People with physical impairments often face difficulties in performing daily tasks such as dressing, leading to dependence on caregivers. While robotic manipulators can provide valuable assistance, close physical interaction raises concerns over safety, comfort and trust, which can limit adoption. This article introduces the Assistive Robot Twin (ART) framework, a real-time digital twin that models both the user and the robot to enhance transparency during robot-assisted dressing. ART integrates two visual safety features: Bounding Boxes (BBs), which define static or dynamic protective zones around critical regions, and Trajectory Visualisation (TV), which displays planned robot movements in real time. We conducted a within-subject study with 36 participants mimicking stroke-related mobility impairment, evaluating six BB/TV configurations using validated interaction quality and system usability questionnaires. The results show that BBs significantly improved perceived safety ( \(\textrm{p} < 0.001\) ), reduced discomfort ( \(\textrm{p} < 0.001\) ) and increased trust ( \(\textrm{p} < 0.001\) ), with dynamic BBs providing the greatest safety benefits. TV significantly enhanced overall system usability ( \(\textrm{p}=0.007\) ), confidence and predictability of robot actions. While the study focuses on perceived interaction quality in a controlled setting with healthy participants, the results provide foundational evidence for the design of transparent assistive systems prior to clinical deployment.

This paper proposes a deep learning approach for contactless detection of sleep apnoea using pulse and blood oxygen saturation (SPO2) data. Three convolutional neural network architectures are adopted for apnoea classification purposes by fusing different features of the available time series signals. A conventional convolutional neural network (CNN), a CNN with a support vector machine (CNN-SVM), and a CNN combined with a recurrent neural network (CNN-RNN) are compared. The RNN includes Gated Recurrent Units (GRU) and Bidirectional GRU (BiGRU). The CNN is utilised to extract features, whilst the SVM and RNN are used for classification. In addition, we compare two different fusion methods, signal-level and feature-level fusion. The performance is validated and evaluated on a public dataset obtain from St. Vincent University Hospital. The results show that the concatenation of SPO2 and pulse signal at the signal level enhances the classification performance compared to using the individual signal. In addition, the classification sensitivity with signal-level fusion is higher than that with feature-level fusion. Overall, the proposed CNN-RNN with GRU (CNN-GRU) architecture gives the best performance with an accuracy of 85.4%, a sensitivity of 61.5%, a specificity of 91.9%, an$F_{1}$score of 0.64, and a$\kappa$score of 0.551 with a dropout rate of 0.5 and a 20-second overlap. The results demonstrate that the proposed deep learning approach offers a promising solution for non-invasive detection of sleep apnoea using affordable physiological signals.

This paper presents the use of a simulation environment as an accurate, ethical, and sustainable alternative to testing robotic prototypes in animal models and simplified phantom models, specifically developed for robotic colonoscopy devices inside the human colon. A virtual simulation of the locomotion mechanism of a prototype robotic colonoscope and the colon was created in Ansys, and robot/colon experiments were conducted on different colon surfaces to validate simulation results. The successfully simulated propulsion force generated by the prototype produced an RMSE of 7% when compared at the optimal operating condition of the device, and 25-30% when compared to a full range of device velocities. The larger RMSE is due to physical phenomena that were not present in the simulation due to the constraints applied. The simulation, however, allowed evaluation of difficult quantities to measure in a real world setting such as the normal interaction force between the device and tissue wall, and stress distribution across the locomotion mechanism, as well as a phenomenon of oscillating propulsion force resulting from the device design. This work demonstrates feasibility of using finite element simulation to shape the design and optimization of a robotic colonoscope and understands its interaction with complex human anatomy.

