Abstract Background As a result of decades of migration in Germany, the number of persons with migration backgrounds from different cultures in residential long-term care nursing facilities will increase. As a result, cultural diversity may also have an impact on nutritional aspects in long-term care. The project ‘Digitally supported diversity and culturally sensitive nursing care on nutritional intake’ (NUTRI-SENSE) examines strategies to improve the nutritional and fluid intake of residents with migration backgrounds in long-term care facilities. The interdisciplinary project aims to improve their health and quality of life with a digitally supported nursing process to prevent undernutrition and dehydration. To synthesize the evidence on diversity- and culturally sensitive approaches in nutrition and fluid intake management, a scoping review was conducted. This research led to the question of the extent to which cultural sensitivity with regard to nutrition and fluid intake is addressed in long-term care nursing homes. A literature search of different databases (PubMed, CINAHL, LIVIVO, CareLit®, manual search: Google Scholar) was conducted in May and June 2025. Results From the 8.010 findings, 28 publications were screened, and 6 publications were included in the review. The evidence on diversity- and culturally sensitive approaches in institutional long-term care nursing regarding nutrition and fluid intake is limited. The main topics are the relationship of culture-specific and dementia-specific needs; the emotional aspects of belonging, food and memories of residents with dementia; meals as a vital source of well-being in nursing homes; the meaning of mealtime experiences in a multicultural society; and, finally, the involvement of family members in the food supply. Conclusion The findings from the scoping review revealed that a systematically developed, diverse and culturally sensitive framework for managing residents’ nutritional and fluid intake in long-term care facilities has not yet been established. Such a framework is the goal for subsequent research and the development of interventions within the NUTRI-SENSE project. In the context of international population ageing and increasing needs in the long-term care sector, the development and evaluation of culturally and diversity-sensitive nutPrition and hydration strategies are of broad, cross-national relevance.

Realistic reproduction of human facial expressions is essential for realistic interactions between humans and humanoid robots. This work presents a data-driven framework for transferring human facial expressions to a humanoid robot and a virtual avatar, aiming to enhance emotional expressiveness and assess its applicability in psychiatric training scenarios. The proposed approach enables cross-domain facial expression mapping while accounting for mechanical constraints of robotic actuation. A user study (n = 40) evaluated emotion recognition across three stimulus categories: human faces (H), unconstrained virtual avatars (A) and humanoid robots with limited facial actuation (R). Participants identified emotions from static images and from dynamic expression sequences, presented with and without speech. Perceived realism and uncanny valley effects were assessed using an eight-item questionnaire rated on a 7-point Likert scale. Results indicate that human-to-robot facial expression transfer is feasible but constrained by mechanical expressivity. Highly expressive emotions such as surprise (H: 87.5%; A: 57.5%; R: 65%) and fear (H: 45%; A: 27.5%; R: 57.5%) achieved moderate recognition rates, whereas subtle emotions such as anger (H: 65%; A: 40%; R: 12.5%) and disgust (H: 60%; A: 10%; R: 22.5%) were poorly recognized on the robot. Dynamic expressions combined with speech significantly improved recognition. These findings demonstrate the feasibility of transferring human facial expressions to humanoid robots while highlighting current limitations of robotic facial actuation. The proposed framework provides a promising basis for emotionally realistic patient simulation and training applications in mental healthcare.

Background The purpose of preoperative informed consent is to provide patients with comprehensive information about their treatment, including risks and alternatives, to enable informed decision-making. However, studies have shown that patients are often unable to understand or remember important information. Mobile health (mHealth) and augmented reality (AR) apps have been identified as promising solutions to improve patient education and knowledge retention. Objective This study aims to identify the essential requirements for an mHealth app to support informed decision-making for patients with colorectal cancer, with a specific focus on the potential of AR for visualization. This research explores the patient and physician perspectives on these requirements, particularly regarding information delivery and visualization to guide app design. Methods A qualitative focus group study was conducted with groups of mostly patients with colorectal cancer and a physician’s group. Topics related to patient education were discussed, guided by a semistructured interview guide covering personal experience; information content; context of use; and acceptance and presentation of content, which included presenting various visualizations in 2D, 3D, and AR. The interviews were transcribed and analyzed using qualitative content analysis. Results We conducted 4 focus groups with patients (n=23) and 1 focus group with physicians (n=7), for a total of 30 participants. Relevant informational content for the app and its presentation was identified. Patients consistently expressed a desire for personalized, detailed, and visual information about their condition and treatment tailored to their specific case throughout the treatment journey, so they could prepare for the informed consent discussion after diagnosis, prepare for treatment, access guidance and track progress during hospitalization, and access information and resources during recovery after treatment. Patients demonstrated a strong preference for interactive 3D visualizations, while physicians favored simpler 2D images that could be easily integrated into their existing workflow. AR visualizations were seen as a potential tool to provide a general overview of anatomy and surgical approaches but more as a novelty feature and a supplement to more traditional visualizations. Conclusions An ideal patient education app combines comprehensive content with interactive, customizable visualizations like 3D models and AR and should be accessible throughout a patient’s treatment journey. This study highlights the need for a patient-centered design that balances detailed information with ease of understanding and considering different preferences for visualization modalities and levels of detail.

