Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.

This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.

A Proof of Concept Combined Using Mixed Reality for Personalized Neurorehabilitation of Cerebellar Ataxic Patients.

BACKGROUND: Guidelines for degenerative cerebellar ataxia neurorehabilitation suggest intensive coordinative training based on physiotherapeutic exercises. Scientific studies demonstrate virtual exergaming therapeutic value. However, patient-based personalization, post processing analyses and specific audio-visual feedbacks are not provided. This paper presents a wearable motion tracking system with recording and playback features. This system has been specifically designed for ataxic patients, for upper limbs coordination studies with the aim to retrain movement in a neurorehabilitation setting. Suggestions from neurologists and ataxia patients were considered to overcome the shortcomings of virtual systems and implement exergaming. METHODS: The system consists of the mixed-reality headset Hololens2 and a proprietary exergaming implemented in Unity. Hololens2 can track and save upper limb parameters, head position and gaze direction in runtime. RESULTS: Data collected from a healthy subject are reported to demonstrate features and outputs of the system. CONCLUSIONS: Although further improvements and validations are needed, the system meets the needs of a dynamic patient-based exergaming for patients with cerebellar ataxia. Compared with existing solutions, the mixed-reality system is designed to provide an effective and safe therapeutic exergaming that supports both primary and secondary goals of an exergaming: what a patient should do and how patient actions should be performed.

Sensing and Control Strategies for a Synergy-Based, Cable-Driven Exosuit via a Modular Test Bench.

Ageing results in the eventual loss of muscle mass and strength, joint problems, and overall slowing of movements, with a greater risk of suffering falls or other such accidents. The use of gait assistance exoskeletons can help in the active aging of this segment of the population. Given the user specificity of the mechanics and control these devices need, the facility used to test different design parameters is indispensable. This work deals with the modeling and construction of a modular test bench and prototype exosuit to test different mounting and control schemes for a cable-driven exoskeleton or exosuit. The test bench allows the experimental implementation of postural or kinematic synergies to assist multiple joints by using only one actuator and the optimization of the control scheme to better adapt to the characteristics of the specific patient. The design is open to the research community and it is expected to improve the design of cable-driven systems for exosuits.

Automated Curb Recognition and Negotiation for Robotic Wheelchairs.

Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user's speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.

Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors.

BACKGROUND: A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms for pathological populations. The objectives of this research are to design and validate open-source algorithms for activity intensity quantification and classification. METHODS: Two versions of a quantification algorithm are proposed (fixed [FB] and modifiable bandwidth [MB]) along with two versions of a classification algorithm (discrete [DM] vs. continuous methods [CM]). The results of these algorithms were compared to those of a commercial activity intensity count solution (ActiLife) with datasets from four activities (n = 24 participants). RESULTS: The FB and MB algorithms gave similar results as ActiLife (r > 0.96). The DM algorithm is similar to a ActiLife (r ≥ 0.99). The CM algorithm differs (r ≥ 0.89) but is more precise. CONCLUSION: The combination of the FB algorithm with the DM results is a solution close to that of ActiLife. However, the MB version remains valid while being more adaptable, and the CM is more precise. This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions.

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors.

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

Egocentric video: a new tool for capturing hand use of individuals with spinal cord injury at home.

BACKGROUND: Current upper extremity outcome measures for persons with cervical spinal cord injury (cSCI) lack the ability to directly collect quantitative information in home and community environments. A wearable first-person (egocentric) camera system is presented that aims to monitor functional hand use outside of clinical settings. METHODS: The system is based on computer vision algorithms that detect the hand, segment the hand outline, distinguish the user's left or right hand, and detect functional interactions of the hand with objects during activities of daily living. The algorithm was evaluated using egocentric video recordings from 9 participants with cSCI, obtained in a home simulation laboratory. The system produces a binary hand-object interaction decision for each video frame, based on features reflecting motion cues of the hand, hand shape and colour characteristics of the scene. RESULTS: The output from the algorithm was compared with a manual labelling of the video, yielding F1-scores of 0.74 ± 0.15 for the left hand and 0.73 ± 0.15 for the right hand. From the resulting frame-by-frame binary data, functional hand use measures were extracted: the amount of total interaction as a percentage of testing time, the average duration of interactions in seconds, and the number of interactions per hour. Moderate and significant correlations were found when comparing these output measures to the results of the manual labelling, with ρ = 0.40, 0.54 and 0.55 respectively. CONCLUSIONS: These results demonstrate the potential of a wearable egocentric camera for capturing quantitative measures of hand use at home.

