The ability to accomplish a sit-to-stand (STS) motion is key to increase functional mobility and reduce rehospitalization risks. While raising aid (transfer) devices and partial bodyweight support (rehabilitation) devices exist, both are unable to adjust the STS training to different mobility levels. Therefore, We have developed an STS training device that allows various configurations of impedance and vertical/forward forces to adapt to many training needs while maintaining commercial raising aid transfer capabilities. Experiments with healthy adults (both men and women) of various heights and weights show that the device 1) has a low impact on the natural STS kinematics, 2) can provide precise weight unloading at the patient's center of mass and 3) can add a forward virtual spring to assist the transfer of the bodyweight to the feet for seat-off, at the start of the STS motion. Keywords: Rehabilitation robotics, Force control, Human-robot interaction, Patient transfer, Floor-lift1. INTRODUCTION For patients in movement rehabilitation, accomplishing functional tasks is key to increasing quality of life and reducing the risk of rehospitalization [1, 2]. Training sit-to-stands (STS) is particularly useful as it has a significant correlation with increasing patient muscle power and the balance required to perform standing and walking tasks [3, 4]. Frequent training is essential to prevent muscle atrophy. However, studies indicate that up to 65% of patients hospitalized in short-term care for seven days or longer develop muscle weakness due to prolonged immobility [5]. This is partly due to the current shortage of qualified clinical staff in hospital settings [6]. Clinical staff can use passive lifts to assist the patient's STS motion, such as the Guldmann GPT1 or the ARJO Sara Stedy, which hold the patient's and knees in a fixed position to reduce fall risks. However, using passive lifts for STS training can be exhaustive and lead to injuries for the clinical staff since it relies on them to move the patient's center of mass (CoM) [7, 8].

Rapid and reliable incident detection is critical for reducing crash-related fatalities, injuries, and congestion. However, conventional methods, such as closed-circuit television, dashcam footage, and sensor-based detection, separate detection from verification, suffer from limited flexibility, and require dense infrastructure or high penetration rates, restricting adaptability and scalability to shifting incident hotspots. To overcome these challenges, we developed DARTS, a drone-based, AI-powered real-time traffic incident detection system. DARTS integrates drones' high mobility and aerial perspective for adaptive surveillance, thermal imaging for better low-visibility performance and privacy protection, and a lightweight deep learning framework for real-time vehicle trajectory extraction and incident detection. The system achieved 99% detection accuracy on a self-collected dataset and supports simultaneous online visual verification, severity assessment, and incident-induced congestion propagation monitoring via a web-based interface. In a field test on Interstate 75 in Florida, DARTS detected and verified a rear-end collision 12 minutes earlier than the local transportation management center and monitored incident-induced congestion propagation, suggesting potential to support faster emergency response and enable proactive traffic control to reduce congestion and secondary crash risk. Crucially, DARTS's flexible deployment architecture reduces dependence on frequent physical patrols, indicating potential scalability and cost-effectiveness for use in remote areas and resource-constrained settings. This study presents a promising step toward a more flexible and integrated real-time traffic incident detection system, with significant implications for the operational efficiency and responsiveness of modern transportation management.

Robotic tactile perception is a complex process involving several computational steps performed at different levels. Tactile information is shaped by the interplay of robot actions, the mechanical properties of its body, and the software that processes the data. In this respect, high-level computation, required to process and extract information, is commonly performed by adapting existing techniques from other domains, such as computer vision, which expects input data to be properly structured. Therefore, it is necessary to transform tactile sensor data to match a specific data structure. This operation directly affects the tactile information encoded and, as a consequence, the task execution. This survey aims to address this specific aspect of the tactile perception pipeline, namely Data Representation. The paper first clearly defines its contributions to the perception pipeline and then reviews how previous studies have dealt with the problem of representing tactile information, investigating the relationships among hardware, representations, and high-level computation methods. The analysis has led to the identification of six structures commonly used in the literature to represent data. The manuscript provides discussions and guidelines for properly selecting a representation depending on operating conditions, including the available hardware, the tactile information required to be encoded, and the task at hand.

