ABSTRACTBrain-computer Interfaces (BCIs) can provide a non-muscular communication channel for individuals with motor impairments. When integrated with virtual reality (VR) and haptic feedback, motor imagery (MI)-based...
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ABSTRACTBrain-computer Interfaces (BCIs) can provide a non-muscular communication channel for individuals with motor impairments. When integrated with virtual reality (VR) and haptic feedback, motor imagery (MI)-based BCIs can augment the rehabilitation outcome for patients with severe motor impairments. However, the physiological impact of these protocols beyond brain-related signals, that reflect autonomic nervous system (ANS) activity, remains underexplored. This study aims to investigate variations in a broader range of physiological signals besides electroencephalography (EEG) – including electrocardiography (ECG), photoplethysmography (PPG), and respiration – across different experimental conditions and to identify the factors driving these changes. 19 healthy subjects underwent MI training across five combinations of feedback conditions: abstract vs. realistic feedback, head-mounted display (HMD) vs. monitor, and the presence or absence of haptic feedback, compared with motor execution data. PPG results were compared with ECG results to assess the reliability of the finger-clip PPG sensor regarding its ability to replace ECG in cases where ease of use and unobtrusiveness in heart monitoring are required. Current findings show that VR-based MI with haptic feedback, results in increased modulation of Beta and Gamma bands, while all conditions may impose a greater mental burden than motor execution, as indicated by the increased respiration rate and decreased heart-rate variability.
This paper demonstrates dexterity optimization of a Deltalike three degrees of freedom (3 DOF) spatial parallel manipulator. The parallel manipulator consists of three identical chains and is able to move on all three...
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Worldwide electric motor market is moving towards Premium/IE3, Super-Premium/IE4 and Ultra-Premium/IE5 efficiency classes. Therefore, motor manufacturers are constantly improving the overall design of three-phase asyn...
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In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a ca...
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In this paper we propose a probabilistic model to parameterize human interactive behaviour from human motion. To Support the model taxonomy, we use Laban Movement Analysis (LMA), proposed by Rudolph Laban [11], to cha...
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Reliable detection and classification of vulnerable road users constitute a critical issue on safety/protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems ...
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This paper develops a hybrid control strategy that provides autonomous transition between hovered and leveled flights to a model-scale fixed-wing aircraft. The aircraft's closed-loop dynamics are described by mean...
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In recent years, there has been a tendency for people to resort to a more sedentary lifestyle, which has only been aggravated by the COVID-19 pandemic and the evermore common remote work policies. This lack of physica...
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This paper proposes a new approach for the Power Spectrum (PS)-based feature extraction applied to probabilistic Laban Movement Analysis (LMA), for the sake of human behaviour understanding. A Bayesian network is pres...
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ISBN:
(纸本)9780982443811
This paper proposes a new approach for the Power Spectrum (PS)-based feature extraction applied to probabilistic Laban Movement Analysis (LMA), for the sake of human behaviour understanding. A Bayesian network is presented to understand human action and behaviour based on 3D spatial data and using the LMA concept which is a known human movement descriptor. We have two steps for the classification process. The first step is estimating LMA parameters which are built to describe human motion situation by using some low level features. Then by having these parameters, it is possible to classify different human actions and behaviours. Here, a sample of using 3D acceleration data of six body parts to obtain some LMA parameters and understand some performed actions by human is shown. A new approach is applied to extract features from a signal data such as acceleration using the PS technique to achieve some of LMA parameters. A number of actions are defined, then a Bayesian network is used in learning and classification process. The experimental results prove that the proposed method is able to classify actions.
This article explores the topic involving children with autism, referencing what this condition is about, as well as the hypotheses for approaching the problem based on the literature reviewed. The proposed approach i...
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