How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR's Rehabilitation Engineering Research Centers.

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a "total approach to rehabilitation", combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970's, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program.

Restoration of sensorimotor functions after spinal cord injury.

The purpose of this review is to discuss the achievements and perspectives regarding rehabilitation of sensorimotor functions after spinal cord injury. In the first part we discuss clinical approaches based on neuroplasticity, a term referring to all adaptive and maladaptive changes within the sensorimotor systems triggered by a spinal cord injury. Neuroplasticity can be facilitated through the training of movements with assistance as needed, and/or by electrical stimulation techniques. The success of such training in individuals with incomplete spinal cord injury critically depends on the presence of physiological proprioceptive input to the spinal cord leading to meaningful muscle activations during movement performances. The addition of rehabilitation technology, such as robotic devices allows for longer training times and provision of feedback information regarding changes in movement performance. Nevertheless, the improvement of function by such approaches for rehabilitation is limited. In the second part, we discuss preclinical approaches to restore function by compensating for the loss of descending input to spinal networks following complete spinal cord injury. This can be achieved with stimulation of spinal networks or approaches to restore their descending input. Electrical and pharmacological stimulation of spinal neural networks is still in an experimental stage; and despite promising repair studies in animal models, translations to humans up to now have not been convincing. It is likely that combinations of techniques targeting the promotion of axonal regeneration and meaningful plasticity are necessary to advance the restoration of function. In the future, refinement of animal studies may contribute to greater translational success.

"Are we there yet?" expectations and experiences with lower limb robotic exoskeletons: a qualitative evaluation of the therapist perspective.

PURPOSE: Lower limb robotic exoskeletons can assist movement, however, clinical uptake in neurorehabilitation is limited. The views and experiences of clinicians are pivotal to the successful clinical implementation of emerging technologies. This study investigates therapist perspectives of the clinical use and future role of this technology in neurorehabilitation. METHODS: Australian and New Zealand-based therapists with lower limb exoskeleton experience were recruited to complete an online survey and semi-structured interview. Survey data were transposed into tables and interviews transcribed verbatim. Qualitative data collection and analysis were guided by qualitative content analysis and interview data were thematically analysed. RESULTS: Five participants revealed that the use of exoskeletons to deliver therapy involves the interplay of human elements - experiences and perspectives of use, and mechanical elements - the device itself. Two overarching themes emerged: the "journey", with subthemes of clinical reasoning and user experience; and the "vehicle" with design features and cost as subthemes, to explore the question "Are we there yet?" CONCLUSION: Therapists expressed positive and negative perspectives from their experiences with exoskeletons, giving suggestions for design features, marketing input, and cost to enhance future use. Therapists are optimistic that this journey will see lower limb exoskeletons integral to rehabilitation service delivery.

Further innovation of design features, marketing, and cost are needed to enhance ongoing development and integration.Routine clinical implementation of lower limb exoskeletons is unlikely at this time in Australia and New Zealand.Therapists do expect lower limb exoskeletons to have an ongoing role in future rehabilitation.

Advancing clinical understanding of surface electromyography biofeedback: bridging research, teaching, and commercial applications.

INTRODUCTION: Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research. AREAS COVERED: This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors. EXPERT OPINION: The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.

The extended impact of co-designed personalised aids for people living with chronic conditions: an exploratory study in a healthcare setting.

Assistive technology has great potential to help individuals living with chronic health conditions, however devices often fail to align with the unique requirements of users. These results in device abandonment and missed opportunities to benefit people. This exploratory study aims to evaluate the short and longer-term satisfaction, psychological benefit, use and resources involved in co-designed customised assistive devices within a current healthcare service. Individuals with chronic health conditions identified daily living challenges. Eleven individuals completed the trial and were involved throughout the design process. Outcome measures evaluated the impact of the devices provided, healthcare utilisation, help required, and resources used. Nineteen custom assistive devices were produced for twenty-four challenges in daily living identified. At 3-months, eighteen devices were still being used. Daily challenges had become easier for individuals to complete and required less help from informal carers. Individuals were satisfied with the devices and service provided. Improvements in competence, adaptability and self-esteem were sustained long-term. The average clinician's time required to produce a device was 5 h 55 min, with an average cost of £203.79. People with chronic conditions were able to benefit from the co-design process resulting in satisfaction and long-term utilisation of the device, and positive psycho-social benefits. The costs associated with embedding this approach in a healthcare service were calculated. Scaling up the co-design process reduced the associated costs per device compared to previous work. Further work is required to evaluate co-designing across larger samples and explore opportunities to further improve the cost-efficiency.

