The Impact of Design Factors on User Behavior in a Virtual Hospital Room to Explore Fall Prevention Strategies.

OBJECTIVES: Falls in hospitals pose a significant safety risk, leading to injuries, prolonged hospitalization, and lasting complications. This study explores the potential of augmented reality (AR) technology in healthcare facility design to mitigate fall risk. BACKGROUND: Few studies have investigated the impact of hospital room layouts on falls due to the high cost of building physical prototypes. This study introduces an innovative approach using AR technology to advance methods for healthcare facility design efficiently. METHODS: Ten healthy participants enrolled in this study to examine different hospital room designs in AR. Factors of interest included room configuration, door type, exit side of the bed, toilet placement, and the presence of IV equipment. AR trackers captured trajectories of the body as participants navigated through these AR hospital layouts, providing insights into user behavior and preferences. RESULTS: Door type influenced the degree of backward and sideways movement, with the presence of an IV pole intensifying the interaction between door and room type, leading to increased sideways and backward motion. Participants displayed varying patterns of backward and sideways travel depending on the specific room configurations they encountered. CONCLUSIONS: AR can be an efficient and cost-effective method to modify room configurations to identify important design factors before conducting physical testing. The results of this study provide valuable insights into the effect of environmental factors on movement patterns in simulated hospital rooms. These results highlight the importance of considering environmental factors, such as the type of door and bathroom location, when designing healthcare facilities.

Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy.

CONTEXT: As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal? OBJECTIVE: To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control. EVIDENCE ACQUISITION: We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling. EVIDENCE SYNTHESIS: We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes. CONCLUSIONS: Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone. PATIENT SUMMARY: We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.

Skin Stretch Haptic Feedback to Convey Closure Information in Anthropomorphic, Under-Actuated Upper Limb Soft Prostheses.

Restoring hand function in individuals with upper limb loss is a challenging task, made difficult by the complexity of human hands from both a functional and sensory point of view. Users of commercial prostheses, even sophisticated devices, must visually attend to the hand to know its state, since in most cases they are not provided with any direct sensory information. Among the different types of haptic feedback that can be delivered, particularly information on hand opening is likely to reduce the requirement of constant visual attention. In recent years, there has been a trend of using underactuated, compliant multi-fingered hands as upper limb prostheses, in part due to their simplicity and ease of use attributed to low degree-of-freedom (d.o.f.) actuation. The trend toward underactuation encourages the design of one d.o.f. haptic devices to provide intuitive sensory feedback from the prosthesis. However, mapping the closure of a multi-d.o.f. prosthetic hand to a simple and intuitive haptic cue is not a trivial task. In this paper, we explore the use of a one d.o.f. skin stretch haptic device, the rice haptic rocker, to provide intuitive proprioceptive feedback indicating overall hand closure of an underactuated prosthesis. The benefits and challenges of the system are assessed in multi-tasking and reduced vision scenarios for an object-size discrimination task, in an effort to simulate challenges in daily life, and are compared against the haptic resolution of the device using the just noticeable difference. Finally, an evaluation done with a prosthesis user, in the form of a truncated version of the Activities Measure for Upper Limb Amputees (AM-ULA), shows possible benefits of the addition of haptic feedback in tasks with reduced visual attention.

HapPro: A Wearable Haptic Device for Proprioceptive Feedback.

OBJECTIVE: Myoelectric hand prostheses have reached a considerable technological level and gained an increasing attention in assistive robotics. However, their abandonment rate remains high, with unintuitive control and lack of sensory feedback being major causes. Among the different types of sensory information, proprioception, e.g., information on hand aperture, is crucial to successfully perform everyday actions. Despite the many attempts in literature to restore and convey this type of feedback, much remains to be done to close the action-perception loop in prosthetic devices. METHODS: With this as motivation, in this paper we introduce HapPro, a wearable, noninvasive haptic device that can convey proprioceptive information for a prosthetic hand. The device was used with an under-actuated, simple to control anthropomorphic robotic hand, providing information about hand aperture by mapping it to the position of a wheel that can run on the user's forearm. Tests with 43 able bodied subjects and one amputee subject were conducted in order to quantify the effectiveness of HapPro as a feedback device. RESULTS: HapPro provided a good level of accuracy for item discrimination. Participants also reported the device to be intuitive and effective in conveying proprioceptive cues. Similar results were obtained in the proof-of-concept experiment with an amputee subject. CONCLUSIONS: Results show that HapPro is able to convey information on the opening of a prosthetic hand in a noninvasive way. SIGNIFICANCE: Using this device for proprioceptive feedback could improve usability of myoelectric prostheses, potentially reducing abandonment and increasing quality of life for their users.

W-FYD: A Wearable Fabric-Based Display for Haptic Multi-Cue Delivery and Tactile Augmented Reality.

