvolved time profiles were compared to the signals obtained from the accelerator chopper system, confirming the reliability of the proton pulse duration obtained from the chopper system . The proton beam focus size was adjusted to be about The 22 MeV proton beam was delivered by the Tandem accelerator of the Maier-Leibnitz-Laboratory in Munich (Germany). The proton beam was pulsed by means of a chopper system, resulting in square pulses with rise/fall times below 3 ns and pulse duration ranging from 10 ns up to a few 29. The probe integrates a low-noise amplifier in order to optimize the performance in reception mode. The in-probe low-noise amplifier consists of a high input impedance, high-voltage protected, low-noise voltage buffer . The integrated front-end electronic was designed to operate in the 40 kHz to 15 MHz range in reception. We previously showed that the broadband capability of this detector allows to effectively measure ionoacoustic signals in the investigated frequency range without signal distortion induced by the sensor frequency response30. This unique feature motivated the choice of the CMUT probe for the experiments as it ensures a proper detection of both the kHz and MHz signals, as required by the measurements with ripple filter, whereas conventional piezo-composite-based transducers, typically characterized by narrower bandwidths, would have only captured one range of frequencies. Note that ionoacoustic measurements could have been performed with hydrophones to operate on such a large bandwidth but the CMUT probe also allows for ultrasound measurements in emission/reception to image the target. The signal received by the CMUT detector was amplified by 60 dB using a low-noise voltage amplifier  and acquired through a digital oscilloscope  at a sampling frequency of 156.25 MHz. In reception only, the CMUT was biased at 75% of its collapse voltage For the measurements we used a 3.5 mm-diameter single-element reverse-fabricated CMUT probe. The transducer was designed to operate in emission-reception around 12 MHzThe contrast agent solutions were prepared right before irradiation. The microbubbles were obtained from a SonoVue preparation  vigorously shaken for one minute and later diluted in deionized water  were set to zero.For the time signals presented in Figs. In order to assess the evolution of the signal amplitude and phase shift with the proton pulse period including statistics, the 3000 acquisitions of each dataset were subdivided into 30 measurements (each measurement being the average of 100 acquisitions). The complete data analysis process is illustrated in Fig. S8 of the supplementary information. The amplitudes were obtained from the Fast Fourier transform (FFT) of the different signals, extracted at the temporal excitation frequency . The simulation geometry included the 34.The spatially resolved dose depositions inside the water tank and phantom were calculated using the FLUKAFor simulating the spread-out dose distribution, the ripple filter was additionally modelled in the FLUKA simulation geometry downstream of the Titanium window. A confocal microscope was used to perform one-dimensional profile scans of the filter surface perpendicular to the ripples . This served as the input to accurately model positions and thickness of aluminum . Considering the water temperature T of the water tank, the dose D given in x,y and z:c is the speed-of-sound in that medium and The simulated dose depositions  were used as input for the ionoacoustic wave propagation based on the open-source acoustic toolbox k-waveThe simulation setup and parameters are summarized in Fig. S1 of the supplementary information. The medium properties  were varied along the beam axis in order to account for the different materials . The India ink was assumed to have the same properties as water, estimated from temperature measurement during the experiments , each setup e.g., ionoacoustic signals in water with ripple filter, was simulated for six different lateral positions of the proton beam. The pressure was then averaged considering a given weighting factor for each of the six simulated pressures. The individual weighting factors were determined by matching the lateral dimension of the total simulated dose distal to the titanium foil (average of the doses obtained at the different positions) to the EBT3 film measurement. The detailed comparison is shown in Fig. S2 of the supplementary information.39, in agreement with the experiments. The CMUT electrical impulse response was also taken into account, by filtering the simulated pressures using cascade-second order section and bandpass second order Butterworth filter (0.15–15 MHz). The filter parameters were defined in order to obtain good agreement between simulations and experiments.The simulated pressure wave was afterwards convolved with 1 or 25-cycle square pulse train of 400 ns period to mimic realistic non-delta like proton excitation40 which modelled the beamline of the Rinecker Proton Therapy Center  for a 130 MeV proton beam, irradiating a water-equivalent phantom. The medium densities were chosen to correspond with the irradiation of a 2cm hepatic tumor surrounded by liver tissue. The tumor was positioned in order to have the Bragg peak inside the targeted volume and moved axially by 5 mm so that the Bragg peak scans the tumor volume . The optical photon dose was simulated similarly as for the 22 MeV proton beam. The same optical photon absorption coefficient as in India ink was used for the hepatic tumor