Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).

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.

SaccpaNet: A Separable Atrous Convolution-based Cascade Pyramid Attention Network to Estimate Body Landmarks Using Cross-modal Knowledge Transfer for Under-blanket Sleep Posture Classification.

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%.

Biomechanical role of bone grafting for calcaneal fracture fixation in the presence of bone defect: A finite element analysis.

BACKGROUND: The purpose of this study was to compare the biomechanical stress and stability of calcaneal fixations with and without bone defect, before and after bone grafting, through a computational approach. METHODS: A finite element model of foot-ankle complex was reconstructed, impoverished with a Sanders III calcaneal fracture without bone defect and with moderate and severe bone defects. Plate fixations with and without bone grafting were introduced with walking stance simulated. The stress and fragment displacement of the calcaneus were evaluated. FINDINGS: Moderate and severe defect increased the calcaneus stress by 16.11% and 32.51%, respectively and subsequently decreased by 10.76% and 20.78% after bone grafting. The total displacement was increased by 3.99% and 24.26%, respectively by moderate and severe defect, while that of posterior joint facet displacement was 86.66% and 104.44%. The former was decreased by 25.73% and 35.96% after grafting, while that of the latter was reduced by 88.09% and 84.78% for moderate and severe defect, respectively. INTERPRETATION: Our finite element prediction supported that bone grafting for fixation could enhance the stability and reduce the risk of secondary stress fracture in cases of bone defect in calcaneal fracture.

Virtual reality-based simulation intervention for enhancing the empathy of informal caregivers of people with dementia: A mixed-methods systematic review.

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.

Computational Biomechanics of Sleep: A Systematic Mapping Review.

Biomechanical studies play an important role in understanding the pathophysiology of sleep disorders and providing insights to maintain sleep health. Computational methods facilitate a versatile platform to analyze various biomechanical factors in silico, which would otherwise be difficult through in vivo experiments. The objective of this review is to examine and map the applications of computational biomechanics to sleep-related research topics, including sleep medicine and sleep ergonomics. A systematic search was conducted on PubMed, Scopus, and Web of Science. Research gaps were identified through data synthesis on variants, outcomes, and highlighted features, as well as evidence maps on basic modeling considerations and modeling components of the eligible studies. Twenty-seven studies (n = 27) were categorized into sleep ergonomics (n = 2 on pillow; n = 3 on mattress), sleep-related breathing disorders (n = 19 on obstructive sleep apnea), and sleep-related movement disorders (n = 3 on sleep bruxism). The effects of pillow height and mattress stiffness on spinal curvature were explored. Stress on the temporomandibular joint, and therefore its disorder, was the primary focus of investigations on sleep bruxism. Using finite element morphometry and fluid-structure interaction, studies on obstructive sleep apnea investigated the effects of anatomical variations, muscle activation of the tongue and soft palate, and gravitational direction on the collapse and blockade of the upper airway, in addition to the airflow pressure distribution. Model validation has been one of the greatest hurdles, while single-subject design and surrogate techniques have led to concerns about external validity. Future research might endeavor to reconstruct patient-specific models with patient-specific loading profiles in a larger cohort. Studies on sleep ergonomics research may pave the way for determining ideal spine curvature, in addition to simulating side-lying sleep postures. Sleep bruxism studies may analyze the accumulated dental damage and wear. Research on OSA treatments using computational approaches warrants further investigation.

Training effects of set- and repetition-interval rest time on recumbent-boxing exercise: Could virtual reality improve further?

This study examined the influence of set-interval and repetition-interval rest time of virtual reality (VR) boxing game in supine-lying posture. Fifty healthy middle-aged adults were randomly assigned into VR and non-VR groups to perform six different exercise protocols with varying set-interval and repetition-interval rest times (S0R0, S0R1/3, S0R2/3, S40R0, S40R1/3, and S40R2/3). Analysis on the non-VR group showed significant differences between exercise protocols for average heart rate (p < 0.001), maximum ventilation volume (p < 0.001), respiratory quotient (p < 0.001), oxygen pulse (p < 0.001), and excess post-exercise oxygen consumption (EPOC) (p = 0.003). VR appeared to have no further improvement on physical training effects in middle-aged adults, while the participants reported negative experience that might be associated with the over-exertion. Future study might need to explore game design elements that can accommodate high-exertion exercises.

