6,579 research outputs found

    Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

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    Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

    Brain-Computer Interfaces in Medicine

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    Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroenceph-alography-based spelling and single-neuron-based device control, researchers have gone on to use electroenceph-alographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function

    EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

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    Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

    Open electronics for medical devices: State-of-art and unique advantages

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    A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design

    MR-compatible Electrophysiology Recording System for Multimodal Imaging

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    Simultaneous acquisition of functional magnetic resonance imaging (fMRI) and electrophysiological recordings is an emerging multimodal neuroimaging strategy for studying brain functions. However, the strong magnetic field generated during fMRI greatly degrades the electrophysiological signal quality during simultaneous acquisition. Here, I developed a low powered, miniaturized, system – “ECHO” which delivers a hardware and software solution to overcome the challenges presented by multimodal imaging. The device monitors fluctuations in electromagnetic field during fMRI and synchronizes amplification and sampling of electrophysiological signals to minimize effects of gradient and RF artifacts (electromagnetic artifacts). Furthermore, I introduced a concept of wirelessly transmitting recorded data through the MRI receiver coil. ECHO transmits the data at a frequency visible to the MRI receiver coil, after which the transmitted data is readily separable from the MRI image in the frequency domain. The MR-compatibility of the recorder was evaluated through a series of experiments with a phantom to study its effects on the MRI image quality. To further evaluate the effectiveness of ECHO, I recorded electrocardiogram and local field potential (evoked potential) in live rats during concurrent fMRI acquisition. In summary, ECHO offers a ‘plug and play’ solution to capture artifact-free electrophysiological data without the need of expensive amplifiers or synchronization hardware which require physical connection to the MRI scanner. This device is expected to make multimodal imaging more accessible and be applied for a broad range of fMRI studies in both the research and clinical fields
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