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.

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.

The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increasingly being utilised in gait analysis and prediction. This review paper systematically explores the current use of intelligent algorithms in gait analysis for robotic control, specifically the control of active lower limb exoskeletons and orthoses. Two databases, IEEE and Scopus, were screened for papers published between 1989 to May 2020. 41 papers met the eligibility criteria and were included in this review. 66.7% of the identified studies used classification models for the classification of gait phases and locomotion modes. Meanwhile, 33.3% implemented regression models for the estimation/prediction of kinematic parameters such as joint angles and trajectories, and kinetic parameters such as moments and torques. Deep learning algorithms have been deployed in ~15% of the machine learning implementations. Other methodological parameters were reviewed, such as the sensor selection and the sample sizes used for training the models.

Prostate cancer, a leading cause of cancer-related mortality among men, often presents challenges in accurate diagnosis and effective monitoring. This systematic review explores the potential of exosomal biomolecules as noninvasive biomarkers for the diagnosis, prognosis, and treatment response of prostate cancer. A thorough systematic literature search through online public databases (Medline via PubMed, Scopus, and Web of science) using structured search terms and screening using predefined eligibility criteria resulted in 137 studies that we analyzed in this systematic review. We evaluated the findings from these clinical studies, revealing that the load of exosomes in the blood and urine of prostate cancer patients, which includes microRNAs (miRNAs), proteins, and lipids, demonstrates disease-specific changes. It also shows that some exosomal markers can differentiate between malignant and benign hyperplasia of the prostate, predict disease aggressiveness, and monitor treatment efficacy. Notably, miRNA emerged as the most frequently studied biomolecule, demonstrating superior diagnostic potential compared to traditional methods like prostate-specific antigen (PSA) testing. The analysis also highlights the pressing need for a standardised analytic approach through multi-centre studies to validate the full potential of exosomal biomarkers for the diagnosis and monitoring of prostate cancer.

Gait trajectory prediction models have several applications in exoskeleton control; they can be used as feed-forward input to low-level controllers and to generate reference/target trajectories for position-controlled exoskeletons. In our study, we implement four deep learning models (LSTM, FCN, CNN and Transformer) that perform one-step-ahead gait trajectory prediction after training on gait patterns of typically developing children. We propose a methodology that optimises for stability in long-term forecasts, and evaluate the performance of the models on typically developing (TD) and Cerebral Palsy (CP) gait during recursive prediction of 200 time-steps in the future (which may lead to propagation of errors) and in the presence of varying levels of Gaussian noise (1%-5%). Results on TD gait show that the FCN and Transformer, with mean absolute errors (MAEs) for one-step-ahead predictions between 1.17°-1.63°, are the most suitable for the intended application. We also proposed an approach for generating adaptive trajectories that can be used as reference trajectories for position-controlled exoskeletons. Gait patterns from children with Cerebral Palsy were fed into gait trajectory prediction models trained on typically developing gait only, to generate corrective patterns. Preliminary results show that the gait patterns of typically developing children were introduced onto the generated trajectories.

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.