The Social Robot in Rehabilitation and Assistance: What Is the Future?

Do you wanna dance? Tales of trust and driving trust factors in robot medication counseling in the pharmacy context

Introduction: The sustainable implementation of socially assistive robots in a pharmacy setting requires that customers trust the robot. Our aim was to explore young adults' anticipations of and motives for trusting robot medication counseling in a high-stakes scenario. Methods: Through a co-creation approach, we co-designed a prototype application for the Furhat platform together with young adults. In-lab testing of a pharmacy scenario, where the robot provides medication counseling related to emergency contraceptive pills, was conducted to deepen our understanding of some factors driving young adults' initial trust establishment and anticipations of interacting with a robot in a high-stakes scenario. Qualitative data from interviews with six study participants were analyzed using inductive, reflexive thematic analysis and are presented through a narrative approach. Results: We outline five tales of trust characterized by personas. A continuum of different anticipations for consulting a robot in medication counseling is presented, ranging from low to high expectations of use. Driving factors in the initial trust establishment process are position, autonomy, boundaries, shame, gaze, and alignment. Discussion: The article adds to the understanding of the dimensions of the multifaceted trust concept, of driving trust factors, and of the subsequent anticipation to trust robots in a high-stakes pharmacy context.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

A Robot-Assisted Framework for Rehabilitation Practices: Implementation and Experimental Results

One of the most interesting characteristics of collaborative robots is their ability to be used in close cooperation scenarios. In industry, this facilitates the implementation of human-in-loop workflows. However, this feature can also be exploited in different fields, such as healthcare. In this paper, a rehabilitation framework for the upper limbs of neurological patients is presented, consisting of a collaborative robot that helps users perform three-dimensional trajectories. Such a practice is aimed at improving the coordination of patients by guiding their motions in a preferred direction. We present the mechatronic setup, along with a preliminary experimental set of results from 19 volunteers (patients and control subjects) who provided positive feedback on the training experience (52% of the subjects would return and 44% enjoyed performing the exercise). Patients were able to execute the exercise, with a maximum deviation from the trajectory of 16 mm. The muscular effort required was limited, with average maximum forces recorded at around 50 N.

Synergizing Intelligence and Building a Smarter Future: Artificial Intelligence Meets Bioengineering

Smart Engineering (SE) describes the methods, processes, and IT tools for the interdisciplinary, system-oriented development of innovative, intelligent, networked products, production plants, and infrastructures [...].

Effect of different modalities of artificial intelligence rehabilitation techniques on patients with upper limb dysfunction after stroke-A network meta-analysis of randomized controlled trials

Background

This study aimed to observe the effects of six different types of AI rehabilitation techniques (RR, IR, RT, RT + VR, VR and BCI) on upper limb shoulder-elbow and wrist motor function, overall upper limb function (grip, grasp, pinch and gross motor) and daily living ability in subjects with stroke. Direct and indirect comparisons were drawn to conclude which AI rehabilitation techniques were most effective in improving the above functions.

Methods

From establishment to 5 September 2022, we systematically searched PubMed, EMBASE, the Cochrane Library, Web of Science, CNKI, VIP and Wanfang. Only randomized controlled trials (RCTs) that met the inclusion criteria were included. The risk of bias in studies was evaluated using the Cochrane Collaborative Risk of Bias Assessment Tool. A cumulative ranking analysis by SUCRA was performed to compare the effectiveness of different AI rehabilitation techniques for patients with stroke and upper limb dysfunction.

Results

We included 101 publications involving 4,702 subjects. According to the results of the SUCRA curves, RT + VR (SUCRA = 84.8%, 74.1%, 99.6%) was most effective in improving FMA-UE-Distal, FMA-UE-Proximal and ARAT function for subjects with upper limb dysfunction and stroke, respectively. IR (SUCRA = 70.5%) ranked highest in improving FMA-UE-Total with upper limb motor function amongst subjects with stroke. The BCI (SUCRA = 73.6%) also had the most significant advantage in improving their MBI daily living ability.

