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Functional connectivity between the brain and body kinematics has largely not been investigated due to the requirement of motionlessness in neuroimaging techniques such as functional magnetic resonance imaging (fMRI). However, this connectivity is disrupted in many neurodegenerative disorders, including Parkinson’s Disease (PD), a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. In this study, brain activity is recorded through functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and body kinematics were captured by a motion capture system (Mocap) based on an inertial measurement unit (IMU) for gross movements (large movements such as limb kinematics), and the WearUp glove for fine movements (small range movements such as finger kinematics). PD and neurotypical (NT) participants were recruited to perform 8 different movement tasks. The recorded data from each modality have been analyzed individually, and the processed data has been used for classification between the PD and NT groups. The average changes in oxygenated hemoglobin (HbO2) from fNIRS, EEG power spectral density in the Theta, Alpha, and Beta bands, acceleration vector from Mocap, and normalized WearUp flex sensor data were used for classification. 12 different support vector machine (SVM) classifiers have been used on different datasets such as only fNIRS data, only EEG data, hybrid fNIRS/EEG data, and all the fused data for two classification scenarios: classifying PD and NT based on individual activities, and all activity data fused together. The PD and NT group could be distinguished with more than 83% accuracy for each individual activity. For all the fused data, the PD and NT groups are classified with 81.23%, 92.79%, 92.27%, and 93.40% accuracy for the fNIRS only, EEG only, hybrid fNIRS/EEG, and all fused data, respectively. The results indicate that the overall performance of classification in distinguishing PD and NT groups improves when using both brain and body data.
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Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain
A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct methods and tightly-coupled optimization techniques that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.
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- PAR ID:
- 10125477
- Date Published:
- Journal Name:
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 7221--7227
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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