Sonoelastography for Testicular Tumor Identification: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.

The objective of this review was to summarize the applications of sonoelastography in testicular tumor identification and inquire about their test performances. Two authors independently searched English journal articles and full conference papers from CINAHL, Embase, IEEE Xplore®, PubMed, Scopus, and Web of Science from inception and organized them into a PIRO (patient, index test, reference test, outcome) framework. Eleven studies (n = 11) were eligible for data synthesis, nine of which (n = 9) utilized strain elastography and two (n = 2) employed shear-wave elastography. Meta-analyses were performed on the distinction between neoplasm (tumor) and non-neoplasm (non-tumor) from four study arms and between malignancy and benignity from seven study arms. The pooled sensitivity of classifying malignancy and benignity was 86.0% (95%CI, 79.7% to 90.6%). There was substantial heterogeneity in the classification of neoplasm and non-neoplasm and in the specificity of classifying malignancy and benignity, which could not be addressed by the subgroup analysis of sonoelastography techniques. Heterogeneity might be associated with the high risk of bias and applicability concern, including a wide spectrum of testicular pathologies and verification bias in the reference tests. Key technical obstacles in the index test were manual compression in strain elastography, qualitative observation of non-standardized color codes, and locating the Regions of Interest (ROI), in addition to decisions in feature extractions. Future research may focus on multiparametric sonoelastography using deep learning models and ensemble learning. A decision model on the benefits-risks of surgical exploration (reference test) could also be developed to direct the test-and-treat strategy for testicular tumors.

Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy.

Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer's disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7-173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18-449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).

Modified lumbo-pelvic exercise to alleviate mild stress urinary incontinence in middle-aged females.

Urinary incontinence is one of the common clinical problems of females passing middle age. Traditional pelvic floor muscle training to alleviate urinary incontinence is too dull and unpleasant. Therefore, we were motivated to purpose a modified lumbo-pelvic exercise training incorporating simplified dancing components with pelvic floor muscle training. The objective of this study was to evaluate the 16-week modified lumbo-pelvic exercise program that incorporated dance and abdominal drawing-in maneuvers. Middle-aged females were randomly assigned into the experimental (n = 13) and control (n = 11) groups. Compared to the control group, the exercise group significantly reduced body fat, visceral fat index, waistline, waist-hip ratio, perceived incontinence score, frequency of urine leakage, and pad testing index (p < 0.05). In addition, there were significant improvements in pelvic floor function, vital capacity, and muscle activity of the right rectus abdominis (p < 0.05). This indicated that the modified lumbo-pelvic exercise program can promote benefits of physical training and alleviate urinary incontinence in middle-aged females.

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System.

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

Forefoot Function after Hallux Valgus Surgery: A Systematic Review and Meta-Analysis on Plantar Load Measurement.

