Posts Tagged deep learning

[ARTICLE] An interactive and low-cost full body rehabilitation framework based on 3D immersive serious games – Full Text


  • Generation of customizable 3D immersive serious games.
  • An interactive and low-cost full body rehabilitation framework.
  • Integration of a Head Mounted Display, a Time-of-Flight and an infrared camera.
  • A Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) reference model.


Strokes, surgeries, or degenerative diseases can impair motor abilities and balance. Long-term rehabilitation is often the only way to recover, as completely as possible, these lost skills. To be effective, this type of rehabilitation should follow three main rules. First, rehabilitation exercises should be able to keep patient’s motivation high. Second, each exercise should be customizable depending on patient’s needs. Third, patient’s performance should be evaluated objectively, i.e., by measuring patient’s movements with respect to an optimal reference model. To meet the just reported requirements, in this paper, an interactive and low-cost full body rehabilitation framework for the generation of 3D immersive serious games is proposed. The framework combines two Natural User Interfaces (NUIs), for hand and body modeling, respectively, and a Head Mounted Display (HMD) to provide the patient with an interactive and highly defined Virtual Environment (VE) for playing with stimulating rehabilitation exercises. The paper presents the overall architecture of the framework, including the environment for the generation of the pilot serious games and the main features of the used hand and body models. The effectiveness of the proposed system is shown on a group of ninety-two patients. In a first stage, a pool of seven rehabilitation therapists has evaluated the results of the patients on the basis of three reference rehabilitation exercises, confirming a significant gradual recovery of the patients’ skills. Moreover, the feedbacks received by the therapists and patients, who have used the system, have pointed out remarkable results in terms of motivation, usability, and customization. In a second stage, by comparing the current state-of-the-art in rehabilitation area with the proposed system, we have observed that the latter can be considered a concrete contribution in terms of versatility, immersivity, and novelty. In a final stage, by training a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) with healthy subjects (i.e., baseline), we have also provided a reference model to objectively evaluate the degree of the patients’ performance. To estimate the effectiveness of this last aspect of the proposed approach, we have used the NTU RGB + D Action Recognition dataset obtaining comparable results with the current literature in action recognition.[…]


Continue —-> An interactive and low-cost full body rehabilitation framework based on 3D immersive serious games – ScienceDirect

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[Abstract] EVA: EVAluating at-home rehabilitation exercises using augmented reality and low-cost sensors


Over one billion people in the world live with some form of disability. This is incessantly increasing due to aging population and chronic diseases. Among the emerging social needs, rehabilitation services are the most required. However, they are scarce and expensive what considerably limits access to them. In this paper, we propose EVA, an augmented reality platform to engage and supervise rehabilitation sessions at home using low-cost sensors. It also stores the user’s statistics and allows therapists to tailor the exercise programs according to their performance. This system has been evaluated in both qualitative and quantitative ways obtaining very promising results.

via EVA: EVAluating at-home rehabilitation exercises using augmented reality and low-cost sensors | SpringerLink

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[ARTICLE] Εvaluation of machine learning methods for seizure prediction in epilepsy – Full Text PDF


Epilepsy affects about 50 million people worldwide of which one third is refractory to medication. An automated and reliable system that warns of impending seizures would greatly improve patient’s quality of life by overcoming the uncertainty and helplessness due to the unpredicted events. Here we present new seizure prediction results including a performance comparison of different methods. The analysis is based on a new set of intracranial EEG data that has been recorded in our working group during presurgical evaluation.
We applied two different methods for seizure prediction and evaluated their performance pseudoprospectively. The comparison of this evaluation with common statistical evaluation reveals possible reasons for overly optimistic estimations of the performance of seizure forecasting systems.

1 Introduction

Affecting about 1 % of the world population, epilepsy is one of the most common neurological diseases. Although seizures cover relatively short periods in a patient’s life, the uncertainty when the next seizure will occur can produce a high level of anxiety [4]. For 70 % of the patients, medication can reduce the frequency of seizures or even abolish them. However, patients report that unwanted side effects of the medication as well as the unpredictability of seizures are the severest handicaps of this disease [13]. A mobile system with the ability to predict seizures can help to relief the patients’ anxiety related to the uncertainty of events by enabling them to seek shelter, apply a short acting drug or inform the treating physician about the event. The device might also be used to prevent or mitigate the seizure [12].

