Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar.

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.

Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator.

Driver's gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers' gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

A Physiological Sensor-Based Android Application Synchronized with a Driving Simulator for Driver Monitoring.

In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving performance. The app can configure, select, receive, process, represent graphically, and store the signals from electrocardiogram (ECG), electromyogram (EMG) and galvanic skin response (GSR) modules and accelerometers, a magnetometer and a gyroscope. The Android app is synchronized in two steps with a driving simulator that we previously developed using the Unity game engine to analyze driving security and efficiency. The Android app was tested with different sensors working simultaneously at various sampling rates and in different Android devices. We also tested the synchronized working of the driving simulator and the Android app with 25 people and analyzed the relation between data from the ECG, EMG, GSR, and gyroscope sensors and from the simulator. Among others, some significant correlations between a gyroscope-based feature calculated by the Android app and vehicle data and particular traffic offences were found. The Android app can be applied with minor adaptations to other different users such as patients with chronic diseases or athletes.

Does an early mobilization and immediate home-based self-therapy exercise program displace proximal humeral fractures in conservative treatment? Observational study.

BACKGROUND: Nonoperative management of proximal humeral fractures (PHFs) is the most common treatment, but its functional outcome may improve with early mobilization. In frail osteoporotic patients, quick recovery of prefracture independency is mandatory. This study assessed fracture displacement in PHFs managed with conservative treatment after early mobilization and a home-based self-exercise program. METHODS: We retrospectively analyzed the radiologic displacement of fracture fragments of PHFs treated conservatively with early mobilization and a home-based self-exercise program. RESULTS: Included were 99 patients with 26 one-part, 32 two-part, 32 three-part, and 9 four-part PHFs managed conservatively, followed by early mobilization and a home-based self-exercise program. In the x-ray examinations, the head displaced from varus into valgus 55° ± 23° to 42° ± 22°, in the normal range of anatomic values. The medial hinge displaced from medial to the diaphysis (+1 ± 6 mm) to lateral to the head (-0.6 ± 6 mm). The greater tuberosity displaced cranially from -1 ± 7 mm to 2 ± 5 mm. The Constant score at the 1-year follow-up was 79.69 ± 16.3. DISCUSSION AND CONCLUSIONS: The home-based self-exercise program for conservative treatment of PHFs displaces the head-diaphysis angle and the medial hinge toward anatomic reduction, but there is a risk of greater tuberosity cranial displacement. Functional results are fairly good, allowing frail patients to keep on with their independency and life style. Because a large number of patients might need further physiotherapy, the quality of the home-based self-exercises should be supervised.

Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

Multimodal video and IMU kinematic dataset on daily life activities using affordable devices.

Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient gross motor tracking solutions for daily life activities recognition and kinematic analysis. The dataset includes 13 activities registered using a commodity camera and five inertial sensors. The video recordings were acquired in 54 subjects, of which 16 also had simultaneous recordings of inertial sensors. The novelty of dataset lies in: (i) the clinical relevance of the chosen movements, (ii) the combined utilization of affordable video and custom sensors, and (iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction in a musculoskeletal model from inertial data. The validation confirms that a minimally disturbing acquisition protocol, performed according to real-life conditions can provide a comprehensive picture of human joint angles during daily life activities.

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network.

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.

Patient Satisfaction Using a Home-Based Rehabilitation Protocol for the Non-Surgical Treatment of Proximal Humeral Fractures: A Prospective Longitudinal Cohort Study.

Supervised, center-based, daily physiotherapy presents limitations: transport, need for an accompanying person, or risk of infection. Home-based rehabilitation protocols (HBRP) can be effective alternatives. We use a HBRP for the non-surgically treated proximal humeral fractures (PHF) in older patients. OBJECTIVES: To assess patient satisfaction and preferences of using a booklet, videos, or an app to guide physiotherapy. PATIENTS AND METHODS: Prospective, single-center observational study of patients ≥55 years old who sustained a non-surgically treated PHF. The HBRP consisted of immediate mobilization, followed by 5 physiotherapist-guided, weekly sessions of rehabilitation and standard physiotherapy after 3 months, if needed. A booklet with images, videos, or a smartphone application were offered to guide the patients. RESULTS: Mean degree of satisfaction (1-5) was 4.66 ± .9: 84 patients (82.4%) were very satisfied, 11 patients (10.8%) were satisfied, and 5 patients (4.9%) were not satisfied at all. Mean Oxford Shoulder Score achieved was 40.5 ± 6.6. 59.8% patients preferred the booklet and 29.4% the videos. Exercise compliance was considered very high in 87.3% of patients, while 4% hardly never followed the HBRP. Only 17.7% patients needed center-based physiotherapy after the HBRP. DISCUSSION: Reasons for satisfaction were good final functional outcome, no need for transportation, being away from hospital, immediate rehabilitation availability and being capable of maintaining independence. Adherence is a major concern. Videos are more didactic explaining the exercises. CONCLUSION: If standard physiotherapy is not available, the HBRP can be a valid treatment option for PHF management in older patients, with a high degree of patient satisfaction. Older patients preferred the booklet to guide physiotherapy.

Measuring Alterations of Spontaneous EEG Neural Coupling in Alzheimer's Disease and Mild Cognitive Impairment by Means of Cross-Entropy Metrics.

Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed. Specifically, we computed Cross-Approximate Entropy (Cross-ApEn) and Cross-Sample Entropy (Cross-SampEn) of 37 patients with dementia due to AD, 37 subjects with MCI, and 37 healthy control (HC) subjects. Our results showed that Cross-SampEn outperformed Cross-ApEn, revealing higher number of significant connections among the three groups (Kruskal-Wallis test, FDR-corrected p-values < 0.05). AD patients exhibited statistically significant lower similarity values at θ and ß1 frequency bands compared to HC. MCI is also characterized by a global decrease of similarity in all bands, being only significant at ß1. These differences shows that ß band might play a significant role in the identification of early stages of AD. Our results suggest that Cross-SampEn could increase the insight into brain dynamics at different AD stages. Consequently, it may contribute to develop early AD biomarkers, potentially useful as diagnostic information.

Efficient transfer entropy analysis of non-stationary neural time series.

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings.

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab(®) implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn.

Synchrony analysis of spontaneous MEG activity in Alzheimer's disease patients.

The aim of this study was to analyze the magnetoencephalography (MEG) background activity in Alzheimer's disease (AD) patients using cross-approximate entropy (Cross-ApEn). Cross-ApEn is a nonlinear measure of asynchrony between time series. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 12 AD patients and 12 age-matched control subjects. We found significantly higher synchrony between MEG signals from AD patients compared with control subjects. Additionally, we evaluated the ability of Cross-ApEn to discriminate these two groups using receiver operating characteristic (ROC) curves with a leave-one-out cross-validation procedure. We obtained an accuracy of 70.83% (66.67% sensitivity, 75% specificity) and a value of area under the ROC curve of 0.83. These results provide evidence of disconnection problems in AD. Our findings show the usefulness of Cross-ApEn to detect the brain dysfunction in AD.