Author(s)3: Sergey N. Makarov, Ara Nazarian, Gregory M. Noetscher, Ziming Zhang, Johnathan W. Adams
Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screeninghttps://www.embs.org/jtehm/wp-content/uploads/sites/17/2021/09/Screen-Shot-2021-09-08-at-10.07.07-AM.png412281IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)//www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png
Objective: There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. Methods: The present study reports…
Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoringhttps://www.embs.org/jtehm/wp-content/themes/movedo/images/empty/thumbnail.jpg150150IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)//www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png
Abstract Information within systems can be extracted through side-channels; unintended communication channels that leak information. The concept of side-channel sensing is explored, in which sensor data is analysed in non-trivial…
LoCoMo-Net: A Low-Complex Deep Learning Framework for sEMG Based Hand Movement Recognition for Prosthetic Controlhttps://www.embs.org/jtehm/wp-content/themes/movedo/images/empty/thumbnail.jpg150150IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)//www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png
Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we…
Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimationhttps://www.embs.org/jtehm/wp-content/uploads/sites/17/2020/07/Singla.png817745IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)//www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png
Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features extracted for…
Author(s)3: Amit Charyya, Arvind Gautam, Dwaipayan Biswas, Madhuri Panwar
MyoNet: A Transfer-learning based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress from sEMGhttps://www.embs.org/jtehm/wp-content/uploads/sites/17/2020/02/Fig_05-01.png21381075IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)//www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png
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…
Author(s)3: Ervin Sejdić, Kristin A. Lowry, Jennica Bellanca, Subashan Perera, Mark S. Redfern, Jennifer S. Brach
Extraction of Stride Events from Gait Accelerometry During Treadmill Walkinghttps://www.embs.org/jtehm/wp-content/uploads/sites/17/2015/12/sejdicgraphicalabstract1.jpg6511096IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)//www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png
Objective: Evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be…