Human walking can be modeled using a springmass-damping (SMD) system. While most studies have focused on standard activities in healthy young to middle-aged populations, less attention has been given to participants' responses to unexpected gait perturbations. These responses may be valuable predictors of falls, particularly in older adults. Our previous study modeled walking in the vertical direction for a diverse group of participants. Since most gait perturbations occur in the medio-lateral and anteriorposterior directions, the next step involves extending the analysis to these directions and treadmill walking between perturbations.The study included 60 adults (aged 18-87 years), who walked on a perturbation treadmill while wearing an inertial measurement unit (IMU) at the lumbar region to capture body acceleration. Participants first walked at their preferred speed on the treadmill ("normal gait data"), followed by "perturbation trials" with gait perturbations. The gait data between the perturbations were analyzed as "inter-perturbation gait data". Force data was recorded using the treadmill's built-in force plates. The SMD model was applied to calculate damping and stiffness coefficients.The lowest median spring stiffness was found in medio-lateral direction and the highest in anterior-posterior direction. The lowest damping coefficient was found in medio-lateral direction and the highest in vertical direction. Compared to "normal gait data", "inter-perturbation gait data" showed higher stiffness and, for some participants, higher damping coefficients, while others exhibited decreased damping. Damping and stiffness coefficients were successfully extracted from treadmill walking data across all directions for a diverse group of participants and linked to gait dynamics. The analysis highlighted human gait adaptability under various conditions. This study provides groundwork for future research on individual responses to unexpected gait perturbations.Clinical relevance- Describing human walking with damping and stiffness coefficients in different directions could contribute to understand reactive dynamic balance, and thus give a sound estimation of a relevant risk factor for falls in older people.

The interdisciplinary AdaMeKoR project (An Adaptive Multi-Component Robot System for Nursing Care) focused on the development, evaluation, and reflection of a robotic system for nursing care. Conducted from 2020 to 2023, its goal was to create solutions that support both care recipients and professional caregivers. The project investigated the potential for physical relief of caregivers and patients’ increased autonomy, guided by the central question of how good care can be supported, while also considering implementation research and ethical implications. This article provides an overview of the project’s theoretical framework, a description of the developed system, and four exemplary empirical studies. The results include quantitative data on the physical relief potential and user experiences with different control formats, as well as qualitative findings from implementation research and ethical analysis. Further, these results are contextualized with respect to the support of good care. Finally, the article discusses broad implications for the research and implementation of robotic systems in nursing care and outlines key overarching insights. The project demonstrates the clear benefits of interdisciplinary collaboration that incorporates the perspectives of all stakeholders involved in care.

The five time chair rise test (5CRT) is commonly used in geriatric medicine and research to assess functional capacity and lower extremity strength to detect early age-related changes in older adults. Traditional stopwatch-based analyses may mask temporal variations in 5CRT transitions due to averaging. Temporal variations and dynamic characteristics are better assessed by motion variability analysis. This work employs k-means clustering using Dynamic Time Warping (DTW) as a metric for 5CRT to examine compensation mechanisms of older adults. The observational study included 172 healthy, community-dwelling adults aged 70+, yielding 860 chair rises recorded on a force plate and clustered using k-means. Descriptive statistics summarized performance distribution across clusters. Optimal clustering revealed two movement patterns, differing significantly (p $$<0.01$$ ) in 5CRT duration and forces during the stabilization phase. These patterns did not correlate directly with shorter or longer 5CRT durations, indicating overlap and highlighting the limitations of traditional stopwatch methods. This study demonstrates the potential of DTW and k-means clustering in geriatric medicine and research, enabling analysis of 5CRT performance independent of temporal variations, identifying potential health issues undetectable by conventional methods. The k-means model can be further trained to automate analysis, enhancing insights from 5CRT.

