Increasing patient engagement in rehabilitation exercises using computer-based citizen science

Use of Virtual Reality in Physical Therapy as an Intervention and Diagnostic Tool

Within the past decade, the integration of computer-generated virtual realities (VRs) has witnessed a significant rise in the field of healthcare, particularly in diagnosis and treatment applications. These VR systems have found extensive use in physical therapy, rehabilitation, research, and assessment. This narrative review article is aimed at providing a comprehensive overview of the literature regarding the implementation of VR in the physical therapy profession. The primary objective of this review is to provide information to clinicians about the diverse applications of VR and its potential advantages in intervening across various patient populations and diagnoses during rehabilitation therapy. Through in-depth discussions with experts and a thorough review of pertinent literature, several significant aspects of the topic were identified. Subsequently, we carried out an online search to investigate the prevalent utilization of VR systems within healthcare, both as assessment tools and for therapeutic interventions. Our examination encompassed a total of 56 articles, with supplementary references incorporated as required.

Our Health: exploring interdisciplinarity and community-based participatory research in a higher education science shop

This paper presents a qualitative case study of the experiences of student and community partners involved in collaborative health research in the context of an extra-curricular higher education science shop: Our Health. Our Health community partners set research questions around health and well-being, and conduct research with interdisciplinary groups of students using a community-based participatory research model. Our case study explores the benefits and challenges that this approach raises for students and community partners as they navigate the complexities of stepping beyond disciplinary boundaries and relationships to develop new research insights and methodologies. This qualitative case study draws on: grounded theory to analyse online focus groups with participating undergraduate students and community partners; semi-structured interviews with graduate students and key university staff members; and online project meetings. For the latter, we used non-participant observation to observe community members and students at work in online meetings, co-creating evolving knowledge around the lived experiences of health issues. Through these methods, we developed a deeper understanding of the relational modes of community–student collaboration in community-based participatory research. Our findings demonstrate the key role played by interdisciplinarity in the context of a community-based participatory research approach in enabling students and community partners to develop their intrapersonal skills, health research skills and knowledge integration skills, while strengthening connections between the academy and wider communities.

Data-Driven Classification of Human Movements in Virtual Reality-Based Serious Games: Preclinical Rehabilitation Study in Citizen Science

BACKGROUND

Sustained engagement is essential for the success of telerehabilitation programs. However, patients' lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science-based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR).

OBJECTIVE

This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert.

METHODS

In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users' range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software's response to their movements. During calibration, the time series of the users' movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements.

RESULTS

The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%).

CONCLUSIONS

Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation.

Towards a Platform for Robot-Assisted Minimally-Supervised Therapy of Hand Function: Design and Pilot Usability Evaluation

BACKGROUND

Robot-assisted therapy can increase therapy dose after stroke, which is often considered insufficient in clinical practice and after discharge, especially with respect to hand function. Thus far, there has been a focus on rather complex systems that require therapist supervision. To better exploit the potential of robot-assisted therapy, we propose a platform designed for minimal therapist supervision, and present the preliminary evaluation of its immediate usability, one of the main and frequently neglected challenges for real-world application. Such an approach could help increase therapy dose by allowing the training of multiple patients in parallel by a single therapist, as well as independent training in the clinic or at home.

METHODS

We implemented design changes on a hand rehabilitation robot, considering aspects relevant to enabling minimally-supervised therapy, such as new physical/graphical user interfaces and two functional therapy exercises to train hand motor coordination, somatosensation and memory. Ten participants with chronic stroke assessed the usability of the platform and reported the perceived workload during a single therapy session with minimal supervision. The ability to independently use the platform was evaluated with a checklist.

RESULTS

Participants were able to independently perform the therapy session after a short familiarization period, requiring assistance in only 13.46 (7.69-19.23)% of the tasks. They assigned good-to-excellent scores on the System Usability Scale to the user-interface and the exercises [85.00 (75.63-86.88) and 73.75 (63.13-83.75) out of 100, respectively]. Nine participants stated that they would use the platform frequently. Perceived workloads lay within desired workload bands. Object grasping with simultaneous control of forearm pronosupination and stiffness discrimination were identified as the most difficult tasks.

DISCUSSION

Our findings demonstrate that a robot-assisted therapy device can be rendered safely and intuitively usable upon first exposure with minimal supervision through compliance with usability and perceived workload requirements. The preliminary usability evaluation identified usability challenges that should be solved to allow real-world minimally-supervised use. Such a platform could complement conventional therapy, allowing to provide increased dose with the available resources, and establish a continuum of care that progressively increases therapy lead of the patient from the clinic to the home.

