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.

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.

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.

A Biomimetic Adhesive and Robust Janus Patch with Anti-Oxidative, Anti-Inflammatory, and Anti-Bacterial Activities for Tendon Repair.

Early stage oxidative stress, inflammatory response, and infection after tendon surgery are highly associated with the subsequent peritendinous adhesion formation, which may diminish the quality and function of the repaired tendon. Although various anti-inflammatory and/or antibacterial grafts have been proposed to turn the scale, most of them suffer from the uncertainty of drug-induced adverse effects, low mechanical strength, and tissue adhesiveness. Here, inspired by the tendon anatomy and pathophysiology of adhesion development, an adhesive and robust dual-layer Janus patch is developed, whose inner layer facing the operated tendon is a multifunctional electrospun hydrogel patch (MEHP), encircled further by a poly-l-lactic acid (PLLA) fibrous outer layer facing the surrounding tissue. Specifically, MEHP is prepared by gelatin methacryloyl (GelMA) and zinc oxide (ZnO) nanoparticles, which are co-electrospun first and then treated by tannic acid (TA). The inner MEHP exhibits superior mechanical performance, adhesion strength, and outstanding antioxidation, anti-inflammation, and antibacterial properties, and it can adhere to the injury site offering a favorable microenvironment for tendon regeneration. Meanwhile, the outer PLLA acts as a physical barrier that prevents extrinsic cells and tissues from invading the defect site, reducing peritendinous adhesion formation. This work presents a proof-of-concept of a drug-free graft with anisotropic adhesive and biological functions to concert the healing phases of injured tendon by alleviating incipient inflammation and oxidative damage but supporting tissue regeneration and reducing tendon adhesion in the later phase of repair and remodeling. It is envisioned that this Janus patch could offer a promising strategy for safe and efficient tendon therapy.

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).

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.

A Multilayered Mesoporous Gold Nanoarchitecture for Ultraeffective Near-Infrared Light-Controlled Chemo/Photothermal Therapy for Cancer Guided by SERS Imaging.

Surface-enhanced Raman scattering (SERS) imaging has emerged as a promising tool for guided cancer diagnosis and synergistic therapies, such as combined chemotherapy and photothermal therapy (chemo-PTT). Yet, existing therapeutic agents often suffer from low SERS sensitivity, insufficient photothermal conversion, or/and limited drug loading capacity. Herein, a multifunctional theragnostic nanoplatform consisting of mesoporous silica-coated gold nanostar with a cyclic Arg-Gly-Asp (RGD)-coated gold nanocluster shell (named RGD-pAS@AuNC) is reported that exhibits multiple "hot spots" for pronouncedly enhanced SERS signals and improved near-infrared (NIR)-induced photothermal conversion efficiency (85.5%), with a large capacity for high doxorubicin (DOX) loading efficiency (34.1%, named RGD/DOX-pAS@AuNC) and effective NIR-triggered DOX release. This nanoplatform shows excellent performance in xenograft tumor model of HeLa cell targeting, negligible cytotoxicity, and good stability both in vitro and in vivo. By SERS imaging, the optimal temporal distribution of injected RGD/DOX-pAS@AuNCs at the tumor site is identified for NIR-triggered local chemo-PTT toward the tumor, achieving ultraeffective therapy in tumor cells and tumor-bearing mouse model with 5 min of NIR irradiation (0.5 W cm-2 ). This work offers a promising approach to employing SERS imaging for effective noninvasive tumor treatment by on-site triggered chemo-PTT.

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.

Physical Fitness of Chinese Primary School Students across the Coronavirus (COVID-19) Outbreak: A Retrospective Repeated Cross-Sectional Study.

Social distancing measures against COVID-19 imposed restrictions on students that may have affected their physical health and fitness. The objective of this study was to investigate the change in physical fitness of primary school students across the coronavirus outbreaks from 2019 to 2021. This was a retrospective repeated cross-sectional study. We obtained the annual physical and fitness assessment data measured every November for all students at the same primary school in Guangzhou, China. There was a total of 6371 observations in the dataset for three years. The physical fitness of the students was evaluated with an overall physical fitness score, body mass index (BMI), lung vital capacity, physical flexibility (via a sit-and-reach test) and sports task performances (sprint, shuttle run, rope-jumping, and sit-up). Generalised estimating equations were used to determine any significant changes from 2019 to 2021, adjusted for confounders. After the COVID-19 outbreak in 2021, there was a significant elevation in BMI of 0.64 kg/m2 in 2020 and 0.39 kg/m2 in 2021 (p < 0.001). The overall physical fitness score was significantly increased by 2.1 and 4.1 points, respectively, in 2020 and 2021 (p < 0.001). Lung vital capacity and rope-jumping performance were significantly improved in both 2020 and 2021 compared with 2019, and sit-up performance was marginally significantly improved in 2020 and significantly improved in 2021. However, students demonstrated poorer flexibility and sprint and shuttle run performance in 2021 compared with 2019. A health promotion programme during and after COVID-19, including online physical education classes, television broadcasts, and a rope-jumping campaign, could account for these positive outcomes, along with the ease of administering rope-jumping and sit-ups at home.