and the absorption in liver tissue was similar to the one in water.The proton and optical doses were obtained from the FLUKA Monte Carlo code . The simulation geometry was similar to the one previously reported by Würl et al.41 , using the empirical conversion table proposed by Yu et al.42. In order to reduce the computation workload imposed by the fine axial binning required to propagate the high frequency photoacoustic pressure  were determined on the basis of the compute tomography Hounsfield units (HU) reported by Berber et al.43, with a special effort to model the stochastic nature of the synchrotron micro-structure. Individual micro-pulses were randomly sampled following a Gaussian distribution 43 .A The beats and distance from the Bragg peak to the tumor edge were determined from the Fourier transform of the filtered signal spectra. The frequency sprectra of the ionoacoustic signals were determined from a first Fourier transform (from time to frequency domain, depicted in Fig. Supplementary material 1


Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies. Stress, an ever-growing issue in modern societies, has become an inseparable part of people’s fast-paced daily lives. Continuously increasing workload, tight deadlines, and the resulting time pressure all contribute to the increasing stress levels. Stress is an organism’s reaction mechanism to a stressor. In a stressful state, certain control systems in the human body, such as the autonomic nervous system (ANS), act mostly unconsciously to control the responses to stress by regulating some bodily functions. This mechanism has been constantly improved throughout human evolution, to create prompt canny reactions in life-threatening situations . Stress Traditional approaches for measuring stress are taken either by a psychologist interviewing the subject or by requesting the study subject to fill in particular questionnaires designed explicitly for self-reporting. Such interviews require the constant presence of a trained psychology specialist. Requesting the subjects to fill out long lists of questionnaires and self-report diaries are the most widely adopted processes to evaluate stress. These techniques are the current gold-standard as well. However, these methods are cumbersome and entirely manual. There are also other concerns and drawbacks regarding the interviews, self-report diaries, and questionnaires due to the way that different subjects commonly behave. Since these reports are based on or influenced by personal feelings, tastes, or opinions, they are highly subjective. As an example, some people tend to respond to questions in a manner that will be viewed favorably by others . Some reAny potential automated stress detection framework for daily life will be developed using a mechanism that tries to surmount the need for any intervention from a psychologist and making the whole process less incommodious. Due to the disadvantages that self-report questionnaires’ possess, research has emerged detecting regular psychophysiological signs of stress with machine learning (ML) algorithms utilizing the reliable and proven indicators such as response activities of the sympathetic nervous system, skin temperature (ST), electrodermal activity (EDA), and electrocardiogram (ECG) . The purPreliminary studies of automated stress detection were held in the laboratory environments, and then, the research took a step into daily life since researchers realized that the stress level experienced in the laboratory is different from daily life stress . HoweverGiven the complexity of data acquisition solely performed in the wild in addition to the advantages of the in-lab data, consequently, the question arises as to whether it is possible to combine these systems somehow and use the high accuracy from the first for the sake of the latter. One of the questions that stands out the most is the feasibility of combinations of these two systems to achieve even better results. In this study, we will represent a framework by which it is possible to design a stress detection mechanism that utilizes both of those methods. The data collected in a laboratory can be used for the long-term performance evaluation of the same system in daily life scenarios.Developing laboratory-based models to improve the performance of daily life stress detection systems;Comparing the performance of laboratory, daily life, and hybrid laboratory-daily life models;Collection of smartwatch-based EDA and HRV data coming from the laboratory and daily life ), with self-reports and context information;Investigating the effect of using context and self-report labels while training the model on system accuracy in different environments.Our work contributes to state-of-the-art in four different aspects:These contributions will provide insights for researchers into how to improve the performance of daily life stress detection systems.The organization of the rest of the paper is as follows. In Most of the automatic stress detection studies in the literature were conducted either in the laboratory environments or restricted daily life settings. We can examine the studies in the literature as five different classes. The first class develops a laboratory model with known context labels and tests in the same environment. Since the stressor levels  are known at any time in laboratory experiments, they could be used as the ground truth labels for machine learning (ML) models, and we called this type laboratory-to-laboratory known context (LLKC) models. The