Given the increasing challenges in today’s healthcare landscape, the role of health promotion and care delivery in home settings is gaining importance. Social robots have emerged as promising tools to support this shift, offering assistance, motivation, and companionship to patients and caregivers. However, their integration into home-based healthcare remains limited. To understand the underlying reasons, this study systematically reviews the literature, identifying the enablers and barriers to the deployment and use of social robots in home environments. Seven electronic databases (Medline, Embase, Web of Science, CINAHL, PsycINFO, Scopus, and Google Scholar) were searched in June 2023 and July 2024. After screening and eligibility assessment, 39 studies, involving actual human-robot interaction and conducted in real home environments, were included and appraised using the Mixed Methods Appraisal Tool. Data extracted from these studies were synthesized thematically. The results show that all studies were conducted in high-income countries, with most focusing on older adults and employing high-cost, anthropomorphic robots that were rarely co-designed with users. The findings suggest that the deployment and use of social robots are shaped by an interplay of the characteristics of interaction, context, robot, and user. They also point to a lack of holistic consideration of these characteristics, limited attention to ethical and legal aspects, and insufficient stakeholder inclusion in current design and implementation practices. To address these limitations, future research may benefit from ecological, participatory, speculative, and performative design approaches that support the development of more inclusive, adaptive, and ethical social robots for home-based healthcare.

Post-Consumer-Recycled (PCR) thermoplastics exhibit inconsistent material properties across batches due to impurities and material degradation. Achieving consistent part-quality attributes in processing different batches of PCR requires continuous adjustment of the state-of-the-art injection molding process.In our work, we present a learning-enabled nonlinear model predictive controller (NMPC) for cavity pressure that updates its model after each injection molding cycle, combined with a learning-enabled part-mass controller that serves as its reference generator. Within the NMPC, we use a physics-based model of ordinary nonlinear differential equations. The model parameters are updated between each injection molding cycle using a sequential quadratic programming approach. We incorporate constraints into the NMPC to prevent issues such as cavity-pressure peaks. The model used inside the part-mass controller is a Gaussian process regression model that leverages a cycle-variant kernel function to account for varying material properties.We test the proposed control algorithm on a plate-mold geometry, processing both virgin polypropylene and multiple batches of PCR material. While transitioning between virgin and two PCR batches over 50 production cycles without interrupting the injection molding process, the NMPC model and the cavity-pressure reference are automatically adjusted, maintaining a mean part-mass deviation of 0.21% relative to the part-mass reference. The results show strong potential for automated process-model adaptation and part-mass control when transitioning between virgin material and different PCR batches.

Adaptive motor control and seamless coordination of muscle actions in response to external perturbations are crucial to maintaining balance during bipedal locomotion. There is an ongoing debate about the specific roles of individual muscles and underlying neural control circuitry that humans employ to maintain balance in different perturbation scenarios. To advance our understanding of human motor control in perturbation recovery, we conducted a study using a portable Angular Momentum Perturbator (AMP). Unlike other push/pull perturbation systems, the AMP can generate perturbation torques on the upper body while minimizing the perturbing forces at the center of mass. In this study, ten participants experienced trunk perturbations during either the mid-stance or touchdown phase in two frontal plane directions (ipsilateral and contralateral). We recorded and analyzed the electromyography (EMG) activity of eight lower-limb muscles from both legs to examine muscular responses in different phases and directions. Based on our findings, individuals primarily employ long-latency hip strategies to effectively counteract perturbation torques, with the occasional use of ankle strategies. Furthermore, it was found that proximal muscles, particularly the biarticular Rectus Femoris, consistently exhibited higher activation levels than other muscles. Additionally, in instances where a statistically significant difference was noted, we observed that the fastest reactions generally stem from muscles in close proximity to the perturbation site. However, the temporal sequence of muscles’ activation depends on the timing and direction of the perturbation. These findings enhance reflex response modeling, aiding the development of simulation tools for accurately predicting exogenous disturbances. Additionally, they hold the potential to shape the development of assistive devices, with implications for clinical interventions, particularly for the elderly.

Until today, in the field of motor learning and rehabilitation, haptic controllers were mostly limited to teach simple tasks such as movements along straight lines, curves, or circles. However, commonly, real-life tasks consist of more complex movements such as in writing, rehabilitation, or surgery. In this paper, a novel haptic controller for robot-assisted learning is introduced. This hybrid path controller can cope with interfering path sections, while it also incorporates the common requirements of effective motor learning: it allows freedom for making spatial errors, free timing to explore the task dynamics, and adaptation to the current skill level of the user. In a practicability study with two different robots, results confirmed the full functionality of the controller and its applicability for a broad range of complex movements.

