Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMotion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I MS ). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for I MS ). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.Landspitali University Hospital, Reykjavi

A Regression Approach to Assess Bone Mineral Density of Patients undergoing Total Hip Arthroplasty through Gait Analysis

Total Hip Arthroplasty (THA) is the gold standard for hip replacement surgery. It can be performed with two different kinds of prostheses: cemented and uncemented. The surgeons have always decided on the type of prosthesis based on the age, sex of the patient and bone stock on x rays. In this paper 42 subjects underwent THA and performed both gait analysis and bone mineral density (BMD) evaluation through CT scans; spatial and temporal gait parameters were used to predict BMD of the distal and proximal parts of the femur before and one year after surgery using machine learning regression analysis. A simple linear regression (LR) and k-nearest neighbor (KNN) were implemented coding with Python Scikit-Learn libraries and some evaluation metrics were computed: the coefficient of determination (R2), mean absolute error, mean squared error and root mean squared error. Both the algorithms had a R2 greater than 75% in predicting both proximal and distal; particularly, LR obtained the highest score of 88.4% in predicting the BMD before the THA and of 81.3% after the THA. All the R2 of KNN ranged from 75% and 77%. All the calculated errors were always below 0.001. In conclusion, this research shows the feasibility of gait parameters for assessing the follow-up after 52 weeks of patients undergoing THA by predicting the BMD. Moreover, the results give insights about the relationship between the patterns of gait and BMD

Predicting lifestyle using BioVRSea multi-biometric paradigms

BioVRSea was recently introduced as an unique multi-biometric system that combine Virtual Reality with a moving platform to induce Motion Sickness (MS). Electromyography (EMG) and balance features measuring the center of pressure (CoP) are among the bio-signals measured during a six segments protocol on BioVRSea. A total of 262 participants has been measured and all of them underwent an MS questionnaire to self-assess the MS relative symptoms and personal information like smoking, physical activity and Body Mass Index. From the last three data a binary lifestyle index is created and Machine Learning models are used to classify it starting from EMG and CoP groups of features taken individually and together. After an appropriate feature's selection, multiple algorithms are applied and the best results for the lifestyle index classification are reached with the K Nearest Neighbors algorithm (0.83 of maximum accuracy and 0.60 of recall) while Random Forest perform the best AUCROC (0.64). The most relevant features for the best models are the CoP ones during the second segment of the experiment, before the platform movements, and during its first light movements. These results show that an unhealthy lifestyle influences in a negative way the performance of a person in term of balance in a induced MS task. They can also be used as a preliminary input to study the influence of lifestyle in the behavior of people who suffers of serious MS problems or neurodegenerative patients using the novel BioVRSea platform

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans

Poly(amido-amine)-based hydrogels with tailored mechanical properties and degradation rates for tissue engineering

Poly(amido-amine) (PAA) hydrogels containing the 2,2-bisacrylamidoacetic acid-4-amminobutyl guanidine monomeric unit have a known ability to enhance cellular adhesion by interacting with the arginin\u2013glycin\u2013aspartic acid (RGD)-binding \u3b1V\u3b23 integrin, expressed by a wide number of cell types. Scientific interest in this class of materials has traditionally been hampered by their poor mechanical properties and restricted range of degradation rate. Here we present the design of novel biocompatible, RGD-mimic PAA-based hydrogels with wide and tunable degradation rates as well as improved mechanical and biological properties for biomedical applications. This is achieved by radical polymerization of acrylamide-terminated PAA oligomers in both the presence and absence of 2-hydroxyethylmethacrylate. The degradation rate is found to be precisely tunable by adjusting the PAA oligomer molecular weight and acrylic co-monomer concentration in the starting reaction mixture. Cell adhesion and proliferation tests on Madin\u2013Darby canine kidney epithelial cells show that PAA-based hydrogels have the capacity to promote cell adhesion up to 200% compared to the control. Mechanical tests show higher compressive strength of acrylic chain containing hydrogels compared to traditional PAA hydrogels