Efficacy of dance-based paradigms, wearable sensors, and auditory feedback for gait retraining in children: A feasibility study

BACKGROUND

Different feedback modes such as auditory, visual and haptic have been used in the past for gait retraining or learning movement patterns. The primary goal of this study was to investigate whether real time auditory feedback would be effective in children learning novel, dance-based movement patterns. For this purpose, a prototype wearable sensor was developed to provide auditory feedback whenever a child touches their heel to the ground.

METHODS

To test the effectiveness of the auditory feedback in learning new patterns, typically developing children were taught simple Indian dance protocols consisting of four counts of foot-work which involved alternating heel-toe movements. The effect of wearing the sensor was assessed by the maximum vertical force with which the subjects struck their foot on the plate.

RESULTS

Auditory feedback reduced the learning time and increased the number of correct movement patterns for trial duration of 2 min. The prototype device did not alter the maximum force with which the subject placed the foot on the ground.

CONCLUSIONS

Real time auditory feedback can be reliably used to learn novel movement patterns.

Effects of Presence and Challenge Variations on Emotional Engagement in Immersive Virtual Environments

Serious games and immersive virtual reality promote emotional engagement during learning tasks, mostly by providing (1) skill-adapted challenges with performance feedback (for trial and error learning) and (2) enhanced presence (further reactions to multimodal stimuli), respectively. However, it is still unclear how each of these two strategies independently influence emotional states to engage subjects to a task. This study assessed the dimensions of emotion (valence-arousal-dominance) of 87 healthy subjects in a virtual game, assigned to 2 groups that were exposed to a different set of 5 trials: Group A experienced game variations by virtual factors affecting user's presence, whereas group B experienced levels of difficulty, affecting challenge. Emotional reports and 26 features extracted from physiological signals were statistically analyzed. Results showed that presence-based experimental conditions were able to modify the sense of arousal, whereas valence and dominance responded to challenge variation, i.e. were positively correlated with game score. Arousal is likely to increase with low sense of coexistence (social presence) and decrease with low scenario realism (physical presence). Faster breathing and higher skin conductance (SC) were detected at high challenge, whereas heart rate variability and SC increased with higher arousal. The evidence from this study suggests that both strategies can be used to separately influence dimensions of emotion, pointing out the customization of presence-based factors as a promising method to adjust emotional engagement by impacting arousal. Further research should be undertaken to identify the independent effect of single presence factors on emotional states.

Using demographics toward efficient data classification in citizen science: a Bayesian approach

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

Monetary and Social Rewards for Crowdsourcing

Crowdsourcing of inventive activities is a particular form of crowdsourcing that helps firms to innovate by involving dispersed individuals to exploit “crowd wisdom”. In this context, the greater the number of contributions, the greater the possibility to gather extremely valuable ideas to produce innovative products and services. While monetary and social rewards can be an effective means to boost contributors’ extrinsic and intrinsic motivations to contribute, a theoretical understanding and empirical evidence of their effects are lacking. This paper focused on the crowdsourcing of inventive activities, initiated by listed companies worldwide, from 2007 to 2014. Our findings shed light on the influence of monetary and social rewards on the number of ideas collected. In particular, we analyzed the impact on the number of contributions brought about by monetary rewards and noted a positive influence related to its presence and also a negative effect related to the amount of the compensation. Moreover, we have demonstrated how the presence of a social cause is beneficial to the number of contributions. Consequently, we contribute to a scholarly understanding of the crowdsourcing phenomenon and we have provided guidance to managers seeking to initiate crowdsourcing campaigns.

The Role of Social Interactions in Motor Performance: Feasibility Study Toward Enhanced Motivation in Telerehabilitation

BACKGROUND

Robot-mediated telerehabilitation has the potential to provide patient-tailored cost-effective rehabilitation. However, compliance with therapy can be a problem that undermines the prospective advantages of telerehabilitation technologies. Lack of motivation has been identified as a major factor that hampers compliance. Exploring various motivational interventions, the integration of citizen science activities in robotics-based rehabilitation has been shown to increase patients' motivation to engage in otherwise tedious exercises by tapping into a vast array of intrinsic motivational drivers. Patient engagement can be further enhanced by the incorporation of social interactions.

OBJECTIVE

Herein, we explored the possibility of bolstering engagement in physical therapy by leveraging cooperation among users in an environmental citizen science project. Specifically, we studied how the integration of cooperation into citizen science influences user engagement, enjoyment, and motor performance. Furthermore, we investigated how the degree of interdependence among users, such that is imposed through independent or joint termination (JT), affects participation in citizen science-based telerehabilitation.

METHODS

We developed a Web-based citizen science platform in which users work in pairs to classify images collected by an aquatic robot in a polluted water canal. The classification was carried out by labeling objects that appear in the images and trashing irrelevant labels. The system was interfaced by a haptic device for fine motor rehabilitation. We recruited 120 healthy volunteers to operate the platform. Of these volunteers, 98 were cooperating in pairs, with 1 user tagging images and the other trashing labels. The other 22 volunteers performed both tasks alone. To vary the degree of interdependence within cooperation, we implemented independent and JTs.

