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After prolonged stay in an intensive care unit (ICU) patients often complain about cognitive impairments that affect health-related quality of life after discharge. The aim of this proof-of-concept study was to test the feasibility and effects of controlled visual and acoustic stimulation in a virtual reality (VR) setup in the ICU. The VR setup consisted of a head-mounted display in combination with an eye tracker and sensors to assess vital signs. The stimulation consisted of videos featuring natural scenes and was tested in 37 healthy participants in the ICU. The VR stimulation led to a reduction of heart rate (p = 0. 049) and blood pressure (p = 0.044). Fixation/saccade ratio (p < 0.001) was increased when a visual target was presented superimposed on the videos (reduced search activity), reflecting enhanced visual processing. Overall, the VR stimulation had a relaxing effect as shown in vital markers of physical stress and participants explored less when attending the target. Our study indicates that VR stimulation in ICU settings is feasible and beneficial for critically ill patients.

Stress is a part of everyday life which can be counteracted by evoking the relaxation response via nature scenes presented using immersive virtual reality (VR). The aim of this study was to determine which sensory aspect of immersive VR intervention is responsible for the greatest relaxation response. We compared four conditions: auditory and visual combined (audiovisual), auditory only, visual only, and no artificial sensory input. Physiological changes in heart rate, respiration rate, and blood pressure were recorded, while participants reported their preferred condition and awareness of people, noise, and light in the real-world. Over the duration of the stimulation, participants had the lowest heart rate during the audiovisual and visual only conditions. They had the steadiest decrease in respiration rate and the lowest blood pressure during the audiovisual condition, compared to the other conditions, indicating the greatest relaxation. Moreover, ratings of awareness indicated that participants reported being less aware of their surroundings (i.e., people, noise, light, real environment) during the audiovisual condition versus the other conditions (p < 0.001), with a preference for audiovisual inputs. Overall, the use of audiovisual VR stimulation is more effective at inducing a relaxation response compared to no artificial sensory inputs, or the independent inputs.

This pilot study aimed to evaluate the usability of a new, feedback-based dexterity training system in people with Parkinson’s disease (PwPD) and healthy adults. Seven PwPD and seven healthy adults participated in the study. The System Usability Scale (SUS) and the Post-Study System Usability Questionnaire Version 3 (PSSUQ) were used to assess usability. Additionally, the feedback shown as a counter, detected through newly developed algorithms, was evaluated by comparing the device-detected repetitions during six exercises to those counted by a supervisor. High median SUS scores of 92.5 were obtained in both PwPD (IQR = 81.25–98.75) and healthy adults (IQR = 87.5–93.75, maximum score 100, minimum score 0). Similarly, high PSSUQ median scores were achieved after the session (1.14, IQR = 1.00–1.33, PD; 1.08, IQR = 1.00–1.58, healthy adults, maximum score 1, minimum score 7). PwPD completed 648 repetitions, with 551 (85%) correctly recognized by the system. For healthy adults, 883 out of 913 (97%) repetitions were classified as right. The present study showed high usability and high perceived user satisfaction for the new training system in all study participants. The system effectively detects exercise repetition rates but requires further refinement to enhance accuracy for specific pinch grip exercises.

Analyzing irregularities in walking patterns helps detect human locomotion abnormalities that can signal health changes. Traditional observation-based assessments have limitations due to subjective biases and capture only a single time point. Ambient and wearable sensor technologies allow continuous and objective locomotion monitoring but face challenges due to the need for specialized expertise and user compliance. This work proposes a seismograph-based algorithm for quantifying human gait, incorporating a step extraction algorithm derived from mathematical morphologies, with the goal of achieving the accuracy of clinical reference systems. To evaluate our method, we compared the gait parameters of 50 healthy participants, as recorded by seismographs, and those obtained from reference systems (a pressure-sensitive walkway and a camera system). Participants performed four walking tests, including traversing a walkway and completing the timed up-and-go (TUG) test. In our findings, we observed linear relationships with strong positive correlations ( R 2 > 0.9) and tight 95% confidence intervals for all gait parameters (step time, cycle time, ambulation time, and cadence). We demonstrated that clinical gait parameters and TUG mobility test timings can be accurately derived from seismographic signals, with our method exhibiting no significant differences from established clinical reference systems.

The noise levels in intensive care units have been repeatedly reported to exceed the recommended guidelines and yield negative health outcomes among healthcare professionals. However, it is unclear which sound sources within this environment are perceived as disturbing. Therefore, this study aimed to evaluate how healthcare professionals in Germany, Switzerland, and Austria perceive the sound levels and the associated sound sources within their work environment and explore sound reduction strategies. An online survey was conducted among 350 healthcare professionals working in intensive care units. The survey consisted of items on demographic and hospital data and questions about the perception of the sound levels [1 (strongly disagree) to 5 (strongly agree)], disturbance from sound sources [1 (not disturbing at all) to 5 (very disturbing)], and implementation potential, feasibility, and motivation to reduce sound reduction measures [1 (not high at all) to 5 (very high)]. Approximately 69.3% of the healthcare professionals perceived the sound levels in the ICUs as too high. Short-lasting human sounds (e.g. moans or laughs) [mean (M) ± standard deviation (SD) = 3.30 ± 0.81], devices and alarms (M ± SD = 2.67 ± 0.59), and short-lasting object sounds (M ± SD = 2.55 ± 0.68) were perceived as the most disturbing sounds. Reducing medical equipment alarms was considered to have greater implementation potential [M ± SD = 3.62 ± 0.92, t(334) = -7.30, p < 0.001], feasibility [M ± SD = 3.19 ± 0.93, t(334) = -11.02, p < 0.001], and motivation [M ± SD = 3.85 ± 0.89, t(334) = -10.10, p < 0.001] for reducing the sound levels. This study showed that healthcare professionals perceive short-lasting human sounds as most disturbing and rated reducing medical equipment alarms as the best approach to reduce the sound levels in terms of potential, feasibility, and motivation for implementation.

Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (r>0.95) with the gold standards across all assessed gait parameters except for cycle time (r=0.91). Similarly, the coefficient of determination was high (R2>0.9) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (r≥0.98,R2≥0.95). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments.

: Unilateral spatial neglectis an attention disorder frequently occurring after a right-hemispheric stroke. Neglect results in a reduction in qualityof life and performance in activities of daily living. With current technical improvements in virtual reality (VR) technology, trainingwith stereoscopic head-mounted displays (HMD) has become a promising new approach for the assessment and the rehabilitation of neglect. The focus of this pilot study was to develop and evaluate a simple visual search task in VR for HMD. The VR system was tested regarding feasibility, acceptance, and potential adverse effects in healthy controls and right-hemispheric stroke patients with and without neglect. : The VR system consisted of two main components, a head-mounted display to present the virtual environment, and a hand-held controller for the interaction with the latter. The task followed the rationale of diagnostic paper-pencil cancellation tasks; i.e., the participants were asked to search targets among distractors. However, instead of a two-dimensional setup, the targets and distractors were arranged in three dimensions, in a sphere around the subject inside its field of view. Usability and acceptance of the task, as well as the performance in the latter, were tested in 15 right-hemispheric subacute stroke patients (10 of whom with and five of whom without unilateral spatial neglect; mean age: 67.1 ± 10.5 years) and 35 age-matched healthy controls. : System usability and acceptance were rated as high both in stroke patients and healthy controls, close to the maximum score of the questionnaire scale. No relevant adverse effects occurred. There was a high correlation ( = 0.854, = 0.002) between the Center of Cancellation [an objective neglect measure) calculated from a paper-pencil cancellation task (Sensitive Neglect Test (SNT)] and the newly developed VR cancellation task. : Overall, the developed visual search task in the tested VR system is feasible, well-accepted, enjoyable, and does not evoke any significant negative effects, both for healthy controls and for stroke patients. Findings for task performance show that the ability of the VR cancellation to detect neglect in stroke patients is similar to paper-pencil cancellation tasks.

Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual’s gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (R2>0.9) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems.

Background Delirium has been long considered as a major contributor to cognitive impairments and increased mortality following a critical illness. Pharmacologic and non-pharmacologic strategies are used against delirium in the intensive care unit (ICU), despite these strategies remaining controversial. Previous studies have shown the feasibility of using virtual reality within the ICU setting, and we propose to use this technology to investigate the effect of immersive virtual reality stimulation on the incidence of delirium in the ICU. Moreover, we propose to use motion sensors to determine if patient movement patterns can lead to early prediction of delirium onset. Methods This study is conducted as a randomized clinical trial. A total of 920 critically ill patients in the ICU will participate. The control group will receive standard ICU care, whereas the intervention group will, in addition to the standard ICU care, receive relaxing 360-degree immersive virtual reality content played inside a head-mounted display with noise-cancelling headphones, three times a day. The first 100 patients, regardless of their group, will additionally have their movement patterns recorded using wearable and ambient sensors. Follow-up measurements will take place 6 months after discharge from the ICU. Discussion Delirium is widely present within the ICU setting but lacks validated prevention and treatment strategies. By providing patients with virtual reality stimulation presented inside a head-mounted display and noise-cancelling headphones, participants may be isolated from disturbances on an ICU. It is believed that by doing so, the incidence of delirium will be decrease among these patients. Moreover, identifying movement patterns associated with delirium would allow for early detection and intervention, which may further improve long-term negative outcomes associated with delirium during critical care. Trial registration ClinicalTrials.gov NCT04498585 . Registered on August 3, 2020

For patients suffering from neurodegenerative disorders, the behavior and activities of daily living are an indicator of a change in health status, and home-monitoring over a prolonged period of time by unobtrusive sensors is a promising technology to foster independent living and maintain quality of life. The aim of this pilot case study was the development of a multi-sensor system in an apartment to unobtrusively monitor patients at home during the day and night. The developed system is based on unobtrusive sensors using basic technologies and gold-standard medical devices measuring physiological (e.g., mobile electrocardiogram), movement (e.g., motion tracking system), and environmental parameters (e.g., temperature). The system was evaluated during one session by a healthy 32-year-old male, and results showed that the sensor system measured accurately during the participant’s stay. Furthermore, the participant did not report any negative experiences. Overall, the multi-sensor system has great potential to bridge the gap between laboratories and older adults’ homes and thus for a deep and novel understanding of human behavioral and neurological disorders. Finally, this new understanding could be utilized to develop new algorithms and sensor systems to address problems and increase the quality of life of our aging society and patients with neurological disorders.