By involving users in the design process, healthcare professionals can create devices that better meet users' expectations, preferences and functional needs, thereby increasing overall usability, satisfaction and utilisation long-term of the devices.Incorporating the individual's perspective and needs into the design process enabled users to better understand the solutions that could be produced and thus encouraged users to identify other challenges in daily living they faced where an assistive device could assist them.Through being provided with devices to support them with specific challenges they faced, individuals were able to perform more tasks independently, reducing the need for help from family members and informal carers for the associated tasks.Re-evaluating the solutions generated with other previous research may help identify common design solutions and features to enable further scaling-up of this co-design approach.

Anthropomorphic model rigid loading indenter with embedded sensor development for wheelchair cushion standard testing.

Develop an anthropomorphic model cushion rigid loading indenter with embedded sensors (AMCRLI-ES) to assess compression and shear forces at key locations such as trochanters and ischial tuberosities. The sensor design was optimized using finite element analysis. The AMCRLI-ES was designed with the same dimensions as specified in ISO 16840-2 tests. The AMCRLI-ES is divided into eight independent sections, and each section consists of one 3-axis load cell sensor to measure compression and shear forces normal to the compression direction. Six commercial cushions were tested using the AMCRLI-ES with standard ISO 16840-2 testing procedures. Statistical differences were found for energy dissipation between cushions. Statistical differences (p < 0.001) were found in all stiffness values. Test results showed that energy dissipation (ED) was correlated with hysteresis at 500 N with moderate to high Pearson product correlation r = -0.537, p = 0.022. The hysteresis at 250 N did not show a statistical correlation with ED. The AMCRLI-ES demonstrated the ability to measure compression and shear forces at key locations on the cushion including the thigh, trochanter, ischial tuberosity, and sacral area. It provides in-depth information about how the weight was distributed on the cushions.

Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals.

BACKGROUND: Although model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces. METHODS: Three-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization. FINDINGS: The electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks. INTERPRETATION: Quantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.

Center of mass-based posturography for free living environment applications.

BACKGROUND: Postural assessment is crucial as risk of falling is a major health problem for the elderly. The most widely used devices are force and balance plates, while center of pressure is the most studied parameter as measure of neuromuscular imbalances of the body sway. In out-of-laboratory conditions, where the use of plates is unattainable, the center of mass can serve as an alternative. This work proposes a center of mass-based posturographic measurement for free living applications. METHODS: Ten healthy and ten Parkinson's disease individuals (age = 26.1 ± 1.5, 70.4 ± 6.2 years, body mass index = 21.7 ± 2.2, 27.6 ± 2.8 kg/m2, respectively) participated in the study. A stereophotogrammetric system and a force plate were used to acquire the center of pressure and the 5th lumbar vertebra displacements during the Romberg test. The center of mass was estimated using anthropometric measures. Posturographic parameters were extracted from center of pressure, center of mass and 5th lumbar vertebra trajectories. Normalized root mean squared difference was used as metric to compare the trajectories; Spearman's correlation coefficient was computed among the posturographic parameters. FINDINGS: Low values of the metric indicated a good agreement between 5th lumbar vertebra trajectory and both center of pressure and center of mass trajectories. Statistically significant correlations were found among the postural variables. INTERPRETATION: A method to perform posturography tracking the movement of the 5th lumbar vertebra as an approximation of center of mass has been presented and validated. The method requires the solely kinematic tracking of one anatomical landmark with no need of plates for free living applications.

Mechanical design of a new device to assist eating in people with movement disorders.