BackgroundThe addition of simulated patients to medical and nursing training makes it possible to create a link between theory and practice. This makes what has been learned more realistic and allows the complexity and multilayered nature of many illnesses to be reflected in a real-life setting. However, the selection, training, and supervision of actors as simulated patients is time consuming and expensive. In this study, we investigated how differently students and nurses perceive 2 different methods of patient simulation. ObjectiveThe aim of this pilot study was to investigate whether patient behavior simulated by a humanoid robot is comparable to patient simulation by actors in videos in terms of training success and user acceptance. Participants were asked to recognize the symptoms presented by the humanoid robot and make a diagnosis. For comparison purposes, we asked a second group of participants to make a diagnosis based on a video featuring a human patient actor. MethodsWe asked the participants (medical students and nursing staff; N=21) to conduct a psychopathological assessment. Group 1 (n=11) used the humanoid robot as a patient simulator, and group 2 (n=10) watched the identical symptoms in a video with a human actor as patient. ResultsThe participants had a mean age of 28.7 (SD 3.5) years. The students were in their sixth semester and had, on average, 7.6 (SD 3.3) years of professional experience in the medical field. The correct diagnosis was made 90% (9/10) of the time based on the video with the human patient actor and 91% (10/11) of the time based on the robot. One participant in each group made the wrong diagnosis, constituting a total error rate of 10% (2/21). In general, participants with the humanoid robot as patient simulator felt more confident that their diagnosis was correct compared to those with the human actor as patient (humanoid robot: 9/11, 82% were neutral to very confident and 2/11, 18% were uncertain to very uncertain; human actor: 9/10, 90% were neutral to very confident and 1/10, 10% were uncertain or unsure). ConclusionsThe simulations of the human actor in the video were judged to be more realistic overall than those of the humanoid robot as patient simulator. However, the differences between the simulation methods in relation to the result (diagnosis) were very small. The results of our pilot study show a good performance of the robot in the simulation of selected psychiatric patient cases. We conclude that a humanoid robot could be a useful addition to patient simulators in medical education and discuss future directions.

Falls are a significant health problem in older people, so preventing them is essential. Since falls are often a consequence of improper reaction to gait disturbances, such as slips and trips, their detection is gaining attention in research. However there are no studies to date that investigated perturbation detection, using everyday wearable devices like hearing aids or smartphones at different body positions. Sixty-six study participants were perturbed on a split-belt treadmill while recording data with hearing aids, smartphones, and professional inertial measurement units (IMUs) at various positions (left/right ear, jacket pocket, shoulder bag, pants pocket, left/right foot, left/right wrist, lumbar, sternum). The data were visually inspected and median maximum cross-correlations were calculated for whole trials and different perturbation conditions. The results show that the hearing aids and IMUs perform equally in measuring acceleration data (correlation coefficient of 0.93 for the left hearing aid and 0.99 for the right hearing aid), which emphasizes the potential of utilizing sensors in hearing aids for head acceleration measurements. Additionally, the data implicate that measurement with a single hearing aid is sufficient and a second hearing aid provides no added value. Furthermore, the acceleration patterns were similar for the ear position, the jacket pocket position, and the lumbar (correlation coefficient of about 0.8) or sternal position (correlation coefficient of about 0.9). The correlations were found to be more or less independent of the type of perturbation. Data obtained from everyday wearable devices appears to represent the movements of the human body during perturbations similar to that of professional devices. The results suggest that IMUs in hearing aids and smartphones, placed at the trunk, could be well suited for an automatic detection of gait perturbations.