While hallux valgus (HV) surgeries are useful for correcting skeletal alignment problems, their effects on plantar load, which reflects forefoot functions, are less understood. The objective of this study is to conduct a systematic review and meta-analysis on the plantar load change after HV surgeries. A systematic search of Web of Science, Scopus, PubMed, CENTRAL, EMBASE, and CINAHL was performed. Studies that assessed the pre- and post-operative plantar pressure of HV patients undergoing surgeries and reported load-related parameters over the hallux, medial metatarsal, and/or central metatarsal regions were included. Studies were appraised by using the modified NIH quality assessment tool for before-after study. Studies suitable for meta-analysis were pooled with the random-effects model, using the standardized mean difference of the before-after parameters as an effect measure. Twenty-six studies containing 857 HV patients and 973 feet were included for the systematic review. Meta-analysis was conducted on 20 of them, and most studies did not favor HV surgeries. Overall, HV surgeries reduced the plantar load over the hallux region (SMD -0.71, 95% CI, -1.15 to -0.26), indicating that forefoot function worsened after surgeries. For the other five outcomes, the overall estimates were not statistically significant, indicating that surgeries did not improve them either. There was substantial heterogeneity among the studies, which in most cases could not be resolved by pre-planned subgroup analyses by surgical classification, year of publication, median age of patients, and length of follow-up. Sensitivity analysis removing lower-quality studies showed that the load integrals (impulse) over the central metatarsal region significantly increased (SMD 0.27, 95% CI, 0 to 0.53), indicating that surgeries increased the risk of transfer metatarsalgia. There is no solid evidence that HV surgeries could improve forefoot functions from a biomechanical point perspective. Currently available evidence even suggests that surgeries might reduce the plantar load over the hallux and adversely affect push-off function. The reasons behind and the effectiveness of alternative surgical methods warrant further investigation.

A Comprehensive Assessment Protocol for Swallowing (CAPS): Paving the Way towards Computer-Aided Dysphagia Screening.

Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry (saliva) swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing (e.g., yawning, coughing, speaking, etc.). The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening.

Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.

Training and retention effects of paced and music-synchronised walking exercises on pre-older females: an interventional study.

BACKGROUND: Physical activity at pre-older ages (55-64 years) can greatly affect one's physical fitness, health, physical-activity behaviour, and quality of life at older ages. The objective of this study was to conduct a 24-week walking-exercise programme among sedentary pre-older females and investigate the influence of different walking cadences on cardiorespiratory fitness and associated biomarkers. METHODS: A total of 78 pre-older sedentary female participants were recruited and randomly assigned to normal (n = 36), paced (n = 15), music-synchronised (n = 15) walking, and no-exercise control (n = 12) groups, respectively. The normal, paced, and music-synchronised walking groups walked at a cadence of 120 steps/min, 125 steps/min, and 120-128 steps/min, respectively, under supervised conditions. Anthropometric characteristics, step length, nutrient intake, blood pressure and composition, and cardiorespiratory fitness were measured at baseline, the 12th week of the programme, the 24th week of the programme (completion), and after a 12-week retention period, which began immediately upon completion of the programme and did not feature any supervised exercises. RESULTS: All walking conditions improved high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, step length, maximum oxygen consumption (VO2max), and oxidative capacity at anaerobic threshold (all P < 0.001); however, after the 12-week retention period only the training effects of HDL-C (P < 0.05) and VO2max (P < 0.05) remained robust. Additionally, music-synchronised walking was found to reduce the fat ratio (P = 0.031), while paced walking was found to reduce body mass (P = 0.049). CONCLUSIONS: The significant pre-post changes in health-related outcomes across the 24-week walking intervention, including improved blood composition, longer step length, and better cardiorespiratory capacity, show that this intervention is promising for improving health and fitness. When, during the retention period, the participants resumed their usual lifestyles without supervised exercise, most physiological biomarkers deteriorated. Thus, for sedentary middle-aged females, persistent behavioural change is necessary to retain the health benefits of physical exercise.

Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model.

Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the anatomical landmark feature through an attention strategy. The system used an integrated visible light and depth camera. Deep learning models (ResNet-34, EfficientNet B4, and ECA-Net50) were trained using depth images. We compared the models with and without an anatomical landmark coordinate input generated with an open-source pose estimation model using visible image data. We recruited 120 participants to perform seven major sleep postures, namely, the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures under four blanket conditions, including no blanket, thin, medium, and thick. A data augmentation technique was applied to the blanket conditions. The data were sliced at an 8:2 training-to-testing ratio. The results showed that ECA-Net50 produced the best classification results. Incorporating the anatomical landmark features increased the F1 score of ECA-Net50 from 87.4% to 92.2%. Our findings also suggested that the classification performances of deep learning models guided with features of anatomical landmarks were less affected by the interference of blanket conditions.

Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review.

Agitated behaviour among elderly people with dementia is a challenge in clinical management. Wrist accelerometry could be a versatile tool for making objective, quantitative, and long-term assessments. The objective of this review was to summarise the clinical application of wrist accelerometry to agitation assessments and ways of analysing the data. Two authors independently searched the electronic databases CINAHL, PubMed, PsycInfo, EMBASE, and Web of Science. Nine (n = 9) articles were eligible for a review. Our review found a significant association between the activity levels (frequency and entropy) measured by accelerometers and the benchmark instrument of agitated behaviour. However, the performance of wrist accelerometry in identifying the occurrence of agitation episodes was unsatisfactory. Elderly people with dementia have also been monitored in existing studies by investigating the at-risk time for their agitation episodes (daytime and evening). Consideration may be given in future studies on wrist accelerometry to unifying the parameters of interest and the cut-off and measurement periods, and to using a sampling window to standardise the protocol for assessing agitated behaviour through wrist accelerometry.

Modified Ni-Nail and C-Nail systems for intra-articular fractures of the calcaneus: A biomechancial study.

OBJECTIVES: We have proposed a novel intramedullary nail (Ni-Nail) by incorporating a sustentaculum tali screw to improve the fixation stability of minimally invasive treatment for calcaneal fractures. This study aimed to evaluate the biomechanical characters of the Ni-Nail system and compare it with traditional C-Nail system. METHODS: A finite element model of a Sanders type-IIIAB calcaneal fracture was reconstructed and fixed using two intramedullary nail systems, which was validated by a cadaver study. A vertical loading of 700 N was applied to the subtalar joint surfaces, and 525 N Achilles tendon tension was applied to the superior border of the Achilles tuberosity. The von Mises stresses and fracture displacements of both fixation models were evaluated. RESULTS: The maximum von Mises stress of the screws of Ni-Nail and C-Nail were 27.92 MPa and 57.42 MPa, respectively, while that of the main nail were 67.44 MPa and 53.01 MPa. In addition, the maximum fracture displacement of the Ni-Nail was larger than that of C-Nail by 15.6% (0.37 mm vs.0.32 mm). CONCLUSIONS: Our static simulation analysis showed that both Ni-Nail and C-Nail demonstrated similar biomechanical stability for calcaneal fixation. The Ni-Nail features a simple structure that is easier to operate and less traumatizing. Future studies may consider to further evaluate the clinical effectiveness by clinical trials and follow-ups.

The Influence of Mattress Stiffness on Spinal Curvature and Intervertebral Disc Stress-An Experimental and Computational Study.

Sleeping support systems can influence spinal curvature, and the misalignment of the spinal curvature can lead to musculoskeletal problems. Previous sleep studies on craniocervical support focused on pillow variants, but the mattress supporting the pillow has rarely been considered. This study used a cervical pillow and three mattresses of different stiffnesses, namely soft, medium, and hard, with an indentation load deflection of 20, 42, and 120 lbs, respectively. A novel electronic curvature measurement device was adopted to measure the spinal curvature, whereby the intervertebral disc loading was computed using the finite element method. Compared with the medium mattress, the head distance increased by 30.5 ± 15.9 mm, the cervical lordosis distance increased by 26.7 ± 14.9 mm, and intervertebral disc peak loading increased by 49% in the soft mattress environment. Considering that the pillow support may increase when using a soft mattress, a softer or thinner pillow is recommended. The head distance and cervical lordosis distance in the hard mattress environment were close to the medium mattress, but the lumbar lordosis distance reduced by 10.6 ± 6.8 mm. However, no significant increase in intervertebral disc loading was observed, but contact pressure increased significantly, which could cause discomfort and health problems.