Robotic rehabilitation can deliver high-dose gait therapy and improve motor function after a stroke. However, for many devices, high costs and lengthy setup times limit clinical adoption. Thus, we designed, built, and evaluated the Passive Mechanical Add-on for Treadmill Exercise (P-MATE), a low-cost passive end-effector add-on for treadmills that couples the movement of the paretic and non-paretic legs via a reciprocating system of elastic cables and pulleys. Two human-device mechanical interfaces were designed to attach the elastic cables to the user. The P-MATE and two interface prototypes were tested with a physical therapist and eight unimpaired participants. Biomechanical data, including kinematics and interaction forces, were collected alongside standardized questionnaires to assess usability and user experience. Both interfaces were quick and easy to attach, though user experience differed, highlighting the need for personalization. We also identified areas for future improvement, including pretension adjustments, tendon derailing prevention, and understanding long-term impacts on user gait. Our preliminary findings underline the potential of the P-MATE to provide effective, accessible, and sustainable stroke gait rehabilitation.

Angular momentum, kinetics, and energetics, including total mechanical energy and its rate of change in relation to power exchange, are important quantities when analyzing human motion in sports, physical labor, and rehabilitation. Inertial measurement units (IMU)-based motion capture (MOCAP) systems provide a portable solution for the ambulatory analysis of these quantities which optical MOCAP systems do not offer. Yet, evaluating IMU-based estimates of these quantities by referencing optical systems is limited by the fact that these systems only measure positions, not kinetic and energetic quantities. To evaluate the accuracy of an IMU-based method for estimating kinetic and energetic quantities without using any external reference, firstly, we propose an estimation method only using angular velocity and acceleration signals supplied by an IMU, and apply this to a single rigid body with known mass and inertia. Then, we propose a novel experimental validation method against physical conservation and action/reaction laws that apply during ballistic movements, using a suitably designed and reconfigurable rigid body with a structure of three orthogonal dumb-bells. The results demonstrated that we could estimate the angular momentum, kinetics, and energetics of a rigid body by only using angular velocity and acceleration signals of an IMU, and the estimation accuracy was well evaluated by the proposed validation method. However, the results showed that the errors in original IMU measurements under dynamic conditions especially concerning angular velocity, uncertainties in calculating rigid body parameters, and vibration propagation due to limited rigidity of tubes of the rigid body influenced the estimation accuracy.

People fall more often when their gait stability is reduced. Gait stability can be directly manipulated by exerting forces or moments onto a person, ranging from simple walking sticks to complex wearable robotics. A systematic review of the literature was performed to determine: What is the level of evidence for different types of mechanical manipulations on improving gait stability? The study was registered at PROSPERO (CRD42020180631). Databases Embase, Medline All, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar were searched. The final search was conducted on the 1st of December, 2022. The included studies contained mechanical devices that influence gait stability for both impaired and non-impaired subjects. Studies performed with prosthetic devices, passive orthoses, and analysing post-training effects were excluded. An adapted NIH quality assessment tool was used to assess the study quality and risk of bias. Studies were grouped based on the type of device, point of application, and direction of forces and moments. For each device type, a best-evidence synthesis was performed to quantify the level of evidence based on the type of validity of the reported outcome measures and the study quality assessment score. Impaired and non-impaired study participants were considered separately. From a total of 4701 papers, 53 were included in our analysis. For impaired subjects, indicative evidence was found for medio-lateral pelvis stabilisation for improving gait stability, while limited evidence was found for hip joint assistance and canes. For non-impaired subjects, moderate evidence was found for medio-lateral pelvis stabilisation and limited evidence for body weight support. For all other device types, either indicative or insufficient evidence was found for improving gait stability. Our findings also highlight the lack of consensus on outcome measures amongst studies of devices focused on manipulating gait.