RESULTS

We found that users' engagement and motor performance are modulated by their assigned task and the degree of interdependence. Motor performance increased when users were subjected to independent termination (P=.02), yet enjoyment decreased when users were subjected to JT (P=.005). A significant interaction between the type of termination and the task was found to influence productivity (P<.001) as well as mean speed, peak speed, and path length of the controller (P=.01, P=.006, and P<.001, respectively).

CONCLUSIONS

Depending on the type of termination, cooperation was not always positively associated with engagement, enjoyment, and motor performance. Therefore, enhancing user engagement, satisfaction, and motor performance through cooperative citizen science tasks relies on both the degree of interdependence among users and the perceived nature of the task. Cooperative citizen science may enhance motivation in robotics-based telerehabilitation, if designed attentively.

Producing knowledge by admitting ignorance: Enhancing data quality through an "I don't know" option in citizen science

The "noisy labeler problem" in crowdsourced data has attracted great attention in recent years, with important ramifications in citizen science, where non-experts must produce high-quality data. Particularly relevant to citizen science is dynamic task allocation, in which the level of agreement among labelers can be progressively updated through the information-theoretic notion of entropy. Under dynamic task allocation, we hypothesized that providing volunteers with an "I don't know" option would contribute to enhancing data quality, by introducing further, useful information about the level of agreement among volunteers. We investigated the influence of an "I don't know" option on the data quality in a citizen science project that entailed classifying the image of a highly polluted canal into "threat" or "no threat" to the environment. Our results show that an "I don't know" option can enhance accuracy, compared to the case without the option; such an improvement mostly affects the true negative rather than the true positive rate. In an information-theoretic sense, these seemingly meaningless blank votes constitute a meaningful piece of information to help enhance accuracy of data in citizen science.

Innovative STRoke Interactive Virtual thErapy (STRIVE) online platform for community-dwelling stroke survivors: a randomised controlled trial protocol

INTRODUCTION

The STRoke Interactive Virtual thErapy (STRIVE) intervention provides community-dwelling stroke survivors access to individualised, remotely supervised progressive exercise training via an online platform. This trial aims to determine the clinical efficacy of the STRIVE intervention and its effect on brain activity in community-dwelling stroke survivors.

METHODS AND ANALYSIS

In a multisite, assessor-blinded randomised controlled trial, 60 stroke survivors >3 months poststroke with mild-to-moderate upper extremity impairment will be recruited and equally randomised by location (Melbourne, Victoria or Launceston, Tasmania) to receive 8 weeks of virtual therapy (VT) at a local exercise training facility or usual care. Participants allocated to VT will perform 3-5 upper limb exercises individualised to their impairment severity and preference, while participants allocated to usual care will be asked to maintain their usual daily activities. The primary outcome measures will be upper limb motor function and impairment, which will be assessed using the Action Research Arm Test and Upper Extremity Fugl-Meyer, respectively. Secondary outcome measures include upper extremity function and spasticity, as measured by the box and block test and Modified AshworthScale, respectively, and task-related changes in bilateral sensorimotor cortex haemodynamics during hand reaching and wrist extension movements as measured by functional near-infrared spectroscopy. Quality of life will be measured using the Euro-Quality of Life-5 Dimension-5 Level Scale, and the Motor Activity Log-28 will be used to measure use of the hemiparetic arm. All measures will be assessed at baseline and immediately postintervention.

ETHICS AND DISSEMINATION

The study was approved by the Deakin University Human Research Ethics Committee in May 2017 (No. 2017-087). The results will be disseminated in peer-reviewed journals and presented at major international stroke meetings.

TRIAL REGISTRATION NUMBER

ACTRN12617000745347; Pre-results.

In Memoriam: Paolo Cappa

Prof. Paolo Cappa passed away on 26 August 2016, at the age of 59, after a long and courageous fight against cancer. Paolo Cappa was a Professor in Mechanical and Thermal Measurements and Experimental Biomechanics in the Department of Mechanical and Aerospace Engineering of Sapienza University of Rome, where he had also served as the Head of the Department, and a Research Professor in the Department of Mechanical and Aerospace Engineering of New York University Tandon School of Engineering. During his intense, yet short, career, he made several significant scientific contributions within the discipline of Mechanical and Thermal Measurements, pioneering fundamental applications to Biomechanics. He co-founded the Motion Analysis and Robotics Laboratory (MARLab) within the Neurorehabilitation Division of IRCCS Pediatric Hospital “Bambino Gesu”, in Rome, to fuel transitional research from the laboratory to clinical practice. Through collaboration with neurologists and physiatrists at MARLab, Prof. Cappa led the development of a powerful array of novel mechanical solutions to wearable robotics for pediatric patients, addressing dramatic needs for children’s health and contributing to the training of an entire generation of Mechanical Engineering students.

