In recent developments of Human Activity Recognition systems (HAR), It has been found that deep learning models are being studied by researchers, especially convolutional neural networks integrated with long shortterm memory cells such as LSTM (ConvLSTM) networks. The structures require large datasets which demand extensive data collection. Therefore, various augmentation methods under focus nowadays. Furthermore, the challenge time-series is to choose method preserves correct labels. this...

Monitoring activities of daily living (ADLs) for wheelchair users, particularly spinal cord injury individuals is important understanding the rehabilitation progress, customizing treatment plans, and observing onset secondary health conditions. This work proposes an innovative sensory system measuring classifying ADLs relevant to We systematically evaluated multiple wearable sensors such as pressure distribution mats on seat, accelerometer data from ear wrists, IMU wheels achieve best...

Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for ambulatory diagnosis monitoring applications of populations at risk cardiovascular disease, generally due to a limited sample size. This paper introduces algorithm BP solely reliant on photoplethysmography (PPG) signals demographic features. It automatically obtains signal features employs the Markov Blanket (MB) feature selection discern informative transmissible features, achieving...

Human activity recognition (HAR) systems are used to monitor Parkinson's disease (PD) patients' mobility progress. Machine learning methods commonly for the development of HAR. These methods, however, require large amount data collected from human subjects. Data augmentation is an affordable alternative facilitating such by producing similar actual In this work, three augmentation: time warping, amplitude warping and linear combination were carried out on acceleration gyroscope signals 6...

Human Activity Recognition (HAR) using wearable systems in telerehabilitation and clinical applications has caught the attention of many researchers, especially for Parkinson's disease (PD) movement therapy. However, distinction between simple activities complex ones how to handle them have not been thoroughly investigated. We propose compare two variants a multi-task network with shared parameters recognize (SAs) (CAs) simultaneously. do so by introducing branched deep neural that uses...

<p>Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for application in populations at risk of cardiovascular disease, with generally limited sample sizes. Here, we introduce algorithm BP solely reliant on photoplethysmography (PPG) signals demographic features. Our automatically obtains signal features employs the Markov Blanket (MB) feature selection to discern informative transmissible features, achieving a robust space population...

<p>Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for application in populations at risk of cardiovascular disease, with generally limited sample sizes. Here, we introduce algorithm BP solely reliant on photoplethysmography (PPG) signals demographic features. Our automatically obtains signal features employs the Markov Blanket (MB) feature selection to discern informative transmissible features, achieving a robust space population...

Inertial Measurement Units have become one of the most widely used instruments in Human Activity Recognition and clinical applications. Their performance needs to be validated against gold standard systems reliably for any particular application. In this study, we MPU9250 TTGO T-Wristband smart band (Shenzhen Xinyuan Electronic Technology© Ltd.) MetaMotionR (MBIENTLABO, San Fransisco, USA) Lee Silverman Voice Treatment-BIG (LSVT-BIG) functional activities. The validation metrics study are...

Abstract Background: The advent of Inertial measurement unit (IMU) sensors has significantly extended the application domain Human Activity Recognition (HAR) systems to healthcare, tele-rehabilitation & daily life monitoring. IMU’s are categorized as body-worn and therefore their output signals HAR performance naturally depends on exact location body segments. Objectives: This research aims introduce a methodology investigate effects misplacing systems. Methods: properly placed misplaced...