Usually, seizure prediction is treated as a binary classification problem of brain activity, recorded as intracranial electroencephalography (icEEG) [8], with the state of impending seizures (preictal) being labeled as 1 and periods with a big temporal distance to the next seizure (interictal) labeled as 0. In this contribution, we present a new database that has been recorded in our working group. By intensifying the cooperation of clinical research and data analysis we minimize loss of descriptive metadata. For feature extraction and classification of the recorded icEEG signals we employed both, a recently proposed deep convolutional neural network and a featurebased method.


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[Abstract + References] Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination – Conference paper


The work reintegration following shoulder biomechanical overload illness is a multidimensional process, especially for those tasks requiring strength, movement control and arm dexterity. Currently different robotic devices used for upper limb rehabilitation are available on the market, but these devices are not based on activities focused on the work reintegration. Furthermore, the rehabilitation programmes aimed to the work reintegration are insufficiently focused on the recovery of the necessary skills for the re-employment.

In this study the details of the design of an innovative robotic platform integrated with wearable sensors and virtual reality scenarios for upper limbs motor rehabilitation and visuomotor coordination is presented. The design of control strategy will also be introduced. The robotic platform is based on a robotic arm characterized by seven degrees of freedom and by an adaptive control, wearable sensorized insoles, virtual reality (VR) scenarios and the Leap Motion device to track the hand gestures during the rehabilitation training. Future works will address the application of deep learning techniques for the analysis of the acquired big amount of data in order to automatically adapt both the difficulty level of the VR serious games and amount of motor assistance provided by the robot.


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via Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination | SpringerLink

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[WEB SITE] New method based on artificial intelligence may help predict epilepsy outcomes


Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians’ decision making processes, this work also highlights how artificial intelligence is driving change in the medical field.

Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death. Debilitating seizures from epilepsy also greatly reduce quality of life, as normal activities are impaired.

Epilepsy surgery is often recommended to patients who do not respond to medications. Many patients are hesitant to undergo brain surgery, in part, due to fear of operative risks and the fact that only about two-thirds of patients are seizure-free one year after surgery. To tackle this critical gap in the treatment of this epilepsy population, Dr. Leonardo Bonilha and his team in the Department of Neurology at MUSC looked to predict which patients are likely to have success in being seizure free after the surgery.

Neurology Department Chief Resident Dr. Gleichgerrcht explains that they tried “to incorporate advanced neuroimaging and computational techniques to anticipate surgical outcomes in treating seizures that occur with loss of consciousness in order to eventually enhance quality of life”. In order to do this, the team turned to a computational technique, called deep learning, due to the massive amount of data analysis required for this project.

The whole-brain connectome, the key component of this study, is a map of all physical connections in a person’s brain. The brain map is created by in-depth analysis of diffusion magnetic resonance imaging (dMRI), which patients receive as standard-of-care in the clinic. The brains of epilepsy patients were imaged by dMRI prior to having surgery.

Deep learning is a statistical computational approach, within the realm of artificial intelligence, where patterns in data are automatically learned. The physical connections in the brain are very individualized and thus it is challenging to find patterns across multiple patients. Fortunately, the deep learning method is able to isolate the patterns in a more statistically reliable method in order to provide a highly accurate prediction.

Currently, the decision to perform brain surgery on a refractory epilepsy patient is made based on a set of clinical variables including visual interpretation of radiologic studies. Unfortunately, the current classification model is 50 to 70 percent accurate in predicting patient outcomes post-surgery. The deep learning method that the MUSC neurologists developed was 79 to 88 percent accurate. This gives the doctors a more reliable tool for deciding whether the benefits of surgery outweigh the risks for the patient.

A further benefit of this new technique is that no extra diagnostic tests are required for the patients, since dMRIs are routinely performed with epilepsy patients at most centers.

This first study was retrospective in nature, meaning that the clinicians looked at past data. The researchers propose that an ideal next step would include a multi-site prospective study. In a prospective study, they would analyze the dMRI scans of patients prior to surgery and follow-up with the patients for at least one year after surgery. The MUSC neurologists also believe that integrating the brain’s functional connectome, which is a map of simultaneously occurring neural activity across different brain regions, could enhance the prediction of outcomes.