Brain-actuated gait trainer with visual and proprioceptive feedback

OBJECTIVE

Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension.

APPROACH

We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects.

MAIN RESULTS

(i) For real-time classification, the average accuracy was [Formula: see text]% and [Formula: see text]% for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback ([Formula: see text]%) was significantly better than with visual feedback ([Formula: see text]%), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn.

SIGNIFICANCE

The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.

A natural user interface to integrate citizen science and physical exercise

Citizen science enables volunteers to contribute to scientific projects, where massive data collection and analysis are often required. Volunteers participate in citizen science activities online from their homes or in the field and are motivated by both intrinsic and extrinsic factors. Here, we investigated the possibility of integrating citizen science tasks within physical exercises envisaged as part of a potential rehabilitation therapy session. The citizen science activity entailed environmental mapping of a polluted body of water using a miniature instrumented boat, which was remotely controlled by the participants through their physical gesture tracked by a low-cost markerless motion capture system. Our findings demonstrate that the natural user interface offers an engaging and effective means for performing environmental monitoring tasks. At the same time, the citizen science activity increases the commitment of the participants, leading to a better motion performance, quantified through an array of objective indices. The study constitutes a first and necessary step toward rehabilitative treatments of the upper limb through citizen science and low-cost markerless optical systems.

The Present and Future of Robotic Technology in Rehabilitation

Robotic technology designed to assist rehabilitation can potentially increase the efficiency of and accessibility to therapy by assisting therapists to provide consistent training for extended periods of time, and collecting data to assess progress. Automatization of therapy may enable many patients to be treated simultaneously and possibly even remotely, in the comfort of their own homes, through telerehabilitation. The data collected can be used to objectively assess performance and document compliance as well as progress. All of these characteristics can make therapists more efficient in treating larger numbers of patients. Most importantly for the patient, it can increase access to therapy which is often in high demand and rationed severely in today's fiscal climate. In recent years, many consumer grade low-cost and off-the-shelf devices have been adopted for use in therapy sessions and methods for increasing motivation and engagement have been integrated with them. This review paper outlines the effort devoted to the development and integration of robotic technology for rehabilitation.

Activating social strategies: Face-to-face interaction in technology-mediated citizen science

The use of crowds in research activities by public and private organizations is growing under different forms. Citizen science is a popular means of engaging the general public in research activities led by professional scientists. By involving a large number of amateur scientists, citizen science enables distributed data collection and analysis on a scale that would be otherwise difficult and costly to achieve. While advancements in information technology in the past few decades have fostered the growth of citizen science through online participation, several projects continue to fail due to limited participation. Such web-based projects may isolate the citizen scientists from the researchers. By adopting the perspective of social strategy, we investigate within a measure-manipulate-measure experiment if motivations to participate in a citizen science project can be positively influenced by a face-to-face interaction with the scientists leading the project. Such an interaction provides the participants with the possibility of asking questions on the spot and obtaining a detailed explanation of the citizen science project, its scientific merit, and environmental relevance. Social and cultural factors that moderate the effect brought about by face-to-face interactions on the motivations are also dissected and analyzed. Our findings provide an exploratory insight into a means for motivating crowds to participate in online environmental monitoring projects, also offering possible selection criteria of target audience.

Effectiveness of the Virtual Reality System Toyra on Upper Limb Function in People with Tetraplegia: A Pilot Randomized Clinical Trial

The aim of this study was to investigate the effects of a virtual reality program combined with conventional therapy in upper limb function in people with tetraplegia and to provide data about patients' satisfaction with the virtual reality system. Thirty-one people with subacute complete cervical tetraplegia participated in the study. Experimental group received 15 sessions with Toyra(®) virtual reality system for 5 weeks, 30 minutes/day, 3 days/week in addition to conventional therapy, while control group only received conventional therapy. All patients were assessed at baseline, after intervention, and at three-month follow-up with a battery of clinical, functional, and satisfaction scales. Control group showed significant improvements in the manual muscle test (p = 0,043, partial η (2) = 0,22) in the follow-up evaluation. Both groups demonstrated clinical, but nonsignificant, changes to their arm function in 4 of the 5 scales used. All patients showed a high level of satisfaction with the virtual reality system. This study showed that virtual reality added to conventional therapy produces similar results in upper limb function compared to only conventional therapy. Moreover, the gaming aspects incorporated in conventional rehabilitation appear to produce high motivation during execution of the assigned tasks. This trial is registered with EudraCT number 2015-002157-35.