Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic multi-structure pediatric knee bone segmentation using optimal multi-level Otsu thresholding to tackle intensity homogeneity in bone structures

Recent studies in medical image segmentation involve new automatic approaches where active learning models are useful with less training samples. Presence of homogenous and heterogenous intensities for a single anatomical structure in pediatric musculoskeletal MR images affects the accuracy in terms of segmentation and classification of labels. This study addresses the homogeneity in intensity issues and introduces a new pre-training pipeline framework of Multi-level Otsu thresholding image as separate channel for 3D UNet model training. The proposed framework achieved higher performance of up to 85% when compared with the Baseline 3D UNet model and the Histogram threshold with 3D UNet. All algorithms are run through MONAI core framework.Clinical Relevance— This study will be of major interest to practicing pediatric clinicians and surgeons for its ability to provide accurate morphological assessment of underlying musculoskeletal structure. For researchers, it provides a new approach in dealing with heterogeneity in intensity problem which is common in pediatric MR imaging.
Differentiation in additive and multiplicative inputs to motoneuron pool as origins of spasticity – a neuromorphic modeling study *

Spasticity is characterized by a velocity-dependent increase in the tonic stretch reflex. Evidence suggests that spasticity originates from hyperactivity in the descending tract or reflex loop. To pinpoint the source of hyperactivity, however, is difficult due to lack of human data in-vivo. Thus, we implemented a neuromorphic model to revive the neurodynamics with spiking neuronal activity. Two types of input were modeled: (1) the additive condition (ADD) to apply tonic synaptic inputs directly into the reflex loop; (2) the multiplicative (MUL) condition to adjust the loop gains within the reflex loop. Results show that both conditions produced antagonist EMG responses resembling patient data. The timing of spasticity is more sensitive to the ADD condition, whereas the amplitude of spastic EMG is more sensitive to the MUL condition. In conclusion, our model shows that both additive and multiplicative hyperactivities suffice to elicit velocity-dependent spastic electromyographic signals (EMG), but with different sensitivities. This simulation study suggests that spasticity caused by different origins may be discernable by the progression of severity, which may help individualized goalsetting and parameter-selection in rehabilitation.Clinical Relevance—Potential application of neuromorphic modeling on spasticity includes selection of parameters for therapeutic plans, such as movement range, repetition, and load.
Effects of Pulse Transit Time and Pulse Arrival Time on Cuff-less Blood Pressure Estimation: A Comparison Study with Multiple Experimental Interventions

Pulse transit time (PTT) has shown a correlation with blood pressure (BP), and it is considered as a potential marker for cuff-less BP estimation. However, pulse arrival time (PAT) including pre-ejection period (PEP) has been utilized more widely because of its convenience to acquisition and calculation. In spite of this, whether PAT can surrogate PTT has been a controversial topic for many years. In this study, we designed an experiment on 55 subjects with multiple interventions, those may cause the changes in BP and PEP. We analyzed the linear and nonlinear correlations between BP and PTT/PAT, and also assessed the performances of PTT-based and PAT-based models on tracking the BP variation. Five typical BP estimation models were used for comparison. We found that PEP could change rapidly in response to the interventions related with physical stress. Although PTT had a better linear correlation with BP, most of the PAT-based models showed more accuracy than PTT-based models in all of the interventions, especially for the calibrated models. It is suggested that PAT has the potential to predict BP, and the inclusion of PEP in the measurement of PAT is necessary.
Maximizing Ranking-Aware Recommendation Quality for Low-Complexity Network-Friendly Recommendation

Network-friendly recommendation (NFR) has been identified as a promising method to facilitate the network performance. However, the current NFR approaches introduce non-negligible computation overhead and primarily focus on the content to recommend, while neglecting the impact of ranking among contents, sharing limited recommendation quality. In this paper, we aim to design a recommendation re-ranking algorithm to maximize the ranking-aware recommendation quality while satisfying the network constraint. We formulate the problem as an integer programming (IP) problem that maximizes the mean reciprocal rank of the baseline recommendation under network constraint. To address the computational challenge posed by the large-scale IP problem, we propose a low-complexity algorithm that enables real-time NFR. Specifically, our approach reduces the computational complexity by re-ranking only the measurement list, instead of the entire content list. Empirical results on real-world datasets demonstrate that compared with state-of-the-art NFR baselines, the proposed scheme delivers an improvement of more than 4.8% in both precision and coverage, while reducing 97.5% computation time.
Investigating the Impact of Confined Space Factors on Signal Propagation