Spine Posture, Mobility, and Stability of Top Mobile Esports Athletes: A Case Series.

Professional esports athletes spend a long time in the same sitting posture during training and competition. Mobile esports may exacerbate potential postural problems because of the closer and unsupported arms and because athletes spend more time in a forward-/flexed-head posture. Prolonged sitting in these postures carries significant health risks and may lead to musculoskeletal problems and injuries. The objective of this retrospective study is to assess the posture, mobility, and stability of the spine for professional mobile esports athletes. We collected spine-assessment data from 48 athletes participating in a top-tier league on a real-time-strategy battle-arena online game. The spinal assessment was conducted using the SpinalMouse® under upright standing and trunk flexion in addition to the Matthiass test. Measurements were converted into Idiag Scores by the SpinalMouse® software. The Idiag Posture, Idiag Mobility, and Idiag Stability scores were 62.50 (IQR: 21), 63.50 (IQR: 19.5), and 54.50 (IQR: 14.5), respectively, and were significantly lower (p < 0.001) than the reference normative value (100). Age was found to have a weak positive correlation with the posture score (ρ = 0.29, p = 0.048). Although career duration appeared to lower the scores, the association was insignificant (p > 0.05). The scores also had no significant association with body height, body mass, body mass index, and esports team (p > 0.05). It was anticipated that mobile-based esports would attenuate the biomechanics of the spine and increase the likelihood of musculoskeletal problems, such as neck and back pain.

Virtual Reality-Based Immersive Rehabilitation for Cognitive- and Behavioral-Impairment-Related Eating Disorders: A VREHAB Framework Scoping Review.

Virtual reality (VR) technology is one of the promising directions for rehabilitation, especially cognitive rehabilitation. Previous studies demonstrated successful rehabilitation in motor, cognitive, and sensorial functions using VR. The objective of this review is to summarize the current designs and evidence on immersive rehabilitation interventions using VR on cognitive- or behavioral-related eating disorders, which was mapped using a VREHAB framework. Two authors independently searched electronic databases, including PubMed, Web of Science, Scopus, CINAHL, EMBASE, and Cochrane Library. Ten (n = 10) articles were eligible for review. Treatments for anorexia nervosa and binge eating disorder/bulimia nervosa were reported through enhanced/experimental cognitive behavior therapy (ECT), cue exposure therapy (CET), and body exposure therapy (BET) via the virtual environment. Some studies reported that the VR effects were superior or comparable to traditional treatments, while the effects may last longer using VR technology. In addition, VR was perceived as acceptable and feasible among patients and therapists and could be valuable for supplementing existing therapies, relieving manpower and caregiver burdens. Future studies may consider incorporating haptic, smell, and biofeedback to improve the experience, and thus the effects of the treatments for the users.

A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study.

Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Swallow Detection with Acoustics and Accelerometric-Based Wearable Technology: A Scoping Review.

Swallowing disorders, especially dysphagia, might lead to malnutrition and dehydration and could potentially lead to fatal aspiration. Benchmark swallowing assessments, such as videofluoroscopy or endoscopy, are expensive and invasive. Wearable technologies using acoustics and accelerometric sensors could offer opportunities for accessible and home-based long-term assessment. Identifying valid swallow events is the first step before enabling the technology for clinical applications. The objective of this review is to summarize the evidence of using acoustics-based and accelerometric-based wearable technology for swallow detection, in addition to their configurations, modeling, and assessment protocols. Two authors independently searched electronic databases, including PubMed, Web of Science, and CINAHL. Eleven (n = 11) articles were eligible for review. In addition to swallowing events, non-swallowing events were also recognized by dry (saliva) swallowing, reading, yawning, etc., while some attempted to classify the types of swallowed foods. Only about half of the studies reported that the device attained an accuracy level of >90%, while a few studies reported poor performance with an accuracy of <60%. The reviewed articles were at high risk of bias because of the small sample size and imbalanced class size problem. There was high heterogeneity in assessment protocol that calls for standardization for swallowing, dry-swallowing and non-swallowing tasks. There is a need to improve the current wearable technology and the credibility of relevant research for accurate swallowing detection before translating into clinical screening for dysphagia and other swallowing disorders.