Background: Pneumatic actuators are widely used in applications like (medical) robots, or prostheses. Pneumatic actuators require a complex manufacturing process and are produced in standardized dimensions to reduce costs. Over the last decade 3D-printing has emerged as a cost-effective and efficient production method in medical applications. 3D-printing can also function as a cost-efficient alternative production method for pneumatic actuators. Objective: The goal of this research is to study the possibility of creating a pneumatic linear actuator with 3D-printing. Furthermore, the aim is to use the advantage of 3D-printing to create pneumatic actuators with non-circular cross-sections. Methodology: To evaluate the performance of a 3D-printed pneumatic actuator, a test setup was designed and built to measure the leakage and sliding friction force. Furthermore, two pneumatic actuators with a non-conventional cross-sectional shape were designed and their performance was tested and compared with a 3D-printed cylindrical pneumatic actuator, since these tests only ran once, the results are more a guideline. During the manufacturing of the cylinders, no post-processing techniques were used. Results: The functioning of a 3D-printed circular pneumatic actuator was proven with low static leakage rates of 2.5%, low dynamic leakage rates of approximately 1%, and a maximum friction force of [Formula presented]. Furthermore, the results show that it is possible to print functioning pneumatic cylinders with a non-cylindrical concave cross-section. The non-conventional cylinders were tested up to [Formula presented] with maximum dynamic leakage of [Formula presented]. Conclusion: This study demonstrates a method to create functional pneumatic linear actuators with 3D-printing. It was possible to create 3D-printed actuators with a conventional shape, e.g. circular and unconventional shapes e.g. stadium/oval shape and a kidney shape. The leak rates for conventional and unconventional shapes were in the same range. This opens up the world for more design freedom in pneumatic actuators.

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This article introduces gnssFGO, a global and online trajectory estimator that fuses global navigation satellite systems (GNSS) observations alongside multiple sensor measurements for robust vehicle localization. In gnssFGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process (GP) regression. This enables querying states at arbitrary timestamps without strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multisensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, inertial measurement unit, and LiDAR-odometry. We employed datasets from measurement campaigns in Aachen, Düsseldorf, and Cologne and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas where a classic multisensor fusion method fails due to sensor degradation. In a test sequence containing a 17-km route through Aachen, the proposed method results in a mean 2-D positioning error 0.48 m while fusing raw GNSS observations with LiDAR odometry in a tight coupling.

Balance recovery after tripping often requires an active adaptation of foot placement. Thus far, few attempts have been made to actively assist forward foot placement for balance recovery employing wearable devices. This study aims to explore the possibilities of active forward foot placement through two paradigms of actuation: assistive moments exerted with the reaction moments either internal or external to the human body, namely 'joint' moments and 'free' moments, respectively. Both paradigms can be applied to manipulate the motion of segments of the body (e.g., the shank or thigh), but joint actuators also exert opposing reaction moments on neighbouring body segments, altering posture and potentially inhibiting tripping recovery. We therefore hypothesised that a free moment paradigm is more effective in assisting balance recovery following tripping. The simulation software SCONE was used to simulate gait and tripping over various ground-fixed obstacles during the early swing phase. To aid forward foot placement, joint moments and free moments were applied either on the thigh to augment hip flexion or on the shank to augment knee extension. Two realizations of joint moments on the hip were simulated, with the reaction moment applied to either the pelvis or the contralateral thigh. The simulation results show that assisting hip flexion with either actuation paradigm on the thigh can result in full recovery of gait with a margin of stability and leg kinematics closely matching the unperturbed case. However, when assisting knee extension with moments on the shank, free moment effectively assist balance but joint moments with the reaction moment on the thigh do not. For joint moments assisting hip flexion, placement of the reaction moment on the contralateral thigh was more effective in achieving the desired limb dynamics than placing the reaction on the pelvis. Poor choice of placement of reaction moments may therefore have detrimental consequences for balance recovery, and removing them entirely (i.e., free moment) could be a more effective and reliable alternative. These results challenge conventional assumptions and may inform the design and development of a new generation of minimalistic wearable devices to promote balance during gait.

Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.

The global navigation satellite systems (GNSS) play a vital role in transport systems for accurate and consistent vehicle localization. However, GNSS observations can be distorted due to multipath effects and non-line-of-sight (NLOS) receptions in challenging environments such as urban canyons. In such cases, traditional methods to classify and exclude faulty GNSS observations may fail, leading to unreliable state estimation and unsafe system operations. This work proposes a deep-learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem. Compared to previous works, we construct a transformer-like attention mechanism to enhance the long short-term memory (LSTM) networks, improving model performance and generalization. For the training and evaluation of the proposed network, we used labeled datasets from the cities of Hong Kong and Aachen. We also introduce a dataset generation process to label the GNSS observations using lidar maps. In experimental studies, we compare the proposed network with a deep-learning-based model and classical machine-learning models. Furthermore, we conduct ablation studies of our network components and integrate the NLOS detection with data out-of-distribution in a state estimator. As a result, our network presents improved precision and recall ratios compared to other models. Additionally, we show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.