Positioning-based systems are used in various areas of people's lives. Monitoring and tracking the movement and location of people and other entities is widespread within the outdoor environment. Over the last few years, the emphasis has been on implementing systems for determining the location and movement of people and entities in confined spaces. Confined space positioning systems are gaining importance for the navigation of people in various types of facilities where high numbers of people are present during operational hours. Also, indoor positioning systems are growing in importance due to the need to implement them in articulated facilities, which include airports, medical facilities, shopping malls and others, in order to improve security or optimize processes in these facilities. Currently, indoor location systems are a useful tool in the context of preventing and limiting the spread of COVID-19 and other respiratory diseases. In this article, the results of testing an indoor positioning system based on Bluetooth Low Energy technology using Bluetooth beacons are interpreted in terms of investigating the signal propagation when using obstacles made of different materials.
Generalizability of Human Activity Recognition Machine Learning Models from non-Parkinson's to Parkinson's Disease Patients

Recent evidence shows that high-intensity exercises reduce tremors and stiffness in Parkinson's disease (PD). However, there is insufficient evidence on the types of exercises; in effect, high-intensity may be a personalized measure. Recent progress in automated Human Activity Recognition using machine learning (ML) models shows potential for better monitoring of PD patients. However, ML models must be calibrated to ignore tremors and accurately identify activity and its intensity. We report findings from a study where we trained ML models using data from medically validated triple synchronous sensors connected to 8 non-PD subjects performing 32 exercises. We then tested the models to identify exercises performed by 8 PD patients at different stages of the disease. Our analysis shows that better data preprocessing before modeling can provide some model generalizability. However, it is extremely challenging, as the models work with high accuracy on one group (Healthy or PD patients) (F1=0.88-0.94) but not on both groups.Clinical relevance-Patients with Parkinson's and other motor-generative diseases can now accurately measure physical activity with machine learning approaches. Clinicians, caregivers, and apps can make accurate, personalized exercise recommendations to augment medications that reduce tremors and stiffness.
POMDP-Guided Active Force-Based Search for Robotic Insertion

In robotic insertion tasks where the uncertainty exceeds the allowable tolerance, a good search strategy is essential for successful insertion and significantly influences efficiency. The commonly used blind search method is time-consuming and does not exploit the rich contact information. In this paper, we propose a novel search strategy that actively utilizes the information contained in the contact configuration and shows high efficiency. In particular, we formulate this problem as a Partially Observable Markov Decision Process (POMDP) with carefully designed primitives based on an in-depth analysis of the contact configuration's static stability. From the formulated POMDP, we can derive a novel search strategy. Thanks to its simplicity, this search strategy can be incorporated into a Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are realized through a low-level Cartesian Impedance Controller. Our method is based purely on the robot's proprioceptive sensing and does not need visual or tactile sensors. To evaluate the effectiveness of our proposed strategy and control framework, we conduct extensive comparison experiments in simulation, where we compare our method with the baseline approach. The results demonstrate that our proposed method achieves a higher success rate with a shorter search time and search trajectory length compared to the baseline method. Additionally, we show that our method is robust to various initial displacement errors.
Comparative Performance of Pre-Trained CNN Architectures on Dermoscopic Pigmented Skin Lesions Classification

Pigmented skin lesions are skin disorders with increased incidence in the last decade. Early detection and accurate diagnosis can reduce mortality. Deep learning, particularly the Convolution Neural Network (CNN), has reached impressive results for many purposes involving image classification. The use of CNN architecture does not require pre-processing at the level of an image as that of traditional methods. The required pre-processing is only an augmentation to increase the data needed for model training. This work compared ten pre-trained CNN architectures: Inception-V3, ResNet-50v2, ResNet- 152v2, InceptionResNet-v2, DenseNet-201, Xception, MobileNet, MobileNet-v2, NASNetLarge and EfficientNet-B7 to determine their performance in classifying pigmented skin lesions. The dataset is ISIC 2019, with eight disease classes that apply data augmentation and balancing. The architectures were evaluated in a multiclass statistical evaluation based on their accuracy, sensitivity, specificity, precision, F-score, and AUC. The findings of this research those four pre-trained CNN architectures, namely Inception-v3, InceptionResNet-v2, NASNetLarge, and DenseNet- 201, showed superior performance with AUC values above 0.90.
Simulation and Analysis of Venturi Tubes for Use in Duct Wind Turbine Power Plants

