Evaluation of Vibration Analysis to Assess Bone Mineral Density in Children

The effectiveness of vibration analysis to assess bone mineral density (BMD) in children with suspected reduction in bone density was studied. A system was designed that measured the ulna's vibration responses in vivo. The system was evaluated on the ulnae of 48 children (mean age=12.0, std=3.5 years), 31 of whom had been confirmed to have osteogenesis imperfecta (OI). All children had dual energy X-ray absorptiometry (DXA) scan as part of their routine clinical care and vibration analysis was performed on the same day. Frequency spectra of the ulnae's vibration responses were obtained and processed by principal component analysis. Four main principal components were selected and together with age, sex and right hand ulna's length were used in a regression analysis to estimate BMD. Regression analysis was repeated using the children's leave-one-out and partitioning methods. The percentage similarity and correlation between the DXA-derived and vibration analysis estimated BMDs using the leave-one-out were 80.34% and 0.59 and for partitioning were 74.2% and 0.64 respectively. There was correlation between vibration analysis BMD readings and those derived from DXA however a larger study will be needed to better establish the extent to which vibration analysis can assist in assessing bone density in clinical environments

Assessing material densities by vibration analysis and independent component analysis

The aim of this study was to investigate vibration analysis and independent component analysis (ICA) to assess the density of multiple materials making up a single structure. Density is important as it reveals information about physical properties of materials. The density of a single material can be determined from the relationship between its mass and volume. However, when a structure consists of multiple materials, identification of their individual densities from the structure is complicated. Vibration analysis is a technique that reveals information about an object’s physical properties such as its density. The investigation was carried out using a plastic test tube filled separately with three liquids of known densities; water, Chloroform and Methanol. Vibration was inducted into the tube, through an electronic system that produced a single impact at a predefined location on the tube. The resulting vibration signals were recorded using two vibration sensors placed on the tube. A signal source separation technique called ICA was used to obtain the vibration effects of the liquid and the tube. The power spectral densities (PSD) of ICA extracted vibration signals were examined. The frequency of the largest peak in the PSD was related to the liquid’s density under test. The study indicated that vibration analysis may be effective in assessing materials’ densities in a structure that contains multiple materials, however a larger study is needed to explore the findings

Vibration analysis as a tool for bone mineral density assessment in children

Dual energy x-ray absorptiometry (DXA) is the current gold standard for assessing bone mineral density (BMD). However DXA uses ionising radiation and has some limitations for assessing BMD in children. In this study, vibration analysis is introduced as a new method to assess BMD in children

A smart sleep apnea detection service

Over the last decades, sleep apnea has become one of the most prevalent healthcare problems. Diagnosis and treatment monitoring are key elements when it comes to addressing this public health crisis. A problem for diagnosis and treatment monitoring is a chronic lack of specialized lab facilities which results in long waiting times or the absence of such services. This can delay appropriate treatment which might prolong living with sleep apnea and thereby leading to health issues due to poor sleep. We address this problem with a smart sleep apnea detection service based on Heart Rate Variably (HRV) analysis. The service incorporates Internet of Medical Things (IoMT), mobile technology (MT), and advanced Artificial Intelligence (AI). The measured signals are relayed by a smart phone into a cloud server via IoMT protocols. Once the data is stored in the cloud server, a deep learning (DL) algorithm is used to detect sleep apnea events. Detecting these events can trigger a warning message which is sent to care givers. The smart sleep apnea detection service is beneficial for patients who find it difficult to access specialized lab facilities for diagnosis or treatment monitoring. Furthermore, the system prolongs the observation period, which can improve the diagnosis accuracy. The resource requirements for the proposed service are lower when compared to clinical facilities, this might lead to significant cost savings for healthcare providers

Environmental benefits of sleep apnoea detection in the home environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment

Spectral analysis of bone low frequency vibration signals

The purpose of this study was to use frequency spectrum analysis to determine the effects of skin and muscle on the bone’s low frequency vibration signals recorded from vibration sensors placed on the skin. A setup was developed that allowed low frequency vibration signals to be recorded. Tests were performed on a sample of 8 turkey legs in vitro, using four vibration sensors placed on the skin, muscle (i.e. leg with the skin removed) and bone (i.e. leg with skin and muscle removed). It was found that bone’s vibration signals could be recorded from sensors placed on the skin, but there were changes in their magnitudes and vibration frequencies. There was also a direct relationship between the main frequency of bone’s vibration and its mass/volume ratio. This is a preliminary study. The ultimate aim of this study (to be achieved in further work) is to predict fracture risk and target therapy appropriately

Correlation analysis of bone vibration frequency and its mass: volume ratio

Background: Vibration analysis is a well-established technique in industry to analyse materials physical properties. The application to bone’s physical properties is unclear. This study investigated the relationship between bone vibration frequency and mass:volume ratio (ρ). Methods: We used eight turkey bones (tibio tursus). Following soft tissue removal, a 12 cm diaphyseal section was isolated, marrow removed using a water jet and the bones dried at 25 °C for 1 week. Bone volume was determined by water displacement, and mass by weighing. Bones were held in a vice at one end in a consistent manner and vibrated either using a miniature vibration motor (continuous vibration approach) or a miniature electronic hammer (impulse vibration approach). Vibration signals were recorded using CM-01B sensor. For the impulse approach, the highest peak in the magnitude frequency spectrum of the vibration signal (F) was used to determine the bone vibration frequency. For the continuous vibration approach, the difference (FD) between the motor vibration frequency and the bone vibration frequency (obtained from the highest peak in the magnitude frequency spectrum) was used. Results: The impulse approach correlated more strongly with ρ than did the continuous approach (correlation of F with ρ 0.57 vs 0.38 respectively). Summary: This study suggests that vibration analysis may be a valuable technique in assessing bone mass/volume properties. This was a preliminary study and we are currently conducting a larger study to explore the findings further

Correlation analysis of bone vibration frequency and bone mineral density in children

Vibration frequencies of ulnae in 19 children aged between 10 and 15 years old were correlated with their bone mineral densities (BMDs) determined using dual energy x-ray absorptiometry. A correlation analysis method to identify the best frequency range for the vibration analysis was developed. The correlation of subjects' bone vibration frequencies in the identified frequency range with their BMDs were 0.71 (for first order polynomial) and 0.81 (for second order polynomial). This is an initial study, based on a small group of subjects. We are currently continuing the work on a larger number of subjects to confirm the findings