Introduction While various models and studies have investigated the simulation and theoretical description of human gait, previous research has not considered irregular situations such as slipping and tripping. However, understanding these situations could be crucial and provide insight into assessing dynamic balance, a factor that is closely linked to the risk of falling. The first step is to investigate the system under normal walking using a participant group that includes people who are particularly susceptible to an increased risk of falling, such as older adults or people with certain health conditions that have so far been neglected in the literature. Methods In this study a control engineering approach was used to estimate the parameters of the human gait system. A total of 42 subjects aged 18 to 78 years, with different body masses and walking speeds, were analyzed. Measurements were conducted on a treadmill with integrated force plates, while participants wore inertial measurement units (IMUs) at lumbar level and were filmed with cameras. Results The calculated spring-mass-damping model parameters, including stiffness (k) and damping (c), varied widely among participants, with stiffness ranging from 4.45 to 45.53 kN/m and damping from 0.26 to 2.13 kNs/m. Contrary to previous findings, no linear relationships were observed between stiffness or damping and velocity, age, or mass. Outliers were identified and related in a plausible way to visual inspection. Conclusion In conclusion, our study successfully characterized human walking during periodic excitation for a diverse participant group using a simple control engineering approach, yielding comparable results to previous studies. Our results pave the way for future investigations to analyze the response of an individual participant to irregular gait perturbations.

Simulation-based learning is an integral part of hands-on learning and is often done through role-playing games or patients simulated by professional actors. In this article, we present the use of a humanoid robot as a simulation patient for the presentation of disease symptoms in the setting of medical education. In a study, 12 participants watched both the patient simulation by the robotic patient and the video with the actor patient. We asked participants about their subjective impressions of the robotic patient simulation compared to the video with the human actor patient using a self-developed questionnaire. In addition, we used the Affinity for Technology Interaction Scale. The evaluation of the questionnaire provided insights into whether the robot was able to realistically represent the patient which features still need to be improved, and whether the robot patient simulation was accepted by the participants as a learning method. Sixty-seven percent of the participants indicated that they would use the robot as a training opportunity in addition to the videos with acting patients. The majority of participants indicated that they found it very beneficial to have the robot repeat the case studies at their own pace.

Quality assurance in research helps to ensure reliability and comparable results within a study. This includes reliable measurement equipment and data-processing. The Azure Kinect DK is a popular sensor used in studies with human subjects that tracks numerous joint positions with the Azure Kinect Body Tracking SDK. Prior experiments in literature indicate that light might influence the results of the body tracking. As similar light conditions are not always given in study protocols, the impact needs to be analyzed to ensure comparable results. We ran two experiments, one with four different light conditions and one with repeated measures of similar light conditions, and compared the results by calculating the random error of depth measurement, the mean distance error of the detected joint positions, and the distance between left and right ankle. The results showed that recordings with similar light conditions produce comparable results, with a maximum difference in the median value of mean distance error of 0.06 mm, while different light conditions result in inconsistent outcomes with a difference in the median value of mean distance error of up to 0.35 mm. Therefore, light might have an influence on the Azure Kinect and its body tracking. Especially additional infrared light appears to have a negative impact on the results. Therefore, we recommend recording various videos in a study under similar light conditions whenever possible, and avoiding additional sources of infrared light.

Introduction:Falls are one of the most common causes of emergency hospital visits in older people. Early recognition of an increased fall risk, which can be indicated by the occurrence of near-falls, is important to initiate interventions.Methods:In a study with 87 subjects we simulated near-fall events on a perturbation treadmill and recorded them with inertial measurement units (IMU) at seven different positions. We investigated different machine learning models for the near-fall detection including support vector machines, AdaBoost, convolutional neural networks, and bidirectional long short-term memory networks. Additionally, we analyzed the influence of the sensor position on the classification results.Results:The best results showed a DeepConvLSTM with an F1 score of 0.954 (precision 0.969, recall 0.942) at the sensor position "left wrist."Discussion:Since these results were obtained in the laboratory, the next step is to evaluate the suitability of the classifiers in the field.

Musculoskeletal Disorders (MSD) are one of the most significant health hazards for nurses causing a high rate of sick leave and exit the profession before the retirement age. In this context, physically heavy tasks like lifting patients frequently in awkward stooped or forced postures lead to constant high physical stresses and thus to MSD and physical deterioration. Therefore, in order to reduce physical load, a focus must be placed on the education and training of nurses on ergonomically correct working techniques. At present, technical assistance for the analysis of ergonomic working methods is rarely used in nursing training. In this work, we present a novel sensor system to improve the educational processes in the healthcare profession. The system includes three-dimensional optical sensors, a wearable sensor suit, a ground reaction force plate and surface electromyography to record and analyze nursing tasks. The system is tested and evaluated in a case study with nursing students (n $$=$$ 13) during a simulated transfer task. The system provided in-depth evaluation of the conducted transfer and increased the feedback quality of an instructor compared to conventional training methods.