To reproduce natural standing-up motion, recent studies have emphasized the importance of coordination between the assisting robot and the human. However, many non-wearable assistive devices have struggled to replicate natural motion trajectories. While wearable devices offer better coordination with the human body, they present challenges in completely isolating mechanical and electrical hazards. To address this, we developed a novel standing-assist robot that integrates features of both wearable and non-wearable systems, aiming to achieve high coordination while maintaining safety. The device employs a four-link mechanism aligned with the human joint structure, designed to reproduce the S-shaped trajectory of the hip and the arc trajectory of the knee during natural standing-up motion. Subject-specific trajectory data were obtained using a gyroscope, and the link lengths were determined to drive the seat along the optimal path. A feedforward speed control using a stepping motor was implemented, and the reproducibility of the trajectory was evaluated based on the geometric constraints of the mechanism. A load-bearing experiment with weights fixed to the seat was conducted to assess the trajectory accuracy under different conditions. Results showed that the reproduction errors for the hip and knee trajectories remained within approximately 4 percent of the seat's total displacement, demonstrating high fidelity to the target paths. In addition, durability testing, thermal safety evaluation, and risk assessment confirmed the reliability and safety of the system for indoor use. These findings suggest that the proposed design offers a promising approach for developing assistive technologies that adapt to individual physical characteristics, with potential applications in elderly care and rehabilitation.

The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.

Over the past few years, the division of gait phases has emerged as a complex area of research that carries significant importance for various applications in the field of gait technologies. The accurate partitioning of gait phases plays a crucial role in advancing these applications. Researchers have been exploring a range of sensors that can be employed to provide data for algorithms involved in gait phase partitioning. These sensors can be broadly categorized into two types: wearable and non-wearable, each offering unique advantages and capabilities. In our study aimed at examining the current approaches to gait analysis and detection specifically designed for implementation in ambulatory rehabilitation systems, we conducted a comprehensive meta-analysis of existing research studies. Our analysis revealed a diverse range of sensors and sensor combinations that demonstrate the ability to analyze gait patterns in ambulatory settings. These sensor options vary from basic force-based binary switches to more intricate setups incorporating multiple inertial sensors and sophisticated algorithms. The findings highlight the wide spectrum of available technologies and methodologies used in gait analysis for ambulatory applications. To conduct an extensive review, we systematically examined two prominent databases, IEEE and Scopus, with the aim of identifying relevant studies pertaining to gait analysis. The search criteria were limited to 189 papers published between 1999 and 2023. From this pool, we identified and included five papers that specifically focused on various techniques including Thresholding, Quasi-static method, adaptive classifier, and SVM-based approaches. These selected papers provided valuable insights for our review.

Accurate and rapid detection of gait phases is of utmost importance in achieving optimal performance of powered lower-limb prostheses and exoskeletons. With the increasing versatility and complexity of these robotic systems, there is a growing need to enhance the performance of gait detection algorithms. The development of reliable and functional gait detection algorithms holds the potential to enhance precision, stability, and safety in prosthetic devices and other rehabilitation technologies. In this systematic review, we delve into the extensive body of research and development in the domain of gait event detection methods, with a specific focus on their application to prosthetic devices. Our review critically assesses various proposed methods, aiming to identify the most effective approaches for gait phase classification in lower limb robotic systems. Through a comprehensive comparative analysis, we highlight the strengths and weaknesses of different algorithms, shedding light on their performance characteristics, applicability, and potential for further improvements. This comprehensive review was conducted by screening two databases, namely IEEE and Scopus. The search was limited to 204 papers published from 2010 to 2023. A total of 6 papers that focused on Heuristic, Thresholding, and Amplitude Zero Crossing involved techniques were identified and included in the review. 33.3% of implemented Algorithms used kinematic parameters such as joint angles, joint linear and angular velocity, and joint angular acceleration. This study purely focuses on threshold-based algorithms and thus paper focusing on other gait phase detection methods were excluded.

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%.

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.