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Robot end-to-end combination of microtubules operated by kinesin.
Recent mobile and wearable electroencephalogram (EEG)-sensing technologies have been demonstrated to be effective for measuring rapid changes of spatio-spectral EEG correlates of brain and cognitive functions of interest with more ecologically natural settings. However, commercial EEG products are available commonly with a fixed headset in terms of the number of electrodes and their locations to the scalp practically constrains their generalizability for different demands of EEG and brain-computer interface (BCI) study. While most progress focused on innovation of sensing hardware and conductive electrodes, less effort has been done to renovate mechanical structures of an EEG headset. Recently, an electrode-holder assembly infrastructure was designed to be capable of unlimitedly (re)assembling a desired n-channel electrode headset through a set of primary elements (i.e., LEGO-like headset). The present work empirically demonstrated one of its advantage regarding coordinating the homogeneous or heterogeneous sensors covering the target regions of the brain. Towards this objective, an 8-channel LEGO headset was assembled to conduct a simultaneous event-related potential (ERP) recording of the wet- and dry-electrode EEG systems and testify their signal quality during standing still versus treadmill walking. The results showed that both systems returned a comparable P300 signal-to-noise ratio (SNR) for standing, yet the dry system was more susceptible to the movement artifacts during slow walking. The LEGO headset infrastructure facilitates a desired benchmark study, e.g., comparing the signal quality of different electrodes on non-stationary subjects conducted in this work, or a specific EEG and BCI application.The purpose of this study was to discriminate between left- and right-hand motor imagery tasks. We recorded the brain signals from two participants using a fNIRS system and compared different feature extraction (mean, peak, minimum, skewness and kurtosis) and classification techniques (linear (LDA) and quadratic discriminant analysis (QDA), support vector machine (SVM), logistic regression, K-nearest-neighbor (KNN), and neural networks with Levenberg-Marquardt (LMA), Bayesian Regularization (BRANN) and Scaled Conjugate Gradient (SCGA) training algorithms). The results showed poor classification accuracies (98%) when mean, peak and minimum were used as features.In general, the signal chain in modern mobile Brain-Computer Interfaces (BCIs) is subdivided into at least two blocks. These are usually wirelessly connected with digital signal processing part implemented separately and often stationary. This causes a limited mobility and results in an additional, although avoidable, latency due to the wireless transmission channel. Therefore, a novel, entirely mobile FPGA-based platform for BCIs has been designed and implemented. While featuring highly efficient adaptability to targeted algorithms due to the ultra low power Flash-based FPGA, the stackable system design and the configurable hardware ensure flexibility for the use in different application scenarios. Powered through a single Li-ion battery, the miniaturized system area of half the size of a credit card leads to high mobility and thus allow for real-world scenario applicability. A Bluetooth Low Energy extension can be connected without any significant area cost, if a wireless data or control signal transmission channel is required. The resulting system is capable of acquiring and fully processing of up to 32 EEG channels with 24 bit precision each and a sampling rate of 250-16k samples per second with a total weight less than 60 g.The millennial age group (18 to 30 years) spend at least 6 hours sitting, either in college or at their workspace. High screen time as a routine, is the major cause for numerous spinal problems. Despite the wide research carried out on postural abnormalities, there exists numerous unrequited queries with regards to lumbar lordosis estimations, due to indeterminate parameters such as age, gender, lifestyle and diet. This work emphasizes the proficient method by observing the posture of a person for early detection of obliteration in Lumbar Lordosis. This further contributes to efficient diagnosis and treatment of spine ailments. With a novel approach to hardware using the myRIO hardware coupled with LabVIEW for interactive interface, the calibration is enhanced using machine learning (ML) - kNN Classifier. The use of machine learning accounts for the variations in the ideal angles of segmented sagittal measures with respect to different subjects. The device is developed to be a non-invasive, user friendly instrument to analyse the casual seated posture trends of the subject. The male subjects are expected to show the tilt angles in the range of -16.3 to -17.2 degrees and similar threshold for females are -15.8 to -16.8 degrees. Out of 120 subjects taken into consideration, the device could accurately classify subjects with obliterated or normal lumbar lordosis). An accuracy and f1- score of 94% and 90% respectively was achieved by the ML model.The current work presents the development and technical validation, in terms of accuracy and latency, of a low-cost portable device that allows identifying possible risks of falling in people when they realize spinal trunk lateral movements. The device is comprised of an Inertial Measurement Unit (IMU) located on the lower back. Measurements are processed to get meaningful parameters such as rotation angles of the back when realizing lateral movements. In order to give performance feedback while doing the test, this device includes a Microcontroller as Raspberry Pi to return visual feedback to the person. The critical system feature is the latency of the system since getting the data of a movement until showing that on the feedback screen. For