second type uses collected self-report labels in laboratory environments and tests the created model in the same environment. We call this type laboratory-to-laboratory self-report (LLSR) models. The third type is using self-report questionnaires collected in the wild and testing the model in the same environment. We named this model daily-to-daily self-report (DDSR) models. Since we could not monitor the everyday life of participants and get the ground truth all the time, a known context does not exist in daily life environments. The laboratory data could be used to enhance daily life stress detection models. If the known context labels in the laboratory are used for an ML model development, we call this fourth type laboratory-to-daily known context (LDKC) models. On the contrary, if the self-reports in the laboratory are used as labels and the developed models are tested in the wild, we name the fifth type laboratory-to-daily self-report (LDSR). Early experimental practices in the laboratory were the first building blocks of this research field. These preliminary studies provided researchers with the idea of choosing the most proper devices, features, and machine learning algorithms that can be used later in everyday life settings. In a trade-off between unobtrusiveness and accurate signals, researchers chose the types of devices with different sensing technologies based on the requirements of their study. Several researchers demonstrated that wearable devices equipped with PPG sensors added more comfort and convenience compared to ECG devices for HRV measurements ,29,30. NIn other cases, researchers decided to mitigate the trade-off between the accuracy and unobtrusiveness and conducted their studies either in the lab or in the wild using unobtrusive wearable devices. The choice of device type  is important because it determines the applicability of the system to daily life, which is the main and final goal of all the stress detection studies. In some of the studies, the data were collected in the wild and trained and tested in these environments ,23,24,25The data collected in the experiments conducted in the laboratory could be used for developing models in the wild to improve the reported low accuracies. There is a limited number of studies using laboratory data for creating models with higher performance in daily life. Li et al.  built thWe collected controlled laboratory data from the participants during a psychological experiment using our implementation of the Trier Social Test (TSST) . The socSetupPre-stress measurements (baseline)The TSST  (inducing stress)Post-stress recovery measurements (recovery)The experiment, which took approximately 1 hour, had the following steps:The communication language between interviewers and the participants was Turkish. Please note that the mother tongue of all participants was Turkish. In addition to that, they knew English as a foreign language. This circumstance affected stress induction.The setup was done as follows;Preparation of experiment areas: The camera should be set. Empatica E4 should be ready.Interviewers should keep eye contact with the participant. Their gestures and facial expressions should be neutral.The participant is informed about the procedure and then signs the consent form.The participant wears the smart band (Empatica E4).The participant is asked to turn off his/her phone in order to eliminate distraction.Before the experiment, the following procedure was applied;The participant filled out the Perceived Stress Scale (PSS) with 14 questions.The participant was told to stay in the waiting area and get rest for 10 min. Reading materials such as magazines with emotionally-neutral contents  were presented to the participant for this period.The participant filled out the PSS-5 (ambulatory PSS) form. This questionnaire was first created by Cohen et al.  and usedThe participant was directed to the interview area.TSST speech preparation period: the following text was read to the participant: “This is the speech preparation portion of the task; you are expected to prepare a five-minute speech describing why you study [name of the degree that the participant studies/studied] and why you would be a good candidate for your ideal job. Your speech will be videotaped and reviewed by the psychologists that we conduct the research with. You have five minutes to prepare and your time begins now.”The participant prepared his/her speech. There should be a digital timer in the room set to five minutes. Interviewers should leave the room.The following text was read to the participant at the end of the speech preparation period: “This is the speech portion of the task. You should speak for the entire five-minute time period. Your time begins now”. Interviewers should start the recording of the camera.TSST speech performance period: If the participant stopped during this period, interviewers allowed him/her to stay silent for around 20 s and then prompted: “You still have time remaining.”After the first 2 min of the speech period, the participants were interrupted and asked to continue their speech in English by telling them: “Could you continue in English from now on, please?”At the end of 2.5 min, if the participant did not attempt to reply to both questions, interviewers prompted the participant to answer the other question.At the end of the speech performance period, the communication between interviewers and the participant resumed in Turkish. Interviewers reset the timer to 5 min and read the following to the