Dr. Gleichgerrcht says that the novelty in the development of this study lies in the fact that this “is not a question of human versus machine, as is often the fear when we hear about artificial intelligence. In this case, we are using artificial intelligence as an extra tool to eventually make better informed decisions regarding a surgical intervention that holds the hope for a cure of epilepsy in a large number of patients.”


via New method based on artificial intelligence may help predict epilepsy outcomes

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[WEB SITE] Deep Learning Device Can Predict Epileptic Seizures

Vanessa Geneva Ahern
JANUARY 29, 2018
predict seizure,signs seizure,epilepsy prediction,hca news

Imagine going about your daily life, working, shopping, and driving, knowing that you might have a seizure at any moment. But relief is on the horizon, as researchers from the University of Melbourne in Victoria, Australia have developed a potentially life-saving deep learning tool that can predict when an epileptic seizure is about to happen.

Their study was published in the journal eBioMedicine last month. The deep learning-based prediction system “achieved mean sensitivity of 69% and mean time warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%,” according to the findings.

Dean Freestone, PhD, senior research fellow at the department of medicine at St. Vincent’s Hospital at the University of Melbourne, says the tech could be contained in a chip inside a wearable device such as a wristband or bracelet, “incorporating a person’s behavior, environment, and physiology.” He and fellow co-author Mark J. Cook, MD, chair of medicine at St. Vincent’s Hospital, have launched a company named Seer Medical to pursue this technology. They hope to implant patients with the technology later this year.

“The technology is now proven. We have shown seizure prediction is possible in our previous paper published in Brain and in a Kaggle contest. This new study is just further backup,” Freestone says.

The advance could change the lives of many people with epilepsy, who worry about looming seizures while they are doing everyday activities. Patients who have tested the technology reported that they felt more in control when they used the wearable device and were more confident doing novel activities. They also claimed to have benefited from improved sleep and decision making.

The new forecasting technology would be best suited for someone having seizures once per week, according to the architects. If someone has seizures every hour, or if the seizures are too infrequent, it is difficult to train the algorithms, Freestone notes.

The way the predictive technology works is similar to Facebook’s facial recognition software. Instead of people in photos, the researchers have trained the algorithms to recognize patterns in the electrical activity of the brain that preempt seizures. “It is software that learns from example. The electrical patterns are very subtle and are invisible to the human eye, but the computer algorithms can identify them. The circadian patterns then help to boost the algorithms performance,” Freestone says.

“Patients can take action to actually prevent seizures. This could be in the form of a medication or even just a change in behavioral. We have also learnt a lot about the mechanisms of seizure, such as the strong influence of circadian cycles,” he adds.

Although significant cost and risk comes with new trials of medical devices, researchers are excited about the changes they can make. “We are working toward a system that will constantly provide a person with a risk level of seizure susceptibility,” Freestone says. “It will be a gauge that outputs a probability. We will incorporate as many aspects of a person’s behavior, environment and physiology as we can acquire from wearable technologies and other sensors.”

The findings came about, in part, thanks to the University of Melbourne’s large, long-term data set, which is unique and apt for exploring deep learning for seizure forecasting.

via Deep Learning Device Can Predict Epileptic Seizures | Healthcare Analytics News

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[Abstract] Focal onset seizure prediction using convolutional networks


Objective: This work investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and as early as possible, clearly two competing objectives.
Methods: Convolutional filters on the wavelet transformation of the EEG signal are used to define and learn quantitative signatures for each period: interictal, preictal, and ictal. The optimal seizure prediction horizon is also learned from the data as opposed to making an a priori assumption.
Results: Computational solutions to the optimization problem indicate a ten-minute seizure prediction horizon. This result is verified by measuring Kullback-Leibler divergence on the distributions of the automatically extracted features.
Conclusions: The results on the EEG database of 204 recordings demonstrate that
  1. the preictal phase transition occurs approximately ten minutes before seizure onset, and
  2. (the prediction results on the test set are promising, with a sensitivity of 87.8% and a low false prediction rate of 0.142 FP/h.
Our results significantly outperform a random predictor and other seizure prediction algorithms.
Significance: We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.

via Focal onset seizure prediction using convolutional networks – IEEE Journals & Magazine

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