Towards co-design of rehabilitation technologies: a collaborative approach to prioritize usability issues.

Introduction: Early stakeholder engagement is critical to the successful development and translation of rehabilitation technologies, a pivotal step of which is usability testing with intended end-users. To this end, several methods employ end-user feedback to identify usability and implementation issues. However, the process of prioritizing identified issues seldom leverages the knowledge and expertise of the range of stakeholders who will ultimately affect the demand and supply of a device. This paper describes a novel method to prioritize end-user feedback using transdisciplinary stakeholder consultation and address it in subsequent product development. The proposed approach was demonstrated using a case study relating to the development of a novel technology for neural recovery after spinal cord injury. Method: Feedback from five individuals with chronic spinal cord injury was collected during two-hour usability evaluation sessions with a fully functional high-fidelity system prototype. A think-aloud and semi-structured interview protocol was used with each participant to identify usability and acceptability issues relating to the system in a 3-phase approach. Phase 1 involved extracting usability issues from think-aloud and semi-structured interview data. Phase 2 involved rating the usability issues based on their significance, technical feasibility, and implementation priority by relevant internal and external stakeholders. Finally, Phase 3 involved aggregating the usability issues according to design and implementation elements to facilitate solution generation, and these solutions were then raised as action tasks for future design iterations. Results: Sixty usability issues representing nine facets of usability were rated. Eighty percent of issues were rated to be of moderate to high significance, 83% were rated as being feasible to address, and 75% were rated as addressable using existing project resources. Fifty percent of the issues were rated to be a high priority for implementation. Evaluation of the grouped issues identified 21 tasks which were mapped to the product roadmap for integration into future design iterations. Discussion: This paper presents a method for meaningful transdisciplinary stakeholder engagement in rehabilitation technology development that can extended to other projects. Alongside a worked example, we offer practical considerations for others seeking to co-develop rehabilitation technologies.

Machine learning methods to support personalized neuromusculoskeletal modelling.

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.

Individuals with mild-to-moderate hip osteoarthritis walk with lower hip joint contact forces despite higher levels of muscle co-contraction compared to healthy individuals.

OBJECTIVE: To compare hip joint contact forces (HJCF), hip muscle forces, and hip muscle co-contraction levels between individuals with mild-to-moderate hip osteoarthritis (OA) and healthy controls during walking. DESIGN: Eighteen participants with mild-to-moderate hip OA and 23 healthy controls walked at a self-selected speed while motion capture and electromyographic data were synchronously collected. HJCF were computed using a calibrated electromyography-informed neuromusculoskeletal model. Hip joint contact forces, muscle forces, and co-contraction indices for flexor/extensor and adductor/abductor muscle groups were compared between groups using independent sample t-tests (P < 0.05). RESULTS: There was no between-group difference in self-selected walking speed. On average, participants with hip OA walked with 11% lower first peak (mean difference 235 [95% confidence interval (CI) 57-413] N) and 22% lower second peak (mean difference 574 [95%CI 304-844] N) HJCF compared to controls. Hip muscle forces were also significantly lower in the hip OA compared to control group at first (mean difference 224 [95%CI 66-382] N) and second (mean difference 782 [95%CI 399-1164] N) peak HJCF. Participants with hip OA exhibited higher levels of hip muscle co-contraction in both flexor/extensor and adductor/abductor muscle groups. Consistent with existing literature, hip joint angles (extension, adduction) and external moments (flexion, extension, adduction) were lower in hip OA compared to controls. CONCLUSION: Lower HJCF were detected in mild-to-moderate hip OA, primarily due to lower hip muscle force production, and despite higher levels of hip muscle co-contraction. Findings suggest that lower loading of the hip joint during walking is a feature of mild-to-moderate hip OA, which could have implications for the pathogenesis of hip OA and/or disease progression.

Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim.

Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time.