that reason, before to start assessing people, we propose a technical method using the Mikrolar Hexapod Robot R3000 for validating the system developed by simulating the movement of the back and recording it with a video camera to apply an offline Motion-to-Photon Latency analysis.Freezing of gait (FOG) is a major hindrance to daily mobility and can lead to falling in people with Parkinson's disease. While wearable accelerometers and gyroscopes have been commonly used for FOG detection, foot plantar pressure distribution could also be considered for this application, given its usefulness in previous gait-based classification. This research examined 325 plantar-pressure based features and 132 acceleration-based features extracted from the walking data of five males with Parkinson's disease who experienced FOG. A set of 61 features calculated from the time domain, Fast Fourier transform (FFT), and wavelet transform (WT) were extracted from multiple input signals; including, total ground reaction force, foot centre of pressure (COP) position, COP velocity, COP acceleration, and 3D ankle acceleration. Minimum-redundancy maximum relevance (mRMR) feature selection was used to rank all features. selleck chemical Plantar-pressure based features accounted for 4 of the top 5 features (ranks 2, 3, 4, 5); the remaining feature was an ankle acceleration based feature (rank 1). The three highest ranked features were the freeze index (calculated from ankle acceleration), total power in the frequency domain (calculated using the FFT from COP velocity), and mean of the WT detail coefficients (calculated from COP velocity). This preliminary analysis demonstrated that features calculated from plantar pressure, specifically COP velocity, performed comparably to ankle acceleration features. Thus, feature sets for FOG detection may benefit from plantar-pressure based features.Spine Curvature Disorder (SCD) is a medical condition that affects the shape of the spine. Methods of monitoring SCDs involve visual inspection followed by X-rays and measurements. Once a patient is diagnosed with SCD and treatment or therapy is implemented, progress is tracked by exposing the patient to multiple periodic X-rays to determine the spine responses to treatments or therapies. Multiple exposures to X-rays is not desirable and is also costly. Therefore, we propose a new method for detecting and monitoring SCD and present our initial research results. We are implementing a non-invasive method that can detect and monitor the spinal postures of SCD. Magnets are placed on a shirt a grid form then a sensor system would be placed on the chest of the body. An on-body magnetic sensor records the sensor data values to determine if the upper body posture is straight or is curved which in turn can assist in detecting if the spine is deformed. We present our initial results on magnetic sensor testing and preliminary results using wearable sensors and a garment integrated magnetic shirt.This paper presents a novel method for tracking gaiting-based (changing contacts, reciprocal, cyclical) withinhand manipulation strategies of a human hand. We present a kinematic model that relies on data collected from 6-DOF magnetic sensors attached to 7 external sites on the hand. The sensors are calibrated by three procedures-sensor-to-fingertip, constrained fingertip workspace limits, and flat hand configuration. Subjects rotated two cubes of different sizes around the 3 object-centric axes, while a synchronized camera recorded the object motion. Hand motions were segmented and then averaged using dynamic time warping (DTW) to yield a representative time-series motion primitive for the given task. The hand movements of two subjects during cube rotation tasks were reconstructed using a 22-degree of freedom (DOF) hand kinematic model. Based on a qualitative evaluation of the joint movements, intrasubject correlations of joint angles were found.Reach-to-grasp actions have been recently studied to highlight how intentions influence action planning and shapes the movement kinematics. Reach-to-grasp (RG) kinematics can reveal important information on motor planning and control in several pathologies, including neurodegenerative diseases. Current methods are mainly based on optoelectronic analysis systems, which provide accurate movement tracking but are expensive, time-consuming, and limited to constrained research-oriented space. In this study, we proposed an innovative, non-invasive, and easy-to-use ringshaped wearable system, named SensRing, able to record inertial data during the movement. To ensure accurate and precise measures, which are mandatory for clinical practice, a preliminary technical validation of the SensRing with respect to the Vicon (i.e., gold standard for motion analysis) was performed on two finger tapping exercises. Preliminary results pointed out very low discrepancies in terms of absolute errors (AbsErr) between the values of repetitions (AbsErr≤0.8), frequency (AbsErr=0.04Hz) and amplitude (AbsErr≤2.7deg) measured by the two systems, as well as high correlation between the measures obtained with the inertial and optical system. Therefore, inertial data from the SensRing were used in a "reach-to-grasp and move" protocol to calculate the performance of a group of healthy young subjects during three RG and move sequences. Particularly, subjects were instructed to reach and grasp a bottle to drink (DRINK), to place it on the table (IND) or to pass it to another partner (SOC). Results showed that SensRing could identify that, in the RG phase, different intentions determine different kinematic parameters of grasping the same object. As concerns the phase of moving, if the movement is different (drink vs IND/SOC) it's easier to find differences between the tasks, but also when the action is the same but with different social intent (IND vs SOC) SensRing found a significant difference.
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