Conclusions

The network meta-analysis (NMA) results and SUCRA rankings suggest RT + VR appears to have a greater advantage compared with other interventions in improving upper limb motor function amongst subjects with stroke in FMA-UE-Proximal and FMA-UE-Distal and ARAT. Similarly, IR had shown the most significant advantage over other interventions in improving the FMA-UE-Total upper limb motor function score of subjects with stroke. The BCI also had the most significant advantage in improving their MBI daily living ability. Future studies should consider and report on key patient characteristics, such as stroke severity, degree of upper limb impairment, and treatment intensity/frequency and duration.

Systematic review registration

www.crd.york.ac.uk/prospero/#recordDetail, identifier: CRD42022337776.

More than surgical tools: a systematic review of robots as didactic tools for the education of professionals in health sciences

Within the field of robots in medical education, most of the work done during the last years has focused on surgeon training in robotic surgery, practicing surgery procedures through simulators. Apart from surgical education, robots have also been widely employed in assistive and rehabilitation procedures, where education has traditionally focused in the patient. Therefore, there has been extensive review bibliography in the field of medical robotics focused on surgical and rehabilitation and assistive robots, but there is a lack of survey papers that explore the potential of robotics in the education of healthcare students and professionals beyond their training in the use of the robotic system. The scope of the current review are works in which robots are used as didactic tools for the education of professionals in health sciences, investigating the enablers and barriers that affect the use of robots as learning facilitators. Systematic literature searches were conducted in WOS and Scopus, yielding a total of 3812 candidate papers. After removing duplicates, inclusion criteria were defined and applied, resulting in 171 papers. An in-depth quality assessment was then performed leading to 26 papers for qualitative synthesis. Results show that robots in health sciences education are still developed with a roboticist mindset, without clearly incorporating aspects of the teaching/learning process. However, they have proven potential to be used in health sciences as they allow to parameterize procedures, autonomously guide learners to achieve greater engagement, or enable collective learning including patients and instructors "in the loop". Although there exist documented added-value benefits, further research and efforts needs to be done to foster the inclusion of robots as didactic tools in the curricula of health sciences professionals. On the one hand, by analyzing how robotic technology should be developed to become more flexible and usable to support both teaching and learning processes in health sciences education, as final users are not necessarily well-versed in how to use it. On the other, there continues to be a need to develop effective and standard robotic enhanced learning evaluation tools, as well good quality studies that describe effective evaluation of robotic enhanced education for professionals in health sciences. As happens with other technologies when applied to the health sciences field, studies often fail to provide sufficient detail to support transferability or direct future robotic health care education programs.

AI-Based Soft Module for Safe Human-Robot Interaction towards 4D Printing

Soft robotic modules have potential use for therapeutic and educational purposes. To do so, they need to be safe, soft, smart, and customizable to serve individuals' different preferences and personalities. A safe modular robotic product made of soft materials, particularly silicon, programmed by artificial intelligence algorithms and developed via additive manufacturing would be promising. This study focuses on the safe tactile interaction between humans and robots by means of soft material characteristics for translating physical communication to auditory. The embedded vibratory sensors used to stimulate touch senses transmitted through soft materials are presented. The soft module was developed and verified successfully to react to three different patterns of human-robot contact, particularly users' touches, and then communicate the type of contact with sound. The study develops and verifies a model that can classify different tactile gestures via machine learning algorithms for safe human-robot physical interaction. The system accurately recognizes the gestures and shapes of three-dimensional (3D) printed soft modules. The gestures used for the experiment are the three most common, including slapping, squeezing, and tickling. The model builds on the concept of how safe human-robot physical interactions could help with cognitive and behavioral communication. In this context, the ability to measure, classify, and reflect the behavior of soft materials in robotic modules represents a prerequisite for endowing robotic materials in additive manufacturing for safe interaction with humans.

Interaction Matters: The Effect of Touching the Social Robot PARO on Pain and Stress is Stronger When Turned ON vs. OFF

Social touch between humans, as well as between humans and animals, was previously found to reduce pain and stress. We previously reported that touching a social robot can also induce a reduction in pain ratings. However, it is unclear if the effect that touching a robot has on pain perception is due to its appearance and its pleasant touch, or due to its ability to socially interact with humans. In the current experiment, we aimed to assess the contribution of the interactive quality to pain perception. We assessed the effect of touching the social robot PARO on mild and strong pain ratings and on stress perception, on a total of 60 healthy young participants. The robot either interacted with participants (ON group, n = 30) or was turned off (OFF group, n = 30). Touching the robot induced a decrease in mild pain ratings (compared to baseline) only in the ON group while strong pain ratings decreased similarly in both the ON and the OFF groups. The decrease in mild pain ratings in the ON group was significantly greater in participants with a higher positive perception of the interaction with PARO. We conclude that part of the effect that touching the robot has on pain stems from its interactive features.

Manipulability Optimization of a Rehabilitative Collaborative Robotic System

The use of collaborative robots (or cobots) in rehabilitation therapies is aimed at assisting and shortening the patient’s recovery after neurological injuries. Cobots are inherently safe when interacting with humans and can be programmed in different working modalities based on the patient’s needs and the level of the injury. This study presents a design optimization of a robotic system for upper limb rehabilitation based on the manipulability ellipsoid method. The human–robot system is modeled as a closed kinematic chain in which the human hand grasps a handle attached to the robot’s end effector. The manipulability ellipsoids are determined for both the human and the robotic arm and compared by calculating an index that quantifies the alignment of the principal axes. The optimal position of the robot base with respect to the patient is identified by a first global optimization and by a further local refinement, seeking the best alignment of the manipulability ellipsoids in a series of points uniformly distributed within the shared workspace.

The Social Robot and the Digital Physiotherapist: Are We Ready for the Team Play?

Motivation: We are witnessing two phenomena. The first is that the physiotherapist is increasingly becoming a figure that must interact with Digital Health. On the other hand, social robots through research are improving more and more in the aspects of social interaction thanks also to artificial intelligence and becoming useful in rehabilitation processes. It begins to become strategic to investigate the intersections between these two phenomena. Objective: Therefore, we set ourselves the goal of investigating the consensus and opinion of physiotherapists around the introduction of social robots in clinical practice both in rehabilitation and assistance. Procedure: An electronic survey has been developed focused on social robot-based rehabilitation and assistance and has been submitted to subjects focused on physiotherapy sciences to investigate their opinion and their level of consent regarding the use of the social robot in rehabilitation and assistance. Two samples of subjects were recruited: the first group (156 participating subjects, 79 males, 77 females, mean age 24.3 years) was in the training phase, and the second (167 participating subjects, 86 males, 81 females, mean age 42.4 years) group was involved in the work processes. An electronic feedback form was also submitted to investigate the acceptance of the proposed methodology. Results: The survey showed a consistency of the results between the two samples from which interesting considerations emerge. Contrary to stereotypes that report how AI-based devices put jobs at risk, physiotherapists are not afraid of these devices. The subjects involved in the study believe the following: (a) social robots can be reliable co-workers but will remain a complementary device; (b) their role will be of the utmost importance as an operational manager in their use and in performance monitoring; (c) these devices will allow an increase in working capacity and facilitate integration. All those involved in the study believe that the proposed electronic survey has proved to be a useful and effective tool that can be useful as a periodic monitoring tool and useful for scientific societies. Conclusions: The evolution of social robots represents an unstoppable process as does the increase in the aging of the population. Stakeholders must not look with suspicion toward these devices, which can represent an important resource, but rather invest in monitoring and consensus training initiatives.

Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People

This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the message queue telemetry transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) system developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise; fear; happiness; sadness; disgust; anger; or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.