This paper discusses the venturi tube used as a duct wind turbine power plant. The design of the power plant using duct wind turbine method is another alternative in exploiting the potential of wind energy as an environmentally friendly power plant. Before the design of the electric generator is carried out, a simulation and analysis of the geometry of the venturi tube is carried out. The simulations is to find the optimum design for wind speed and energy generated. In this paper, four designs are made, namely a straight design, a bent design on the input channel, a bent design on the input and output channels, and a bent design without sharp corners. To help analyze the designs, the Ansys Fluent is used. The simulation results show that the highest airflow velocity is located in the throat channel and the average velocity ratio between the input channel and the venturi channel is around 5x. Venturi Tubes with Bent Shape on the input and output channels without sharp angles can reduce turbulence in all parts of the channel.
Human-in-the-Loop Optimization of Active Back-Support Exoskeleton Assistance Via Lumbosacral Joint Torque Estimation

The assistive profile of an active back support exoskeleton is strongly dependent on the manual tuning of controller gains based on previous experience and trial-and-error. Human-in-the-loop (HIL) optimization allows for automatic tuning of assistive profiles to different subjects. Most HIL methods make use of intrusive sensors that could affect out-of-the-lab exoskeleton adoption. Therefore, we propose a HIL-based assistive controller architecture using only one single IMU that can be easily embedded in any exoskeleton system. To validate our algorithm we recruited 3 subjects and asked them to perform a series of successive load liftings. Meanwhile, we analysed the back-muscles activations focusing on cumulative activation (iEMG), and median activation. We also monitored the total torque generated by the exoskeleton. With respect to an assistance-less condition, the proposed controller resulted in up to 19% reduction of the back-muscles activity. Moreover, compared to a state-of-the-art controller that produced up to 15% reduction of the back-muscles activity, the new controller also required generation of 4% less exoskeleton torque.
Towards non-invasive peripheral stimulation as a treatment for Parkinson’s disease gait

Non-invasive coordinated reset stimulation (CRS) to the hands has been shown to improve motor ability in Parkinson’s patients, but not specific for gait disturbances. The overall aim of the project is the application of vibrotactile CRS to the feet to improve gait impairments in Parkinson’s disease. As a first step towards this objective, we showed that vibrotactile stimulation to the feet can elicit a cortical response and have identified differences in younger and older individuals. Our findings suggest the potential for non-invasive peripheral stimulation as a therapeutic technique.Clinical Relevance— This is an important step towards developing a non-invasive stimulation technique for the management of gait disturbances in Parkinson’s disease.
Rethinking Transmit Power Control for SAE J3161/1 Congestion Control Algorithm

The Society of Automotive Engineers (SAE) has specified wireless channel congestion control algorithm for vehicle-to-everything (V2X) communication: J2945/1 for Dedicated Short Range Communication (DSRC) and J3161/1 for cellular V2X (C-V2X) environment. Although J2945/1 utilizes both transmit (Tx) power control and message rate control, the more recently specified J3161/1 standard for C-V2X environment has opted to abandon the power control. It is understandable as previous studies have demonstrated the inefficacy of power control in J2945/1, resulting in the rate control solely fighting congestion. Consequently, channel congestion leads to a fast increase in packet inter-reception time (PIR) and potentially tracking error. This letter advocates for the reinstatement of Tx power control in J3161/1 as a means to minimize PIR, while proposing a distinct approach from J2945/1 to show the feasibility of an effective combined control. Based on vehicle traffic engineering principles, the approach switches control modes to use power control to cope with increased vehicle density and rate control with increased channel utilization. It leads to significant improvements in packet delivery characteristics while more effectively managing channel load.
Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning

We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i.e. fault detection). Current IRL methods for fault detection, however, either use a linear reward representation or require extensive sampling from the environment to estimate the policy, rendering them inappropriate for safety-critical situations where sampling of failure observations via fault injection can be expensive and dangerous. To address this issue, this paper proposes a zero-shot and exogenous fault detector based on an approximate variational reward imitation learning (AVRIL) structure. The fault detector recovers a reward signal as a function of externally observable information to describe the normal operation, which can then be used to detect anomalies caused by faults. Our method incorporates expert knowledge through a customizable reward prior distribution, allowing the fault detector to learn the reward solely from normal operation samples, without the need for a simulator or costly interactions with the environment. We evaluate our approach for exogenous partial fault detection in multi-stage robotic manipulator tasks, comparing it with several baseline methods. The results demonstrate that our method more effectively identifies unseen faults even when they occur within just three controller time steps.
Continuous Erroneous Feedback Processing during Deviation from the Road within a 2D Steering Task*