The assessment of implant status and complications of Total Hip Replacement (THR) relies mainly on the clinical evaluation of the X-ray images to analyse the implant and the surrounding rigid structures. Current clinical practise depends on the manual identification of important landmarks to define the implant boundary and to analyse many features in arthroplasty X-ray images, which is time-consuming and could be prone to human error. Semantic segmentation based on the Convolutional Neural Network (CNN) has demonstrated successful results in many medical segmentation tasks. However, these networks cannot define explicit properties that lead to inaccurate segmentation, especially with the limited size of image datasets. Our work integrates clinical knowledge with CNN to segment the implant and detect important features simultaneously. This is instrumental in the diagnosis of complications of arthroplasty, particularly for loose implant and implant-closed bone fractures, where the location of the fracture in relation to the implant must be accurately determined. In this work, we define the points of interest using Gruen zones that represent the interface of the implant with the surrounding bone to build a Statistical Shape Model (SSM). We propose a multitask CNN that combines regression of pose and shape parameters constructed from the SSM and semantic segmentation of the implant. This integrated approach has improved the estimation of implant shape, from 74% to 80% dice score, making segmentation realistic and allowing automatic detection of Gruen zones. To train and evaluate our method, we generated a dataset of annotated hip arthroplasty X-ray images that will be made available.

This research investigates how professional carers physically assist frail older adults. Carers were asked to discuss their approach and steps for providing safe physical assistance and highlight hazards that assistive robots would have to deal with in such situations. The aspects raised by carers indicate that irrespective of the degree of vulnerability of the older adults, carers can evaluate trust and the older adults' ability and willingness to collaborate during the assistive task through multiple modalities. These include tactile, visual and verbal cues, which the carers use to discern a measure of collaboration and adapt their assistance accordingly. Understanding these hazards and their effective collaboration is a vital step towards developing safe, physically assistive robots.

The integration of minimally invasive robotic assistance and image-guidance can have positive impact on joint fracture surgery, providing a better clinical outcome with respect to the current open procedure. In this paper, a new design of the RAFS surgical system is presented. The redesign of the robotic system and its integration with a novel 3D navigation system through a new clinical workflow, overcomes the drawbacks of the earlier prototype. This makes the RAFS surgical system more suitable to clinical scenarios in the operating theatre. System accuracy and effectiveness are successfully demonstrated through laboratory trials and preliminary cadaveric trials. The experimental results demonstrate that the system allows the surgeon to reduce a 2-fragment distal femur fracture in a cadaveric specimen, with a reduction accuracy of up to 0.85 mm and 2.2°. Preliminary cadaveric trials also provided a positive and favorable outcome pointing to the usability and safety of the RAFS system in the operating theatre, potentially enhancing the capacity of joint fracture surgeries.

Focusing on upper limb rehabilitation of weak stroke patients, this pilot study explores how motion intent can be detected using force sensitive resistors (FSR). This is part of a bigger project which will see the actuation and control of an intent-driven exoskeleton. The limited time stroke survivors have with their therapists means that they can not often get enough training. During active-assisted training, therapists guide the paralysed limb through a movement only after detecting visual or haptic cues of the motion intent from the patient. Aiming to replicate therapist practices of recognising patients' intention to move, a pilot study of a tactile system is performed. The system will perform consistently even with patients who have low muscle strength and control ability. Currently available devices for detecting muscle activity do not offer the robustness and performance necessary; Electromyography (EMG) sensors, a well-established method, is affected by factors like skin moisture and BCI (Brain Computer Interface) has a slow response time. The proposed tactile sensing system is a simple yet robust solution both from a sensing as well as a usability point of view. Pilot experiments have been performed with a healthy subject emulating low muscle activation conditions. An overall accuracy of 80.45% is achieved when detecting forearm and arm muscle contractions and hence motion intent.

Robots that can assist in activities of daily living (ADL) such as dressing assistance, need to be capable of intuitive and safe interaction. Vision systems are often used to provide information on the position and movement of the robot and user. However, in a dressing context, technical complexity, occlusion and concerns over user privacy pushes research to investigate other approaches for human-robot interaction (HRI). We analysed verbal, proprioceptive and force feedback from 18 participants during a human-human dressing experiment where users received dressing assistance from a researcher mimicking robot behaviour. This paper investigates the occurrence of deictic speech in an assisted-dressing task and how any ambiguity could be resolved to ensure safe and reliable HRI. We focus on one of the most frequently occurring deictic words “up”, which was captured over 300 times during the experiments and is used as an example of an ambiguous command. We attempt to resolve the ambiguity of these commands through predictive models. These models were used to predict end effector choice and the direction in which the garment should move. The model for predicting end effector choice resulted in 70.4% accuracy based on the user's head orientation. For predicting garment direction, the model used the angle of the user's arm and resulted in 87.8% accuracy. We also found that additional categories such as the starting position of the user's arms and end-effector height may improve the accuracy of a predictive model. We present suggestions on how these inputs may be attained through non-visual means, for example through haptic perception of end-effector position, proximity sensors and acoustic source localisation.

