A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy.

Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.

Evaluation of functional tests performance using a camera-based and machine learning approach.

The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists' assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.

Automatic Telerehabilitation System in a Home Environment Using Computer Vision.

In this paper, we follow up on research dealing with body tracking and motor rehabilitation. We describe the current situation in telerehabilitation in the home environment. Existing solutions do not allow wide adoption due to hardware requirements and complicated setup. We come with the possibility of telerehabilitation using only laptop or mobile web camera. Together with physiotherapists, we have compiled a set of complex motor exercises to show that the system can be practically used.

Affordable Personalized, Immersive VR Motor Rehabilitation System with Full Body Tracking.

During the last decade, we have experienced fast development of virtual reality technology combined with various sensors and applications in different fields. The devices and applications are more easily accessible for the broader public. In this article, we describe a feasibility study of an affordable personalized, immersive VR motor rehabilitation system with full body tracking. While virtual reality rehabilitation is a relatively new field, several applications were already proven more effective than traditional rehabilitation programs. The applied methods utilize VR headset HTC Vive and HTC trackers together witch inverse kinematics algorithms to provide full body tracking. For that, we provide a framework for individual body calibration. The main result of the study is a developed virtual environment with guided rehabilitation scenarios based on personalized body calibration. We have proven that this approach can be used in personalized rehabilitation programs.

Personalized Monitoring and Assistive Systems: Case Study of Efficient Home Solutions.

The rapid emergence and proliferation of connected medical devices and their application in healthcare are already part of the Healthcare Internet of Things (IoT) - as this area started to be named. Their true impact on patient care and other aspects of healthcare remains to be seen and is highly dependent on the quality and relevancy of the data acquired. There is also the trend of application of IoT in telemedicine and home care environment. Currently many research groups focus on design and development of various solutions that can assist elderly and handicapped people in their home environment. However, many of these solutions are sophisticated and require advanced users that are able to control the device, handle error states and exceptions. They are frequently using expensive technologies that are good for laboratory environment but they are not affordable for many elderly or handicapped persons. In the paper we will analyze the current situation, present identified needs of elderly population and propose potential solutions. On a case study of efficient home solution of a personalized and assistive system we will show possibilities of technologically simple solutions using off-the-shelf devices and elements.