Recent research of our group indicated that erroneous feedback processing can not only be detected via established correlates in the electroencephalogram (EEG) for discrete stimuli, but also arises as modulations of the brain signals when faced with a continuous and periodic error signal. However, limitations in our previous paradigm prevented a definitive statement on the error signal as the exclusive source of the modulations, as well as on the connection between the observed error-related negativity (ERN)-like and error positivity (Pe)-like continuous correlates. Within a new paradigm involving EEG recordings of 10 participants, we disentangled modulation sources, substantiating our hypothesis that the observed periodicity arises primarily due to feedback processing. Further, we provide evidence that the continuous ERN- and Pe-like potentials are locked to separate phases in the error signal, rather than time-locked to a shared event, indicating that both potentials arise independently of one another.
A super-resolution ultrasound imaging method based on active-modulated super-resolution optical fluctuation imaging

Super resolution ultrasound imaging (SR-US) methods including super-resolution optical fluctuation imaging (SOFI) have been successfully demonstrated to improve imaging performance of ultrasound (US). However, the imaging quality of US improved by conventional SOFI depends on the probability of microbubbles (MB) appearing in imaging regions. Current SOFI-based ultrasound imaging methods usually fix the probability of MBs, ignoring the effect of probability characteristics, leading to artifacts in high-order SOFI images. Inspired by active-modulated SOFI (AR-SOFI), in this paper, we propose a new method, termed as AR-SOFI-US, for further improving the performance of SR-US, which is achieved by effectively controlling the probabilities of MBs on an appropriate range. Through a series of numerical simulations, we compare the imaging resolution at differing MB probabilities and demonstrate that by controlling the probabilities of MBs when they appear in the imaging regions, incorporating the proposed AR-SOFI-US method, we can improve the spatial resolution of SR-US to a higher degree, especially for the high-order SOFI imaging results.
Enhancing User Detection via SS Burst Repetition in 5G Millimeter Wave Systems

Fifth-generation millimeter wave (mmWave) systems deploy spatial beam search using synchronization signal (SS) blocks for initial access (IA). Technique based on narrow beam sweeping procedure completed in a single SS burst is optimum for idle users that have arrived before IA. With a maximum IA duration of 5ms specified by 3GPP Release 15, the probability of user arrival during IA is non-trivial. For these users, existing search procedures will result in significant discovery delays and low detection probability. This work considers an alternative scenario wherein the user arrives after the commencement of IA. In such cases, a single SS burst may not suffice and one must consider repeating the SS burst to enhance user detection probability and reduce IA delay. Repetition of SS burst brings the trade-off between half-power beamwidth (HPBW) and the number of SS blocks per burst i.e., large HPBW supports a smaller number of SS blocks per burst and vice-versa. To achieve high accuracy of angular position with SS burst repetition, a grating-based search has been proposed which utilizes antenna element spacing greater than half-wavelength. This paper presents an IA delay, location error and detection analysis for both scenarios; with and without SS burst repetition. Simulation results demonstrate the enhanced performance of the proposed grating search technique with SS burst repetition in terms of user detection, reduced IA delay, and low angular position error.
A modular system for the synchronized multimodal data acquisition during Awake Surgery: towards the emergence of a dedicated clinical database*

Awake Surgery (AS) is considered the best treatment for brain tumors located in or near eloquent areas. During this intervention, Direct Electrical Stimulations (DES) are delivered by the surgeon on the patient’s brain in order to obtain an accurate brain mapping of the patient. The patient is asked to perform various tasks (e.g. counting, object naming, emotion recognition) through neuropsychological tests during these stimulations. These DES may cause a reversible lesion inducing deficits on the patient which can be observed during these tasks by the medical staff. The resection is then performed or not according to the patient’s response. The intraoperative deficits can take several forms and can be difficult to analyze and identify. The development of new solutions allowing the automatic detection of these deficits could be therefore essential. However, still today, no structured and organized AS dedicated database is available that could be used to train and test these algorithms. We propose a modular system allowing the synchronized multimodal acquisition of various information including physiological measurements, DES signals and parameters, and task-related data to create such database.Clinical relevance— Acquiring synchronized multimodal data during AS will allow the creation of a dedicated database that could then be used to reveal new correlations between DES and the patient’s response, and to develop and test new algorithms for the automatic detection of deficits.