participant: “During the final five-minute math portion of this task, you will be asked to subtract 13 from 1022 sequentially. You will verbally report your answers aloud, and be asked to start over from 1022 if a mistake is made. Your time begins now.” If the participant makes any mistake, the interviewer says the following: “That is incorrect, please start over from 1022.” Participant filled out the PSS-5 questionnaire.Our implementation of the TSST is described as follows:Participants were directed to the couch as a relaxing place.Participants wore an Apple Watch given to him/her at this stage, followed the breathing exercise built in the Apple Watch for a minute and then followed a mindfulness video, for the remaining four minutes, on a comfortable couch, sitting or lying as the participant preferred.Interviewers should leave the room after giving the Apple Watch.At the end of the five minute long recovery period, interviewers returned to the room, and the participant filled out the PSS-5 questionnaire.In order to alleviate the stress response, we applied a biofeedback based intervention, which was the built-in breathing application of Apple Watch . The proAfter the controlled room experiments were finished, we gave the Empatica E4 devices to all participants. They were told to wear the Empatica E4 devices for twelve hours per day, between 9 a.m. and 9 p.m., for seven days. These days were not necessarily consecutive. We applied the EMA to collect information about the subject’s stress level . EMA invThe procedure of the methodology used in this study was approved by the Institutional Review Board for Research with Human Subjects of Boğaziçi University with Approval Number 2018/16. Prior to the data acquisition, each participant received a consent form that explained the experimental procedure and its benefits and implications to both society and the subject. The procedure was also explained verbally to the subject. All of the data were stored anonymously.In order to propose an unobtrusive stress detector suitable for everyday use, we used an Empatica E4  comfortaThe physiological signals coming from different sensors were segmented into non-overlapping time windows. We selected the window size as two minutes since it was reported that the duration of stress stimulation and recovery processes was approximately a few minutes and these segment sizes could capture such processes . IBI  intervalsStandard deviation of the inter-beat intervalRoot mean square of the successive difference of the RR intervals.Percentage of the number of the successive RR intervals varying more than 50 ms from the previous intervalTotal number of RR intervals divided by the height of the histogram of all RR intervals measured on a scale with bins of 1/128 sTriangular interpolation of RR interval histogramPower in the low-frequency band (0.04–0.15 Hz)Power in the high-frequency band (0.15–0.4 Hz)Ratio of LF to HF.Prevalent low-frequency oscillation of the heart ratePrevalent high-frequency oscillation of the heart ratePower in the very low-frequency band (0.00–0.04 Hz)Related standard deviation of successive RR interval differencesWe extracted 13 heart rate variability (HRV) time and frequency domain features from RR intervals. These features were commonly used in the previous works ,43,46,47We applied correlation-based feature (CBF)  selectioAlthough we had evenly distributed data in terms of known context labels in the laboratory, we had a class imbalance problem in the self-reported ground truth labels obtained from the daily life and laboratory. In the laboratory, 71.5% of the data was relaxed and 28.5% of data was stressed. In addition, 73% of the daily life data was relaxed, and the remaining 27% was stressed. We overcame this problem by randomly undersampling the extra samples of the majority class, which is the most commonly used procedure for imbalanced datasets . We furtWe developed five different models that used different ground truth types and 248 training and test environment combinations see . The datWe used five different machine learning classifiers for recognition of stress events. These classifiers were the ones mostly used and that best performed in the literature ,42, nameIn order to select the best parameter set for the MLP, we experimented with the different numbers of hidden layers  and units (from 1 to 20); one outperformed others, which had two hidden layers, and each layer had five units. We experimented with N for the kNN. After parameter tuning, the best N was selected as 3. We applied the radial basis kernel (RBF) and linear kernel for the SVM. We selected RBF for SVM because it outperformed the linear kernel. For the RF, we selected the number of trees as 100.We divided our tests into two groups: laboratory and in the wild experiments. We examined the results in the following two sections.In this experiment, our aim was to differentiate between the stress and baseline states. In this section, we investigate the performance of our stress detection scheme in two different manners. The first one was using the known context as the ground truth. We further provide these labels as classes to the ML algorithm. The second way was to use the perceived stress levels collected from self-reports as the ground truth. In order to measure the perceived stress levels, we collected PSS-5. For these five questions, positive emotions (happy and cheerful) were evaluated inversely. In other words, if a participant stated six: extremely happy from 1–6, it was evaluated as