Robotic assistance can bring significant improvements to orthopedic fracture surgery: facilitate more accurate fracture fragment repositioning without open access and obviate problems related to the current minimally invasive fracture surgery techniques by providing a better clinical outcome, reduced recovery time, and health-related costs. This paper presents a new design of the robot-assisted fracture surgery (RAFS) system developed at Bristol Robotics Laboratory, featuring a new robotic architecture, and real-time 3D imaging of the fractured anatomy. The technology presented in this paper focuses on distal femur fractures, but can be adapted to the larger domain of fracture surgeries, improving the state-of-the-art in robot assistance in orthopedics. To demonstrate the enhanced performance of the RAFS system, 10 reductions of a distal femur fracture are performed using the system on a bone model. The experimental results clearly demonstrate the accuracy, effectiveness, and safety of the new RAFS system. The system allows the surgeon to precisely reduce the fractures with a reduction accuracy of 1.15 mm and 1.3°, meeting the clinical requirements for this procedure.

Discovering older adults' perceptions and expectations of domestic care service robots are vital in informing the design and development of new technologies to ensure acceptability and usability. This paper identifies issues that were elicited from older adults using different methods to promote creative thinking about domestic robots at an emotional level, as well as pragmatic level. These included exploring people's ideal embodiment preferences and requirements for a domestic care service robot, and also what embodiments and functional aspects will not be acceptable. We analysed our findings using relevant constructs from the Unified Theory of Acceptance and Use of Technology, and Technology Acceptance models. In addition to some already well-established findings, we discovered some surprising aspects concerning interaction, behaviour and appearance and the ability for the robot to fit the relevant context, both physically and conceptually.

Robot-assisted orthopaedic surgery has proved to provide better outcomes over conventional surgery in terms of improved spatial accuracy and more reliable and repeatable results. We present a novel use of robots in orthopaedic surgery - precise anatomic reduction of joints' fractures. The joints of the body are frequently involved in bone breaks, typically classified as intra-articular fractures. If a joint is to function properly again, that is to provide pain-free stability and movement, the broken pieces of the joint must be subjected to an anatomic reduction - being put back together as perfectly as possible. Having minimally invasive, closed anatomic reduction as a starting point, our method involves a rigorous surgical workflow analysis to create a general framework for robotic reduction techniques. Small and lightweight spatial parallel manipulators are utilized in this pilot study to move fragments through pre-calculated trajectories. In this paper, our initial analysis of clinical and functional requirements of a robot-assisted joint surgery is presented, followed by description of a developed physical robotic system for minimally invasive joint surgery. Experimental results show that a simple graphical user interface is sufficient to obtain the transformation parameters to position fragments in space. The fragment manipulating devices can position and align fragments with an accuracy of ±1mm and ±5degress.

Rehabilitation Robotics involves the use of robotic systems as an enabling technology for people with kinetic problems, in order to help them recover from a physical trauma. This paper presents the investigation of a robotic system for stroke and post hand-surgery patient rehabilitation, in order to gradually regain flexibility in their finger-joints by passively extending and flexing their fingers. It includes one linear actuator for each finger and a thin-film force sensor at each fingertip as a safety measure against overstraining the finger-joints. Prior to designing the system, kinematic and dynamic models of a human hand have been derived and simulated in MATLAB. Data obtained from this model show a strong correlation to natural human hand movements, recorded in this study using a 6 DoF motion capture system. Design of the robotic system is performed using UGS NX6 software.