26,114 research outputs found
Big Data and Parkinsonâs Disease: Exploration, Analyses, and Data Challenges.
In healthcare, a tremendous amount of clinical and laboratory tests, imaging, prescription and medication data are being collected. Big data analytics on these data aim at early detection of disease which will help in developing preventive measures and in improving patient care. Parkinson disease is the second-most common neurodegenerative disorder in the United States. To find a cure for Parkinson\u27s disease biological, clinical and behavioral data of different cohorts are collected, managed and propagated through Parkinsonâs Progression Markers Initiative (PPMI). Applying big data technology to this data will lead to the identification of the potential biomarkers of Parkinsonâs disease. Data collected in human clinical studies is imbalanced, heterogeneous, incongruent and sparse. This study focuses on the ways to overcome the challenges offered by PPMI data which is wide and gappy. This work leverages the initial discoveries made through descriptive studies of various attributes. The exploration of data led to identifying the significant attributes. We are further working to build a software suite that enables end to end analysis of Parkinsonâs data (from cleaning and curating data, to imputation, to dimensionality reduction, to multivariate correlation and finally to identify potential biomarkers)
Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders
Lower limb disorders are a substantial contributor to both disability and lower standards of life. The prevalent disorders affecting the lower limbs include osteoarthritis of the knee, hip, and ankle. The present study focuses on the use of footwear that incorporates force-sensing resistor sensors to classify lower limb disorders affecting the knee, hip, and ankle joints. The research collected data from a sample of 117 participants who wore footwear integrated with force-sensing resistor sensors while walking on a predetermined walkway of 9 meters. Extensive preprocessing and feature extraction techniques were applied to form a structured dataset. Several machine learning classifiers were trained and evaluated. According to the findings, the Random Forest model exhibited the highest level of performance on the balanced dataset with an accuracy rate of 96%, while the Decision Tree model achieved an accuracy rate of 91%. The accuracy scores of the Logistic Regression, Gaussian Naive Bayes, and Long Short-Term Memory models were comparatively lower. K-fold cross-validation was also performed to evaluate the modelsâ performance. The results indicate that the integration of force-sensing resistor sensors into footwear, along with the use of machine learning techniques, can accurately categorize lower limb disorders. This offers valuable information for developing customized interventions and treatment plans
A Review of EMG Techniques for Detection of Gait Disorders
Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices
The Key Artificial Intelligence Technologies in Early Childhood Education: A Review
Artificial Intelligence (AI) technologies have been applied in various
domains, including early childhood education (ECE). Integration of AI
educational technology is a recent significant trend in ECE. Currently, there
are more and more studies of AI in ECE. To date, there is a lack of survey
articles that discuss the studies of AI in ECE. In this paper, we provide an
up-to-date and in-depth overview of the key AI technologies in ECE that
provides a historical perspective, summarizes the representative works,
outlines open questions, discusses the trends and challenges through a detailed
bibliometric analysis, and provides insightful recommendations for future
research. We mainly discuss the studies that apply AI-based robots and AI
technologies to ECE, including improving the social interaction of children
with an autism spectrum disorder. This paper significantly contributes to
provide an up-to-date and in-depth survey that is suitable as introductory
material for beginners to AI in ECE, as well as supplementary material for
advanced users.Comment: 39 pages, 9 figures, 4 table
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