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Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars—three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors. [ABSTRACT FROM AUTHOR]

Dementia is a long‐term and progressive syndrome that not only influences the person with dementia (PWD) but also the caregiver. However, informal caregivers are not always empathic and understand the symptoms of dementia, leading to destructive caregiving relationships and poor quality of caregiving. VR‐based simulation interventions can provide a more realistic and memorable learning experience for caregivers to walk in PWDs' shoes. This review aimed to provide practitioners and researchers with insights on developing and/or adopting an effective VR‐based simulation intervention for enhancing the empathy of informal caregivers of PWD. A mixed‐methods systematic review was conducted. Quantitative, qualitative, and mixed‐methods studies were searched from MEDLINE, PsycINFO, CINAHL, Scopus, Embase, and Cochrane Library updating. Standard JBI critical appraisal instruments were used for the quality appraisal. A convergent segregated approach was used to synthesize and integrate the data. A total of seven studies were included. Inconsistent quantitative results were reported on the effects of VR‐based simulation on empathy enhancement. Significant effects were reported on knowledge of dementia and emotion‐focused coping strategies. Two themes were generated from the qualitative studies, including "Informal caregivers gained better insight into problems encountered by older people with dementia" and "Thinking from the perspective of older people with dementia, leading to changes in attitudes and behaviours towards dementia". The qualitative synthesized evidence showed that informal caregivers gained better insight into problems encountered by PWD, but the quantitative synthesized results are inconsistent. Yet, informal caregivers experienced a change in attitude by thinking from the perspective of PWD. [ABSTRACT FROM AUTHOR]

Dysphagia is a common geriatric syndrome that might induce serious complications and death. Standard diagnostics using the Videofluoroscopic Swallowing Study (VFSS) or Fiberoptic Evaluation of Swallowing (FEES) are expensive and expose patients to risks, while bedside screening is subjective and might lack reliability. An affordable and accessible instrumented screening is necessary. This study aimed to evaluate the classification performance of Transformer models and convolutional networks in identifying swallowing and non-swallowing tasks through depth video data. Different activation functions (ReLU, LeakyReLU, GELU, ELU, SiLU, and GLU) were then evaluated on the best-performing model. Sixty-five healthy participants (n = 65) were invited to perform swallowing (eating a cracker and drinking water) and non-swallowing tasks (a deep breath and pronouncing vowels: "/eɪ/", "/iː/", "/aɪ/", "/oʊ/", "/u:/"). Swallowing and non-swallowing were classified by Transformer models (TimeSFormer, Video Vision Transformer (ViViT)), and convolutional neural networks (SlowFast, X3D, and R(2+1)D), respectively. In general, convolutional neural networks outperformed the Transformer models. X3D was the best model with good-to-excellent performance (F1-score: 0.920; adjusted F1-score: 0.885) in classifying swallowing and non-swallowing conditions. Moreover, X3D with its default activation function (ReLU) produced the best results, although LeakyReLU performed better in deep breathing and pronouncing "/aɪ/" tasks. Future studies shall consider collecting more data for pretraining and developing a hyperparameter tuning strategy for activation functions and the high dimensionality video data for Transformer models. [ABSTRACT FROM AUTHOR]

Radiographic Cobb's angle is the gold standard for evaluation of spinal curvature, however, X-ray is ionising. In contrast, ultrasound is non-ionising and inexpensive. However, no study has reported the reliability and accuracy of ultrasound on sagittal curvature analysis. Ultrasound and X-ray scanning were conducted on 16 sets of spine phantoms with different deformities. Intra-rater and inter-rater reliability, correlations, mean absolute differences (MAD) and linear regression of ultrasound spinous process angles (USSPA), X-ray spinous process angles (XSPA) and X-ray Cobb's angles (XCA) together with the intra-operator reliability of USSPA were investigated. In addition, USSPA and XCA of five AIS subjects were obtained using the ultrasound system. In the phantom study, excellent intra-rater and inter-rater reproducibility for the three angles and excellent intra-operator reproducibility for USSPA were demonstrated. Good to moderate or better correlations were obtained among the angles. All three angles indicated positive linear relationships with MAD ≤ 6.0°. The results of the preliminary study demonstrated a high intra-reliability for the ultrasound measurements. The measured difference between the USSPA and XCA methods was 6.3° ± 5.4°. The results showed that ultrasound is feasible for measuring sagittal curvature and has the potential for monitoring the curve progression and evaluating sagittal spinal profiles. [ABSTRACT FROM AUTHOR]

The accuracy of sleep posture assessment in standard polysomnography might be compromised by the unfamiliar sleep lab environment. In this work, we aim to develop a depth camera-based sleep posture monitoring and classification system for home or community usage and tailor a deep learning model that can account for blanket interference. Our model included a joint coordinate estimation network (JCE) and sleep posture classification network (SPC). SaccpaNet (Separable Atrous Convolution-based Cascade Pyramid Attention Network) was developed using a combination of pyramidal structure of residual separable atrous convolution unit to reduce computational cost and enlarge receptive field. The Saccpa attention unit served as the core of JCE and SPC, while different backbones for SPC were also evaluated. The model was cross-modally pretrained by RGB images from the COCO whole body dataset and then trained/tested using dept image data collected from 150 participants performing seven sleep postures across four blanket conditions. Besides, we applied a data augmentation technique that used intra-class mix-up to synthesize blanket conditions; and an overlaid flip-cut to synthesize partially covered blanket conditions for a robustness that we referred to as the Post-hoc Data Augmentation Robustness Test (PhD-ART). Our model achieved an average precision of estimated joint coordinate (in terms of PCK@0.1) of 0.652 and demonstrated adequate robustness. The overall classification accuracy of sleep postures (F1-score) was 0.885 and 0.940, for 7- and 6-class classification, respectively. Our system was resistant to the interference of blanket, with a spread difference of 2.5%.

Purpose: Evaluation of the internal biomechanics of the foot-and-ankle complex is challenging for the prescription of orthosis particularly for midfoot arthritis patients in which the joint condition is crucial., Methods: Using computational modeling and design optimization techniques, the objective of this study was to compare the biomechanical functions among different combinations of design factors using computer simulation. A finite element foot model was reconstructed from a midfoot arthritis patient. Orthotic designs with 3 levels for each of the 3 design factors (arch height, lateral wedge angle, and insole stiffness) contributed to 9 configurations using a fractional factorial design were tested., Results: An increase in peak plantar stress of the midfoot was facilitated by a medium arch height and wedge angle, and stiffest insole material, notwithstanding the combination neither reduced the peak plantar stress of other foot regions nor was consistent with the combination that minimized the stress of the articular cartilage., Conclusions: Insole with high arch (H = 30 mm), low stiffness (E = 1.0 MPa), and medium wedge angle (A = 5°) could minimize the stress of the cartilage at the arthritic joint (primary outcome) and could be beneficial to the patients. Also, insole stiffness predominantly influenced cartilage stress. However, secondary outcomes including the stress of the navicular and medial cuneiform and the regional plantar stress did not produce the same solution. Future studies can consider a patient-specific loading profile to further the investigation on the stabilizing effect and the attenuation of load transfer induced by the insole.