one because happiness and cheerfulness are inversely proportional to stress levels. On the other hand, anger, sadness, and frustration were evaluated proportionally when calculating the score . On the other hand, in the wild, we had only self-reports of individuals, which was among the reasons why daily life stress detection performances were low , and theWe used both laboratory ground truth labels separately while developing machine learning models for laboratory environments and testing in daily life. The LDSR model was trained with the laboratory self-report labels, whereas the LDKC model was trained with the laboratory known context labels. We expected that laboratory self-report based labeling would have higher performance than laboratory context-based labeling because we had only the perceived stress labels (questionnaires) in the wild, which were more coherent with the laboratory self-reports. Furthermore, participants might experience different perceived stress than the known context implied, which reduced the performance of the LDKC models. We further developed a DDSR model that was solely trained and tested with daily life data. We compare these three models in Since stress detection systems have lower accuracies in the wild when compared to laboratory environments, there is a need to develop new techniques to improve their performance. In this study, we examined the effect of developing ML models in different environments and with varying ground truth labels. To the best of our knowledge, this was the first work to examine all possible combinations of perceived stress measurements for daily life and laboratory settings along with different ground truth labels. We used EDA, HRV, ST, and ACC signals  in our unobtrusive stress detection system. We first trained and tested our system in the laboratory environment. We obtained a maximum of 94.4% accuracy with HR, 86.70% with EDA, and 92.30% with HRV + EDA, which showed that our system detected the stress levels in the laboratory successfully. These results were aligned with the literature . Choosin


Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients’ assistance needs. In medicine, the importance of evaluating patient-centered outcomes in both clinical research and in the management area has grown in recent years . This apOther Italian regions have used similar systems. However, these systems, in several cases, do not consider the complexity of patient care. The computation of the number of nurses and nurse assistants dedicated to patient caregiving is a problem widely debated in the literature. A recent European study (RN4CAST) involving more than 25,000 nurses showed that a small number of nurses negatively influenced the mortality rate after surgical procedures . In the In the nursing literature, there remains a paucity of data-driven classification systems, especially in clinical decision support. However, in nursing fields, there is also growing application of MLTs , for exaThis study is a retrospective study of administrative data recorded between 2015 and 2018 at the University Hospital of Padova . Patients older than 18 years old admitted to the hospital in an ordinary regime  with a length of stay of at least three days  were included in the study. Patients admitted to intensive care and to the maternal unit were excluded.The Barthel index is an instrument widely used worldwide for the evaluation of patients’ ability to perform basic activities of daily living (ADLs) . The tooThis study is based on administrative data without any involvement of the patients. Patients admitted to our hospital provided their consent to use their data for research papers. The study was approved by an internal review board. Data were treated anonymously according to the current regulations, both Italian and European.Descriptive analysis is reported as quartiles I, II (median), and III for continuous variables and as percentages and absolute numbers for categorical variables.An MLT algorithm was used to cluster patients according to their clinical features. The following variables were considered for the computation: age, gender, admission date, the first diagnosis of discharge classified according to the International Classification of Disease (ICD-9), department of admission, and the partial score of the Barthel index. The steps of the MLT classification are shown in A large clustering applications (CLARA) algorithm  was consGroups distinguished with the CLARA algorithm do not necessarily have clinical meaning in terms of patient care needs. Patients who are not clearly distinguished by the algorithm  fall into a doubtful zone. A randomly selected sample of patients located in the doubtful zone was manually classified by an expert nurse according to the groups defined by the CLARA clustering algorithm. The expert nurse used additional information to classify patients, such as integrated clinical nursing notes, Braden scale for risk of pressure ulcers , Conley The MLTs used in doubtful cases were as follows: (i) random forest (RF), a supervised learning algorithm that randomly creates and merges multiple decision trees into one “forest” ; (ii) a The records included in the study number 74,514. Of these records, 52%  were from women with a median age of 64 years old admitted mainly in medical  and critical  areas .The best CLARA algorithm was the one that clustered records into two groups: low resource consumption (LRC) and high resource consumption (HRC) . AnalyziAn expert nurse manually classified 1000 