Many people living with neurological disorders, such as cerebral palsy, stroke, muscular dystrophy or dystonia, experience upper limb impairments (muscle spasticity, loss of selective motor control, muscle weakness or tremors) and are unable to eat independently. This article presents the development of a new device to assist with eating, aimed at stabilizing the movement of people who have movement disorders. The design was guided by insights gathered through focus groups, with occupational therapists and engineers, about the challenges faced by individuals who have movement disorders and difficulty in eating autonomously. The proposed assistive device prototype is designed to be fixed on a table and to support a spoon. The mechanism is designed so that the spoon maintains a position parallel to the ground for the user. Dampers and inertia allow stabilizing the user's motion. A preliminary trial with five individuals living with cerebral palsy is presented to assess the prototype's performance and to guide future iterations of the prototype. Task completion time generally decreased and movement fluidity generally improved when using the assistive device prototype. The prototype showed good potential in stabilizing the spoon for the user and improving movement fluidity.

Polymeric Orthosis with Electromagnetic Stimulator Controlled by Mobile Application for Bone Fracture Healing: Evaluation of Design Concepts for Medical Use.

Background: The occurrence of bone fractures is increasing worldwide, mainly due to the health problems that follow the aging population. The use of additive manufacturing and electrical stimulators can be applied for bioactive achievements in bone healing. However, such technologies are difficult to be transferred to medical practice. This work aims to develop an orthosis with a combined magnetic field (CFM) electrostimulator that demonstrates concepts and design aspects that facilitate its use in a real scenario. Methods: A 3D-printed orthosis made of two meshes was manufactured using PLA for outer mechanical stabilization mesh and TPU for inner fixation mesh to avoid mobilization. A CFM stimulator of reduced dimension controlled by a mobile application was coupled onto the orthosis. The design concepts were evaluated by health professionals and their resistance to chemical agents commonly used in daily activities were tested. Their thermal, chemical and electrical properties were also characterized. Results: No degradation was observed after exposure to chemical agents. The CMF achieved proper intensity (20-40 µT). The thermal analysis indicated its appropriate use for being modelled during clinical assessment. Conclusion: An orthosis with a coupled electrostimulator that works with a combined magnetic field and is controlled by mobile application was developed, and it has advantageous characteristics when compared to traditional techniques for application in real medical environments.

Mini-review: Rehabilitation engineering: Research priorities and trends.

Rehabilitation Engineering is the use of engineering principles applied to rehabilitation, disability, and independent living. Google Scholar is a searchable resource that allows people from around the world to create profiles of their interests and collaborations, and it provides a means to search the broad scientific and technical literature. Google Scholar was used to identify the 150 most cited people who listed Rehabilitation Engineering in their profile. Research impact, characteristics, and areas of research of the most cited rehabilitation engineers were examined. Furthermore, gender and geographical differences in research metrics of the highest citied rehabilitation engineers were investigated. Consumer priorities in rehabilitation engineering were identified using a voice of consumer (VoC) survey and recent literature based on VoC studies. Gaps between research publication and activities and consumer priorities were identified to recommend seven areas of research with high demand and opportunity for growth and innovation. Implications.

Editor's Perspective on Health Economics in Prosthetics and Orthotics.

There is a scarcity of literature exploring Health Economics in Prosthetics and Orthotics (P&O). The P&O sector has, over the past decades, moved to a Bachelor's or Masters Degree level as the requirement for entry to practice and, with that, there has been a growing body of research and knowledge generation focusing primarily on clinical aspects and engineering advances. No corresponding body of research has emerged on the economic aspects of P&O, creating a fundamental weakness in both technical and clinical research efforts to advance this field within an economically sustainable framework. This weakness will become critical as data driven engineering advances (e.g. exoskeletons, mass customizable prostheses) and clinical improvements (e.g. osseointegration, diabetes treatments) will make reimbursement for devices ever more complex and challenging. The tension between what is possible and what is fundable will increase unless what is possible also drives down costs. Finding the right balance in Prosthetics and Orthotics will be a challenge, as this sector already struggles to justify current standards of care. This Special Edition takes a snapshot of stakeholder perspectives and opinions on the topic of Health Economics in P&O and is organized around the following stakeholder groups: End-user, Researcher (Engineering and Clinical), Prosthetic and Orthotic Practitioner and, of critical importance, four papers describing an interdisciplinary project on the Health Economics of Osseointegration that was led by a payor. Each author was also asked to provide a "Call to Action" in which they identify one or more key areas that need to be addressed in order to move forward with the barriers or opportunities they have identified in their paper. The intent of the Special Edition is to generate discussion and encourage more in-depth research on this topic.