Automated Shoulder Implant Manufacturer Detection using Encoder Decoder based Classifier from X-ray Images.

Total shoulder arthroplasty is the process of replacing the damaged ball and socket joint in the shoulder with a prosthesis made with polyethylene and metal components. The prosthesis helps to restore the normal range of motion and reduce pain, enabling the patient to return to their daily activities. These implants may need to be replaced over the years due to damage or wear and tear.

An Efficient Service-based System for Hierarchical Human Activity Sensing.

In this paper, we propose an end-to-end system, based on SEnsing as Service (SEAS) model, which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. Thus, reducing the cost of data transfer and CPU usage. We also propose a classification algorithm as a part of the system to recognize Activities of Daily Living (ADL).

CigTrak: Smartwatch-Based Accurate Online Smoking Puff & Episode Detection with Gesture-Focused Windowing for CNN.

Wearable-based motion sensing solutions are capable of automatically detecting and tracking individual smoking puffs and/or episodes to aid the users in their journey of smoking cessation. But they are either obtrusive to use, perform with a low accuracy, or have questionable ability of running fully on a low-power device like a smartwatch, all affecting their widespread adoption. We propose 'CigTrak', a novel pipeline for an accurate smoking puff and episode detection using 6-DoF motion sensor on a smartwatch.

3D Cardiac Substructures Segmentation from CMRI using Generative Adversarial Network (GAN).

Cardiac magnetic resonance imaging (CMRI) improves the diagnosis of cardiovascular diseases by providing images at high spatio-temporal resolution helping physicians in providing correct treatment plans. Segmentation and identification of various substructures of the heart at different cardiac phases of end-systole and end-diastole helps in the extraction of ventricular function information such as stroke volume, ejection fraction, myocardium thickness, etc. Manual delineation of the substructures is tedious, time-consuming, and error-prone.

Cardiac Anomaly Detection from Cine MRI Images using Physiological Features and Random Forest Classifier.

Computer-aided diagnosis (CAD) with cine MRI is a foremost research topic to enable improved, faster, and more accurate diagnosis of cardiovascular diseases (CVD). However, current approaches that use manual visualization or conventional clinical indices can lack accuracy for borderline cases. Also, manual visualization of 3D/4D MR data is time-consuming and expert-dependent.

Synthetic PPG Signal Generation to Improve Coronary Artery Disease Classification: Study With Physical Model of Cardiovascular System.

This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using a physical model of the cardiovascular system to improve classifier performance with a combination of synthetic and real data. The physical model is an in-silico cardiac computational model, consisting of a four-chambered heart with electrophysiology, hemodynamic, and blood pressure auto-regulation functionality. Starting with a small number of measured PPG data, the cardiac model is used to synthesize healthy as well as PPG time-series pertaining to coronary artery disease (CAD) by varying pathophysiological parameters.

Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation that leads to sudden cardiac death (SCD). WCD are frequently prescribed to patients deemed to be at high arrhythmic risk but the underlying pathology is potentially reversible or to those who are awaiting an implantable cardioverter-defibrillator. WCD is programmed to detect appropriate arrhythmic events and generate high energy shock capable of depolarizing the myocardium and thus re-initiating the sinus rhythm.

Walking Pole Gait to Reduce Joint Loading post Total Knee Athroplasty: Musculoskeletal modeling Approach.

Excessive knee contact loading is precursor to osteoarthritis and related knee ailment leading to knee athroplasty. Reducing contact loading through gait modifications using assisted pole walking offers noninvasive process of medial load offloading at knee joint. In this paper, we evaluate the efficacy of different configuration of pole walking for reducing contact force at the knee joint through musculoskeletal (MSK) modeling.

Anatomical Landmark Detection using Deep Appearance-Context Network.

Accurate identification of anatomical landmarks is a crucial step in medical image analysis. While deep neural networks have shown impressive performance on computer vision tasks, they rely on a large amount of data, which is often not available. In this work, we propose an attention-driven end-to-end deep learning architecture, which learns the local appearance and global context separately that helps in stable training under limited data.