Despite the importance of softness, there is no evidence of wearable haptic systems able to deliver controllable softness cues. Here, we present the Wearable Fabric Yielding Display (W-FYD), a fabric-based display for multi-cue delivery that can be worn on a user's finger and enables, for the first time, both active and passive softness exploration. It can also induce a sliding effect under the finger-pad. A given stiffness profile can be obtained by modulating the stretching state of the fabric through two motors. Furthermore, a lifting mechanism allows to put the fabric in contact with the user's finger-pad, to enable passive softness rendering. In this paper, we describe the architecture of W-FYD, and a thorough characterization of its stiffness workspace, frequency response, and softness rendering capabilities. We also computed device Just Noticeable Difference in both active and passive exploratory conditions, for linear and non-linear stiffness rendering as well as for sliding direction perception. The effect of device weight was also considered. Furthermore, performance of participants and their subjective quantitative evaluation in detecting sliding direction and softness discrimination tasks are reported. Finally, applications of W-FYD in tactile augmented reality for open palpation are discussed, opening interesting perspectives in many fields of human-machine interaction.

Postural Hand Synergies during Environmental Constraint Exploitation.

Humans are able to intuitively exploit the shape of an object and environmental constraints to achieve stable grasps and perform dexterous manipulations. In doing that, a vast range of kinematic strategies can be observed. However, in this work we formulate the hypothesis that such ability can be described in terms of a synergistic behavior in the generation of hand postures, i.e., using a reduced set of commonly used kinematic patterns. This is in analogy with previous studies showing the presence of such behavior in different tasks, such as grasping. We investigated this hypothesis in experiments performed by six subjects, who were asked to grasp objects from a flat surface. We quantitatively characterized hand posture behavior from a kinematic perspective, i.e., the hand joint angles, in both pre-shaping and during the interaction with the environment. To determine the role of tactile feedback, we repeated the same experiments but with subjects wearing a rigid shell on the fingertips to reduce cutaneous afferent inputs. Results show the persistence of at least two postural synergies in all the considered experimental conditions and phases. Tactile impairment does not alter significantly the first two synergies, and contact with the environment generates a change only for higher order Principal Components. A good match also arises between the first synergy found in our analysis and the first synergy of grasping as quantified by previous work. The present study is motivated by the interest of learning from the human example, extracting lessons that can be applied in robot design and control. Thus, we conclude with a discussion on implications for robotics of our findings.

An Integrated Approach to Characterize the Behavior of a Human Fingertip in Contact with a Silica Window.

Understanding the mechanisms of human tactual perception represents a challenging task in haptics and humanoid robotics. A classic approach to tackle this issue is to accurately and exhaustively characterize the mechanical behavior of human fingertip. The output of this characterization can then be exploited to drive the design of numerical models, which can be used to investigate in depth the mechanisms of human sensing. In this work, we present a novel integrated measurement technique and experimental set up for in vivo characterization of the deformation of the human fingertip at contact, in terms of contact area, force, deformation, and pressure distribution. The device presented here compresses the participant's fingertip against a flat surface, while the aforementioned measurements are acquired and experimental parameters such as velocity, finger orientation, and displacement (indentation) controlled. Experimental outcomes are then compared and integrated with the output of a 3D finite element (FE) model of the human fingertip, built upon existing validated models. The agreement between numerical and experimental data represents a validation for our approach.

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition.

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.

ThimbleSense: A Fingertip-Wearable Tactile Sensor for Grasp Analysis.

Accurate measurement of contact forces between hand and grasped objects is crucial to study sensorimotor control during grasp and manipulation. In this work, we introduce ThimbleSense, a prototype of individual-digit wearable force/torque sensor based on the principle of intrinsic tactile sensing. By exploiting the integration of this approach with an active marker-based motion capture system, the proposed device simultaneously measures absolute position and orientation of the fingertip, which in turn yields measurements of contacts and force components expressed in a global reference frame. The main advantage of this approach with respect to more conventional solutions is its versatility. Specifically, ThimbleSense can be used to study grasping and manipulation of a wide variety of objects, while still retaining complete force/torque measurements. Nevertheless, validation of the proposed device is a necessary step before it can be used for experimental purposes. In this work, we present the results of a series of experiments designed to validate the accuracy of ThimbleSense measurements and evaluate the effects of distortion of tactile afferent inputs caused by the device's rigid shells on grasp forces.

A Finite element model of tactile flow for softness perception.

Touch is an extremely dynamic sense. To take into account this aspect, it has been hypothesized that there are mechanisms in the brain that specialize in processing dynamic tactile stimuli, in a way not too dissimilar from what happens for optical flow in dynamic vision. The concept of tactile flow, related to the rate of expansion of isostrain volumes in the human fingerpad, was used to explain some perceptual illusions as well as mechanisms of human softness perception. In this paper we describe a computational model of tactile flow, and apply it to a finite element model of interaction between deformable bodies. The shape and material properties of the bodies are modeled from those of a human fingertip interacting with specimens with different softness properties. Results show that the rate of expansion of isostrain volumes can be used to discriminate different materials in terms of their softness characteristics.