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

Vers la Sécurité et la Protection de la Vie Privée Sensibles au Contexte comme un Service dans l'Internet des Objets

International audienceSmart city is one of the most known Internet of Things (IoT) applications. The smart city services improve user’s daily lives. However, security and privacy issues are slowing down their adoption. In addition, the characteristics of IoT devices, applications and users make security implementation of the considered applications a challenging task. To address these issues, we present, in this paper, a new context-aware security and privacy architecture for the IoT. Thanks to the “as a service” approach, this new architecture will be user-centric. It will also support known context-aware security issues: dynamicity, flexibility. In addition, it will address mobility, customization of security and privacy services, and support for generic IoT applications, particularly for smart city. To do so, a knowledge plane allowing effective management of context-awareness is proposed. A security and privacy plane allowing better implementation of context-aware security and privacy mechanisms is also proposed. This will be done through dynamic composition of context-based micro services. The role of the different components of these two planes are also described

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

Postural control paradigm (BioVRSea): towards a neurophysiological signature

Objective. To define a new neurophysiological signature from electroencephalography (EEG) during a complex postural control task using the BioVRSea paradigm, consisting of virtual reality (VR) and a moving platform, mimicking the behavior of a boat on the sea. Approach. EEG (64 electrodes) data from 190 healthy subjects were acquired. The experiment is composed of 6 segments (Baseline, PRE, 25%, 50%, 75%, POST). The baseline lasts 60 s while standing on the motionless platform with a mountain view in the VR goggles. PRE and POST last 40 s while standing on the motionless platform with a sea simulation. The 3 other tasks last 40 s each, with the platform moving to adapt to the waves, and the subject holding a bar to maintain its balance. The power spectral density (PSD) difference for each task minus baseline has been computed for every electrode, for five frequency bands (delta, theta, alpha, beta, and low-gamma). Statistical significance has been computed. Main results. All the bands were significant for the whole cohort, for each task regarding baseline. Delta band shows a prefrontal PSD increase, theta a fronto-parietal decrease, alpha a global scalp power decrease, beta an increase in the occipital and temporal scalps and a decrease in other areas, and low-gamma a significant but slight increase in the parietal, occipital and temporal scalp areas. Significance. This study develops a neurophysiological reference during a complex postural control task. In particular, we found a strong localized activity associated with certain frequency bands during certain phases of the experiment. This is the first step towards a neurophysiological signature that can be used to identify pathological conditions lacking quantitative diagnostics assessment

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

Primary prevention of diabetic retinopathy with fibrates: a retrospective, matched cohort study

Objectives To compare the progression of diabetic retinopathy (DR) in people with type 2 diabetes treated with fibrates with that of non-exposed controls. Design Retrospective, matched cohort study. Setting UK Clinical Practice Research Datalink (CPRD). Participants 5038 people with type 2 diabetes with a history of fibrate exposure but without evidence of DR were identified. Three thousand one hundred and seventy-six (63%) people could be randomly matched to one non-exposed control; of these, 2599 (81.8%) were matched without any missing blood pressure or glycated haemoglobin (HbA1c) values. Main outcome measures The primary endpoint was first recorded DR with a secondary endpoint of all-cause mortality or first DR. Time to clinical endpoints was compared using Cox proportional hazards models. Results Mean follow-up was 5.1 and 5.0 years for fibrate-exposed and non-exposed patients, respectively. For fibrate-exposed participants, there was a reduction in DR: 33.4 events/1000 person-years vs 40.4 (p=0.002), and in death or DR: 50.6 vs 60.2 (p<0.001). For those matched with full systolic blood pressure and HbA1c data, crude event rates were 34.3 versus 43.9 for DR (p<0.001) and 51.2 vs 63.4 (p<0.001) for death or DR. Following adjustment, DR was significantly delayed for those treated with fibrates, with an adjusted HR (aHR) of 0.785 (p<0.001) for participants with complete data and an aHR of 0.802 (p<0.001) for all participants. Conclusions The treatment with fibrates in people with type 2 diabetes was independently associated with reduced progression to a first diagnosis of DR

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.</jats:p

Similar immunogenicity profiles between the proposed biosimilar MYL-1501D and reference insulin glargine in patients with diabetes mellitus: the phase 3 INSTRIDE 1 and INSTRIDE 2 studies

Abstract Background MYL-1501D is a proposed biosimilar to insulin glargine. The noninferiority of MYL-1501D was demonstrated in patients with type 1 diabetes and type 2 diabetes in 2 phase 3 trials. Immunogenicity of MYL-1501D and reference insulin glargine was examined in both studies. Methods INSTRIDE 1 and INSTRIDE 2 were multicenter, open-label, randomized, parallel-group studies. In INSTRIDE 1, patients with type 1 diabetes received MYL-1501D or insulin glargine over a 52-week period. In INSTRIDE 2, patients with type 2 diabetes treated with oral antidiabetic drugs, insulin naive or not, received MYL-1501D or reference insulin glargine over a 24-week period. Incidence rates and change from baseline in relative levels of antidrug antibodies (ADA) and anti–host cell protein (anti-HCP) antibodies in both treatment groups were determined by a radioimmunoprecipitation assay and a bridging immunoassay, respectively. Results were analyzed using a mixed-effects model (INSTRIDE 1) or a nonparametric Wilcoxon rank sum test (INSTRIDE 2). Results Total enrollment was 558 patients in INSTRIDE 1 and 560 patients in INSTRIDE 2. The incidence of total and cross-reactive ADA was comparable between treatment groups in INSTRIDE 1 and INSTRIDE 2 (P &gt; 0.05 for both). A similar proportion of patients had anti-HCP antibodies in both treatment groups in INSTRIDE 1 at week 52 (MYL-1501D, 93.9 %; reference insulin glargine, 89.6 %; P = 0.213) and in INSTRIDE 2 at week 24 (MYL-1501D, 87.3 %; reference insulin glargine, 86.9 %; P &gt; 0.999). Conclusions In INSTRIDE 1 and INSTRIDE 2, similar immunogenicity profiles were observed for MYL-1501D and reference insulin glargine in patients with type 1 diabetes and type 2 diabetes, respectively. Trial registration ClinicalTrials.gov, INSTRIDE 1 (NCT02227862; date of registration, August 28, 2014); INSTRIDE 2 (NCT02227875; date of registration, August 28, 2014). </jats:sec