Heart Region Segmentation using Dense VNet from Multimodality Images.

Cardiovascular diseases (CVD) have been identified as one of the most common causes of death in the world. Advanced development of imaging techniques is allowing timely detection of CVD and helping physicians in providing correct treatment plans in saving lives. Segmentation and Identification of various substructures of the heart are very important in modeling a digital twin of the patient-specific heart.

Precise Bleeding and Red lesions localization from Capsule Endoscopy using Compact U-Net.

Wireless capsule endoscopy is a non-invasive and painless procedure to detect anomalies from the gastrointestinal tract. Single examination results in up to 8 hrs of video and requires between 45 - 180 mins for diagnosis depending on the complexity. Image and video computational methods are needed to increase both efficiency and accuracy of the diagnosis.

Resource Constrained CVD Classification Using Single Lead ECG On Wearable and Implantable Devices.

Electrocardiogram (ECG) is one of the fundamental markers to detect different cardiovascular diseases (CVDs). Owing to the widespread availability of ECG sensors (single lead) as well as smartwatches with ECG recording capability, ECG classification using wearable devices to detect different CVDs has become a basic requirement for a smart healthcare ecosystem. In this paper, we propose a novel method of model compression with robust detection capability for CVDs from ECG signals such that the sophisticated and effective baseline deep neural network model can be optimized for the resource constrained micro-controller platform suitable for wearable devices while minimizing the performance loss.

Selective Discrimination of VOCs Applying Gas Sensing Kinetic Analysis over a Metal Oxide-Based Chemiresistive Gas Sensor.

Semiconducting metal oxide-based gas sensors have inadequate selectivity as they are responsive toward a variety of gases. Here, we report the implementation of gas sensing kinetic analysis of the sensor to identify the tested volatile organic compounds (VOCs) (2-propanol, formaldehyde, methanol, and toluene) precisely. A single chemiresistive sensor was employed having tin oxide-based hollow spheres as the sensing material, which were obtained by chemical synthesis.

Detection of Counterfeit Medicines Using Hyperspectral Sensing.

Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that of counterfeit medicines is around 200 billion USD [2][3]. Counterfeit medicines can be detected by technical experts using visual inspection or through sophisticated lab and relevant methods. However, such methods require time, sample preparation and technical expertise with lab setup.

Surgical tool segmentation and localization using spatio-temporal deep network.

Laparoscopic cholecystectomy surgery is a minimally invasive surgery to remove the gallbladder, where surgical instruments are inserted through small incisions in the abdomen with the help of a laparoscope. Identification of tool presence and precise segmentation of tools from the video is very important in understanding the quality of the surgery and training budding surgeons. Precise segmentation of tools is required to track the tools during real-time surgeries.

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal.

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics.

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics.

Early Detection of Mild Cognitive Impairment using Pervasive Sensing.

Aging in place and independent living for the elderly has gained importance, and so has instrumented homes for ambient assisted living (AAL). In this paper we explore the feasibility of using passive sensors to provide insights into the cognitive and physical well-being of the subject. We derive a novel clustering based tactics to check heterogeneity in terms of movement behaviour among patients, and then provide our feasibility study on detection of mild cognitive impairment based on the results of the clustering.

PI-Sole: A Low-Cost Solution for Gait Monitoring Using Off-The-Shelf Piezoelectric Sensors and IMU.

In this paper, viability of low-cost off-the-shelf Piezoelectric ceramic disc elements is explored for an insole-based gait monitoring system, `PI-Sole' (Piezo In-Sole). Piezoelectric elements can sense dynamic changes in pressure in a closed-loop environment with good sensitivity and a wide measurement range. In this paper, method to enable these elements to continuously sense plantar pressure while walking is proposed, making them a very cost-efficient alternative to the widely used Force Sensing Resistors (FSR) and pressure plates for monitoring human gait.