BIOMED Research Publications

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    PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2023-12) Aarotale, Parshuram N.; Hill, Twyla J.; Rattani, Ajita
    Due to an alarming trend related to obesity affecting 93.3 million adults in the United States alone, body mass index (BMI) and body weight have drawn significant interest in various health monitoring applications. Consequently, several studies have proposed self-diagnostic facial image-based BMI prediction methods for healthy weight monitoring. These methods have mostly used convolutional neural network (CNN) based regression baselines, such as VGG19, ResNet50, and EfficientNetB0, for BMI prediction from facial images. However, the high computational requirement of these heavy-weight CNN models limits their deployment to resource-constrained mobile devices, thus deterring weight monitoring using smartphones. This paper aims to develop a lightweight facial patch-based ensemble (PatchBMI-Net) for BMI prediction to facilitate the deployment and weight monitoring using smartphones. Extensive experiments on BMI-annotated facial image datasets suggest that our proposed PatchBMI-Net model can obtain Mean Absolute Error (MAE) in the range [3.58, 6.51] with a size of about 3.3 million parameters. On cross-comparison with heavyweight models, such as ResNet-50 and Xception, trained for BMI prediction from facial images, our proposed PatchBMI-Net obtains equivalent MAE along with the model size reduction of about 5.4x and the average inference time reduction of about 3x when deployed on Apple-14 smartphone. Thus, demonstrating performance efficiency as well as low latency for on-device deployment and weight monitoring using smartphone applications.
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    Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals
    (IEEE Computer Society, 2023-10) Aarotale, Parshuram N.; Rattani, Ajita
    Electrocardiograms (ECGs), a medical monitoring technology recording cardiac activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is based on the analysis of the deformation of the signal shapes due to irregular heart rates associated with heart diseases. Due to the infeasibility of manual examination of large volumes of ECG data, this paper aims to propose an automated AI-based system for ECG-based arrhythmia classification. To this front, a deep-learning-based solution has been proposed for ECG-based arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10 sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from PhysioNet with four different types of arrhythmias were used. The sampling frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was estimated and stacked row-wise to obtain a single image from 12 channels, resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF scalograms are fed to the pretrained convolutional neural network (CNN), 1D CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia classification. The fine-tuned CNN models obtained the best test accuracy of about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia classification.
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    Influence of Metal Processing on Microstructure and Properties: Implications for Biodegradable Metals: A Mini Review
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09) Jaraba, Khulud; Mahapatro, Anil
    Biodegradable metallic alloys are currently being explored extensively for use in temporary implant applications, since the prolonged existence of implants within the body has been linked with health complications and metal toxicity. There are many metal alloy fabrication methods available in the industrial, aerospace, and biomedical fields; some of them have more advanced techniques and specialized equipment than others. Past studies have shown that the performances of materials is greatly affected by the concentration of alloying elements and the metal processing techniques used. However, the impact each fabrication method has on the chemical and mechanical properties of the material is not fully understood; this lack of knowledge limits the advancement of the field of biodegradable metals. This review provides a general introduction to biodegradable metals and their applications and then aims to give a broad overview of the influence of metal processing on the microstructure and properties of metal alloys. The possible implications of these fabrication methods for the biodegradable metals are discussed.
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    Modeling of Human-Exoskeleton Alignment and Its Effect on the Elbow Flexor and Extensor Muscles during Rehabilitation
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09) Rincon, Clarissa; Delgado, Pablo; Hakansson, Nils A.; Yihun, Yimesker S.
    Human-exoskeleton misalignment could lead to permanent damages upon the targeted limb with long-term use in rehabilitation. Hence, achieving proper alignment is necessary to ensure patient safety and an effective rehabilitative journey. In this study, a joint-based and task-based exoskeleton for upper limb rehabilitation were modeled and assessed. The assessment examined and quantified the misalignment present at the elbow joint as well as its effects on the main flexor and extensor muscles' tendon length during elbow flexion-extension. The effects of the misalignments found for both exoskeletons resulted to be minimal in most muscles observed, except the anconeus and brachialis. The anconeus muscle demonstrated a relatively higher variation in tendon length with the joint-based exoskeleton misalignment, indicating that the task-based exoskeleton is favored for tasks that involve this particular muscle. Moreover, the brachialis demonstrated a significantly higher variation with the task-based exoskeleton misalignment, indicating that the joint-based exoskeleton is favored for tasks that involve the muscle.
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    Effect of simulated space conditions on functional connectivity
    (International Academic Express, 2022-04-01) Aarotale, Parshuram N.; Desai, Jaydip
    Long duration spaceflight missions can affect the cognitive and behavioral activities of astronauts due to changes in gravity. The microgravity significantly impacts the central nervous system physiology which causes the degradation in the performance and lead to potential risk in the space exploration. The aim of this study was to evaluate functional connectivity at simulated space conditions using an unloading harness system to mimic the body-weight distribution related to Earth, Mars, and International Space Station. A unity model with six directional arrows to imagine six different motor imagery tasks associated with arms and legs were designed for the Oculus Rift S virtual reality headset for testing. An Electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) signals were recorded from 10 participants in the distributed weight conditions related to Earth, Mars, and International Space station using the g.Nautilus fNIRS system at sampling rate of 500 Hz. The magnitude squared coherence were estimated from left vs right hemisphere of the brain that represents functional connectivity. The EEG coherence was the higher which shows the strong functional connectivity and fNIRS coherence was lower shows weak functional connectivity between left vs right hemisphere of the brain, during all the tasks and trials irrespective of the simulated space conditions. Further analysis of functional connectivity needed between the intra-regions of the brain.