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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]