Articles and Recent News
MyoNet: A Transfer-learning based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress from sEMG
The clinical assessment technology such as remote monitoring of rehabilitation progress for lower limb related ailments rely on the automatic evaluation of movement performed along with an estimation of joint angle information. In this paper, we introduce a transfer-learning based Long-term Recurrent Convolution Network (LRCN) named as ‘MyoNet’ for the classification of lower limb movements, along with the prediction of the corresponding knee joint angle.
Feasibility of using high-contrast grating as a point-of-care sensor for therapeutic drug monitoring of immunosuppressants
Abstract Point-of-care (POC) testing has demonstrated great transformative potential in personalized medicine. In particular, patients undergoing transplantation require POC testing to ensure appropriate serum immunosuppressant levels so as to maintain adequate graft function and survival. However, no suitable POC device for monitoring immunosuppressant levels is currently available.
Automated Detection of Symptomatic Autonomic Dysreflexia through Multimodal Sensing
Objective: Autonomic Dysreflexia (AD) is a potentially life-threatening syndrome which occurs in individuals with higher level spinal cord injuries (SCI). AD is caused by triggers which can lead to rapid escalation of pathophysiological responses and if the trigger is not removed, AD can be fatal. There is currently no objective, non-invasive and accurate monitoring system available to automatically detect the onset of AD symptoms in real time in a non-clinical setting.
Accuracy of the Apple Watch 4 to Measure Heart Rate in Patients with Atrial Fibrillation
Wearable wrist-monitors offer an unobtrusive way to acquire heart rate data in an efficient manner. Previous work in this field has focused on studying healthy subjects during exercise but has yet to assess the efficacy of these devices in patients suffering from common cardiac arrhythmias such as atrial fibrillation. Objective The objective of this pilot study was to assess the accuracy of the Apple Watch heart rate monitor in fifty patients experiencing atrial fibrillation compared to telemetry.
Intraoperative Localization of STN during DBS Surgery using a Data-driven Model
A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson’s Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team.
Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection
Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians’ burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns.