Design and Control of a Single-Leg Exoskeleton with Gravity Compensation for Children with Unilateral Cerebral Palsy.

Children with cerebral palsy (CP) experience reduced quality of life due to limited mobility and independence. Recent studies have shown that lower-limb exoskeletons (LLEs) have significant potential to improve the walking ability of children with CP. However, the number of prototyped LLEs for children with CP is very limited, while no single-leg exoskeleton (SLE) has been developed specifically for children with CP. This study aims to fill this gap by designing the first size-adjustable SLE for children with CP aged 8 to 12, covering Gross Motor Function Classification System (GMFCS) levels I to IV. The exoskeleton incorporates three active joints at the hip, knee, and ankle, actuated by brushless DC motors and harmonic drive gears. Individuals with CP have higher metabolic consumption than their typically developed (TD) peers, with gravity being a significant contributing factor. To address this, the study designed a model-based gravity-compensator impedance controller for the SLE. A dynamic model of user and exoskeleton interaction based on the Euler-Lagrange formulation and following Denavit-Hartenberg rules was derived and validated in Simscape™ and Simulink® with remarkable precision. Additionally, a novel systematic simplification method was developed to facilitate dynamic modelling. The simulation results demonstrate that the controlled SLE can improve the walking functionality of children with CP, enabling them to follow predefined target trajectories with high accuracy.

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions.

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future.

Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications.

Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges.

Exosomes in the Diagnosis and Treatment of Renal Cell Cancer.

Renal cell carcinoma (RCC) is the most prevalent type of kidney cancer originating from renal tubular epithelial cells, with clear cell RCC comprising approximately 80% of cases. The primary treatment modalities for RCC are surgery and targeted therapy, albeit with suboptimal efficacies. Despite progress in RCC research, significant challenges persist, including advanced distant metastasis, delayed diagnosis, and drug resistance. Growing evidence suggests that extracellular vesicles (EVs) play a pivotal role in multiple aspects of RCC, including tumorigenesis, metastasis, immune evasion, and drug response. These membrane-bound vesicles are released into the extracellular environment by nearly all cell types and are capable of transferring various bioactive molecules, including RNA, DNA, proteins, and lipids, aiding intercellular communication. The molecular cargo carried by EVs renders them an attractive resource for biomarker identification, while their multifarious role in the RCC offers opportunities for diagnosis and targeted interventions, including EV-based therapies. As the most versatile type of EVs, exosomes have attracted much attention as nanocarriers of biologicals, with multi-range signaling effects. Despite the growing interest in exosomes, there is currently no widely accepted consensus on their subtypes and properties. The emerging heterogeneity of exosomes presents both methodological challenges and exciting opportunities for diagnostic and clinical interventions. This article reviews the characteristics and functions of exosomes, with a particular reference to the recent advances in their application to the diagnosis and treatment of RCC.

Optimisation of Deep Learning Small-Object Detectors with Novel Explainable Verification.

In this paper, we present a novel methodology based on machine learning for identifying the most appropriate from a set of available state-of-the-art object detectors for a given application. Our particular interest is to develop a road map for identifying verifiably optimal selections, especially for challenging applications such as detecting small objects in a mixed-size object dataset. State-of-the-art object detection systems often find the localisation of small-size objects challenging since most are usually trained on large-size objects. These contain abundant information as they occupy a large number of pixels relative to the total image size. This fact is normally exploited by the model during training and inference processes. To dissect and understand this process, our approach systematically examines detectors' performances using two very distinct deep convolutional networks. The first is the single-stage YOLO V3 and the second is the double-stage Faster R-CNN. Specifically, our proposed method explores and visually illustrates the impact of feature extraction layers, number of anchor boxes, data augmentation, etc., utilising ideas from the field of explainable Artificial Intelligence (XAI). Our results, for example, show that multi-head YOLO V3 detectors trained using augmented data produce better performance even with a fewer number of anchor boxes. Moreover, robustness regarding the detector's ability to explain how a specific decision was reached is investigated using different explanation techniques. Finally, two new visualisation techniques are proposed, WS-Grad and Concat-Grad, for identifying explanation cues of different detectors. These are applied to specific object detection tasks to illustrate their reliability and transparency with respect to the decision process. It is shown that the proposed techniques can result in high resolution and comprehensive heatmaps of the image areas, significantly affecting detector decisions as compared to the state-of-the-art techniques tested.

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis-PAFO). Several studies have forecasted healthy gait trajectories, but, to the best of our knowledge, none have forecasted gait trajectories of children with pathological gait yet. These exhibit higher inter- and intra-subject variability compared to typically developing gait of healthy subjects. Pathological trajectories represent the typical gait patterns that rehabilitative exoskeletons and actuated orthoses would target. In this study, we implemented two deep learning models, a Long-Term Short Memory (LSTM) and a Convolutional Neural Network (CNN), to forecast hip, knee, and ankle trajectories in terms of corresponding Euler angles in the pitch, roll, and yaw form for children with neurological disorders, up to 200 ms in the future. The deep learning models implemented in our study are trained on data (available online) from children with neurological disorders collected by Gillette Children's Speciality Healthcare over the years 1994-2017. The children's ages range from 4 to 19 years old and the majority of them had cerebral palsy (73%), while the rest were a combination of neurological, developmental, orthopaedic, and genetic disorders (27%). Data were recorded with a motion capture system (VICON) with a sampling frequency of 120 Hz while walking for 15 m. We investigated a total of 35 combinations of input and output time-frames, with window sizes for input vectors ranging from 50-1000 ms, and output vectors from 8.33-200 ms. Results show that LSTMs outperform CNNs, and the gap in performance becomes greater the larger the input and output window sizes are. The maximum difference between the Mean Absolute Errors (MAEs) of the CNN and LSTM networks was 0.91 degrees. Results also show that the input size has no significant influence on mean prediction errors when the output window is 50 ms or smaller. For output window sizes greater than 50 ms, the larger the input window, the lower the error. Overall, we obtained MAEs ranging from 0.095-2.531 degrees for the LSTM network, and from 0.129-2.840 degrees for the CNN. This study establishes the feasibility of forecasting pathological gait trajectories of children which could be integrated with exoskeleton control systems and experimentally explores the characteristics of such intelligent systems under varying input and output window time-frames.

An annotated water-filled, and dry potholes dataset for deep learning applications.

Potholes have long posed a challenging risk to automated systems due to their random and stochastic shapes and the reflectiveness of their surface when filled with water, whether it is "muddy" water or clear water. This has formed a significant limitation to autonomous assistive technologies such as Electric-Powered Wheelchairs (EPWs), mobility scooters, etc. due to the risk potholes pose on the user's well-being as it could cause severe falls and injuries as well as neck and back problems. Current research proved that Deep Leaning technologies are one of the most relevant solutions used to detect potholes due to the high accuracy of the detection. One of the main limitations to the datasets currently made available is the lack of photos describing water-filled, rabble-filled, and random coloured potholes. The purpose of our dataset is to provide the answer to this problem as it contains 713 high-quality photos representing 1152 manuall-annotated potholes in different shapes, locations, colours, and conditions, all of which were manually-collected via a mobile phone and within different areas in the United Kingdom along with two additional benchmarking videos recorded via a dashcam.

Train vs. Play: Evaluating the Effects of Gamified and Non-Gamified Wheelchair Skills Training Using Virtual Reality.

This study compares the influence of a gamified and a non-gamified virtual reality (VR) environment on wheelchair skills training. In specific, the study explores the integration of gamification elements and their influence on wheelchair driving performance in VR-based training. Twenty-two non-disabled participants volunteered for the study, of whom eleven undertook the gamified VR training, and eleven engaged in the non-gamified VR training. To measure the efficacy of the VR-based wheelchair skills training, we captured the heart rate (HR), number of joystick movements, completion time, and number of collisions. In addition, an adapted version of the Wheelchair Skills Training Program Questionnaire (WSTP-Q), the Igroup Presence Questionnaire (IPQ), and the Simulator Sickness Questionnaire (SSQ) questionnaires were administered after the VR training. The results showed no differences in wheelchair driving performance, the level of involvement, or the ratings of presence between the two environments. In contrast, the perceived cybersickness was statistically higher for the group of participants who trained in the non-gamified VR environment. Remarkably, heightened cybersickness symptoms aligned with increased HR, suggesting physiological connections. As such, while direct gamification effects on the efficacy of VR-based wheelchair skills training were not statistically significant, its potential to amplify user engagement and reduce cybersickness is evident.

A pixel-wise annotated dataset of small overlooked indoor objects for semantic segmentation applications.

The purpose of the dataset is to provide annotated images for pixel classification tasks with application to powered wheelchair users. As some of the widely available datasets contain only general objects, we introduced this dataset to cover the missing pieces, which can be considered as application-specific objects. However, these objects of interest are not only important for powered wheelchair users but also for indoor navigation and environmental understanding in general. For example, indoor assistive and service robots need to comprehend their surroundings to ease navigation and interaction with different size objects. The proposed dataset is recorded using a camera installed on a powered wheelchair. The camera is installed beneath the joystick so that it can have a clear vision with no obstructions from the user's body or legs. The powered wheelchair is then driven through the corridors of the indoor environment, and a one-minute video is recorded. The collected video is annotated on the pixel level for semantic segmentation (pixel classification) tasks. Pixels of different objects are annotated using MATLAB software. The dataset has various object sizes (small, medium, and large), which can explain the variation of the pixel's distribution in the dataset. Usually, Deep Convolutional Neural Networks (DCNNs) that perform well on large-size objects fail to produce accurate results on small-size objects. Whereas training a DCNN on a multi-size objects dataset can build more robust systems. Although the recorded objects are vital for many applications, we have included more images of different kinds of door handles with different angles, orientations, and illuminations as they are rare in the publicly available datasets. The proposed dataset has 1549 images and covers nine different classes. We used the dataset to train and test a semantic segmentation system that can aid and guide visually impaired users by providing visual cues. The dataset is made publicly available at this link.

Wearable Lower-Limb Exoskeleton for Children With Cerebral Palsy: A Systematic Review of Mechanical Design, Actuation Type, Control Strategy, and Clinical Evaluation.

Children with a neurological disorder such as cerebral palsy (CP) severely suffer from a reduced quality of life because of decreasing independence and mobility. Although there is no cure yet, a lower-limb exoskeleton (LLE) has considerable potential to help these children experience better mobility during overground walking. The research in wearable exoskeletons for children with CP is still at an early stage. This paper shows that the number of published papers on LLEs assisting children with CP has significantly increased in recent years; however, no research has been carried out to review these studies systematically. To fill up this research gap, a systematic review from a technical and clinical perspective has been conducted, based on the PRISMA guidelines, under three extended topics associated with "lower limb", "exoskeleton", and "cerebral palsy" in the databases Scopus and Web of Science. After applying several exclusion criteria, seventeen articles focused on fifteen LLEs were included for careful consideration. These studies address some consistent positive evidence on the efficacy of LLEs in improving gait patterns in children with CP. Statistical findings show that knee exoskeletons, brushless DC motors, the hierarchy control architecture, and CP children with spastic diplegia are, respectively, the most common mechanical design, actuator type, control strategy, and clinical characteristics for these LLEs. Clinical studies suggest ankle-foot orthosis as the primary medical solution for most CP gait patterns; nevertheless, only one motorized ankle exoskeleton has been developed. This paper shows that more research and contribution are needed to deal with open challenges in these LLEs.