Exercise & Fitness

Enhancing Precision in Gesture Detection for Hand Recovery After Injury Using Leap Motion and Machine Learning.

This paper presents an improved solution for detecting gestures with a better precision using the Leap Motion sensor and Machine Learning support. A neural network is trained to recognize a hand rotation gesture expressing the grade of recovery, with a supination and pronation exercise. The supination-pronation movement is divided into 4 levels because the users are not usually able to perform a c...

Semi-supervised learning of Hidden Markov Models for biological sequence analysis.

MOTIVATION: Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint p...

An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs.

MOTIVATION: Convolutional neural networks (CNNs) have been tremendously successful in many contexts, particularly where training data are abundant and...

[Brain-Machine Interface and Neuro-Rehabilitation].

Brain-Machine Interface (BMI) is a technology that enables users to control computers/machines intuitively via their volitional brain activities. Cont...

Realtime Indoor Workout Analysis Using Machine Learning & Computer Vision.

These days there are thousands of workout videos available on the internet. Samsung Health [1] provides a dedicated section called programs containing...

Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning.

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The ...

Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks.

Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and ...

Experimental Analysis of Artificial Neural Networks Performance for Physical Activity Recognition Using Belt and Wristband Devices.

Physical activity (PA) is widely recognized as one of the important elements of personal healthy life. To date, as the development of wearable sensing...

Feasibility Study of Deep Neural Network for Heart Rate Estimation from Wearable Photoplethysmography and Acceleration Signals.

Heart rate (HR) estimation using wearable reflectance-type photoplethysmographic (PPG) signals is challenging due to low signal-to-noise ratio (SNR). ...

Classification of Rehabilitation Participation in Elderly In-patients with Mild Cognitive Impairments Utilizing Physiological Responses.

This study investigated the possibility of utilising physiological responses and machine learning techniques to determine the degree of participation ...

Short-term Effects of Robot-Resistance Exercises on Muscle Strength and Activations: Types of Muscle Contraction and Speed of Contraction.

We developed the isokinetic exercise robot for single joint muscle training. The purpose of this study was to investigate the effects of contraction t...

Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System.

Development of noninvasive brain-machine interface (BMI) systems based on electroencephalography (EEG), driven by spontaneous movement intentions, is ...

Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs.

Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using the 0-4 Kellgren Lawrence (KL) grading system where 0 is n...

Investigation of Fatigue Using Different EMG Features.

Rehabilitative exercise for people suffering from upper limb impairments has the potential to improve their neuro-plasticity due to repetitive trainin...

Multi-purpose Robotic Training Strategies for Neurorehabilitation with Model Predictive Controllers.

One of the main challenges in robotic neuroreha-bilitation is to understand how robots should physically interact with trainees to optimize motor lean...

Virtual Reality Environments and Haptic Strategies to Enhance Implicit Learning and Motivation in Robot-Assisted Training.

Motivation plays a crucial role in motor learning and neurorehabilitation. Participants' motivation could decline to a point where they may stop train...

Feasibility study: Towards Estimation of Fatigue Level in Robot-Assisted Exercise for Cardiac Rehabilitation.

Socially Assistive Robotics (SAR) has shown to be an important tool to assist patients in physical rehabilitation. SAR is used to provide feedback abo...

Method for Muscle Tone Monitoring During Robot-Assisted Therapy of Hand Function: A Proof of Concept.

Robot-assisted rehabilitation of hand function is becoming an established approach to complement conventional therapy after stroke, particularly in vi...

Brain activity during reciprocal social interaction investigated using conversational robots as control condition.

We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (hu...

A Cancer Paradox: Machine-Learning Backed Propensity-Score Analysis of Coronary Angiography Findings in Cardio-Oncology.

OBJECTIVES: Cancer has been proposed as a cardiovascular risk factor. We aimed to assess the cardiovascular risk profile and coronary angiography (CA)...