records of the 6271 records belonging to the doubtful group (group 3) to create the gold standard. Among the gold standard cases, 475 records were placed in the HRC group, while 525 were placed in the LRC group.The records classified by the expert’s opinion served as the gold standard for the training of the RF, SVM, and GLMN algorithms. The two-step algorithm was then applied to the overall sample. In the The LRC group was more represented than the HRC group in each year considered. Gender was equally distributed in each group . The disThis study classifies patients on the basis of administrative data scoring according to physical function evaluation. Administrative data are a rich and inexpensive source of general epidemiological information .The two-step algorithm classifies patients into two main categories based on the level of care assistance needed in activities of daily living. The percentage of patients classified at a high level of assistance in the overall sample was 21.6%. The patients considered to be at a high level of assistance are prevalently elderly, are admitted to emergency urgency and medical departments, and have a lower score on the partial Barthel index. The group classified as having a low level of assistance was instead characterized by patients with higher partial BI scores. These patients were mainly women and were principally admitted to the mental health ward or surgical departments.The problem of nursing and nursing assistant allocation is particularly relevant in healthcare systems in terms of the cost and quality of care provided. These two professional figures represent the largest segments of healthcare systems; for this reason, the management of this professional component has a relevant impact in terms of healthcare costs. Despite the high impact of nursing work in the healthcare system, this component is not considered in reimbursement systems, especially in Italian systems .Other instruments for the evaluation of patients′ complexity of care have been reported in the literature. However, they cannot always be used because some of them were created for the intensive care unit setting or require further data  for their utilization. The project research of nursing, for example, requires the creation of a detailed list of nursing activities . In the This work proposes a two-step classification algorithm leading to automatic profiling of patients into high- and low-assistance groups. The first step is based on a cluster analysis aimed at identifying a specific group of patients with particular care needs. Cluster analysis is the most commonly used MLT in the nursing literature . In prevThe second classification step of the algorithm proposed in this work is based on MLT classification algorithms. The literature has demonstrated that these methods are useful when managing a large amount of data, as shown in research aimed at identifying critical elements for predicting risk status in nursing documentation . AnotherIn all of the examples reported, MLTs were used as support tools for clinical decisions; this work instead proposes a two-step algorithm as a support tool for organizational management.This study was a single-center study considering the data provided by a university hospital. Future research developments are needed to apply and validate the two-step algorithm using similar data.To our knowledge, there has been a paucity of studies in the nursing field attempting to classify patients using administrative data to make clinical decisions with the support of automatic tools. The main result of this study is that patients admitted to the hospital can be classified as having a high level or low level of assistance needed according to both medical information and physical activities. This objective is crucial in nursing research, especially when considering specific populations, such as older people, who often require more nursing and nursing assistant aid in daily care routines due to their decline in physical function.The two-step algorithm could be used as a support tool in organizational management. In a subsequent phase, the two-step algorithm could be used to automatically profile patients as high or low assistance needed at admission. This procedure will allow us to organize the allocation of a greater number of professionals to these departments, which mainly consist of patients requiring additional care.The early identification of patients who require more nursing assistance could improve the quality of care, reduce negative outcomes for both healthcare providers and patients and help in the planning of appropriate pathways according to the needs of the patient. The strength of this two-step algorithm, compared to other systems for quantifying the personnel needed, lies in the clusterization being data-driven and not derived from an a priori classification. Moreover, the data required in this two-step algorithm do not require specific information related to the patient, which may not be available at all times.Thus, MLTs, when applied to high-dimensional data, offers the opportunity to classify patients requiring a high level of nursing assistance accurately. These techniques are favored over traditional statistical models since they can identify complex relations between data, improving the prediction ability. Further studies should be performed to improve model generalizability by including data from other countries. However, future work is needed to improve these results by implementing them in clinical practice.