Background Although digital approaches for disease prevention in older people have a high potential and are being used more often, there are still inequalities in access and use. One reason could be that in technology development future users are insufficiently taken into consideration, or involved very late in the process using inappropriate methods. The aim of this work was to analyze the motivation of older people participating, and their perceptions of future participation in the research and development process of health technologies aimed at health care for older people.Methodology Quantitative and qualitative data from one needs assessment and two evaluation studies were analyzed. The quantitative data were analyzed descriptively and the qualitative data were analyzed content-analytically with inductive-deductive category formation.Results The median age of the 103 participants (50 female) was 75 years (64-90), most of whom were interested in using technology and had prior experience of study participation. Nine categories for participation motivation were derived. A common motivation for participation was to promote and support their own health. Respondents were able to envision participation both at the beginning of the research process and at its end. In terms of technique development, different ideas were expressed, but there was a general interest in technological development. Methods that would enable exchange with others were favored most.Conclusions Differences in motivation to participate and ideas about participation were identified. The results provide important information from the perspective of older people and complement the existing state of research.

Purpose Physically demanding activities at the nursing bed are a key factor in the overwork of nursing staff and play a major role in the development of musculoskeletal disorders. The heavy back strain plays a significant part in this. Technical aids such as robotic assistance systems have the potential to minimize this overload during nursing activities. In the present work, we have investigated the relief potential of a supporting robotic assistance system developed in the AdaMeKoR project. An exploratory study design was developed to assess the relief potential of the robotic system for nurses during the care action of repositioning from the supine position to the sitting position at the edge of a nursing bed under kinaesthetic principles. Methods The study was conducted in March 2022 with a total of 21 nursing professionals participating. Safety precautions at this stage of the robot’s development made it necessary to use a 40 kg patient simulator instead of having a human act as the patient. Each participant performed the repositioning three times in the conventional manner and three times with the robotic-assistance. The conventional and the robotic-assisted task execution was compared using different perspectives of analysis. From a sensory perspective, ground reaction forces and electromyography data were collected and analyzed. A kinaesthetic perspective was added using 3D-video data which was analyzed by professional kinaesthetics trainers. A third perspective was added by collecting the subjective workload experiences of the participants. Results While participants’ self-assessment based on a NASA-TLX questionnaire suggests more of a physical and psychological strain from using the robot, electromyography shows a 24.41% reduction in muscle activity for left back extensors and 7.99% for right back extensors. The kinaesthetic visual inspection of the study participants also allows conclusions to be made that the robot assistance system has a relieving effect when performing the nursing task. Conclusions The conducted study suggests that overall the robotic-assistance has the potential of relieving nurses of partial physical exertion during mobilization. However, the different focuses of analysis show varying results in regard to external, i.e. sensor data and expert analysis, compared to internal, i.e. the nurses, perspectives. Going forward, these results have to be further expanded to get more robust analyses and insights on the interdependencies of subjective factors contributing to the experience of workload. In view of the fact that robotics for nursing is still a relatively new field and there are various lessons to be learned regarding the conceptualization of studies and corresponding evaluations, our approach of combining perspectives of analysis allows for a more differentiated view of the subject at hand.

Robotic use cases where a robot must perform a force application can lead to overloaded joints due to excessive external joint forces. This results in performance degradation or even hardware damage in the worst case. Such cases are conceivable, for example, in physical human-robot interaction, where the collaborating robot should remain relatively small and light, but large loads have to be handled several times a day in collaboration with humans, e.g., in nursing care. For this purpose, we present an approach to estimate suitable configurations of a predetermined task with a start and goal robot pose to generate a robot manipulator path which possesses suitable conditions to leverage the manipulator's maximum pushing force potential. We also evaluate different metrics to show that robot base placement plays a major role when utilizing the robot manipulator's pushing capability for different tasks. Based on the robot's configuration and/or placement, the pushing force potential can differ by several magnitudes.

The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.