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This paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset of computed tomography (CT) scans of 10 right hands (5F:5M, 27-37 years, free from disease or injury) imaged at 0.3 mm resolution, segmented, meshed and aligned. Model generated included pose neutralisation to remove joint angle variation during imaging. Repositioning was successful; no joint flexion variation was observed in the resulting model. The first principal component (PC) of morphological variation represented phalanx size in all fingers. Subsequent PCs showed variation in position along the palmar-dorsal axis, and bone breadth: length ratio. Finally, the models were interrogated to provide gross measures of bone lengths and joint spaces. These models have been published for open use to support wider community efforts in hand biomechanical analysis, providing bony anatomy descriptions whilst preserving the security of the underlying imaging data and privacy of the participants. The model describes a small, homogeneous population, and assumptions cannot be made about how it represents individuals outside the training dataset. However, it supplements anthropometric datasets with additional shape information, and may be useful for investigating factors such as joint morphology and design of hand-interfacing devices and products. The model has been shared as an open-source repository ( https://github.com/abel-research/OpenHands ), and we encourage the community to use and contribute to it.

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PURPOSE: While innovation is known to catalyse solutions to global sustainable development challenges, lack of engagement from stakeholders during conceptualisation and development may influence the degree of success of implementation. METHODS AND MATERIALS: This paper presents a complete and novel engagement methodology, developed from value led business modelling approaches, for working with multi-sector stakeholders. The methodology can be used to determine barriers and facilitators to clinical practice innovations or translational research, within a country-specific context. The approach has then been applied in the Cambodian prosthetics and orthotics sector to provide a practice-based exemplar application of the framework. RESULTS: This approach seeks to ensure the suitability and sustainability of clinical practice and research programmes being implemented within a complex ecosystem. A theoretical basis, drawn from academic and business innovation sectors, has been consolidated and adapted for practical application to design, direct, and inform initiatives in low resource settings. CONCLUSIONS: The methods presented provide a way to both develop and articulate the mission, vision, and goals of any proposed change, and to effectively communicate these with stakeholders in a way that engages the personal and professional values that exist in their ecosystem. It provides a structured process through which meaningful conversations can happen, and a basis for relationship management with key stakeholders; intrinsic to enable a sustained legacy from research and development.

The engagement from stakeholders during conceptualisation and throughout development can determine the success, or not, of any implementation and scale of innovation.This paper presents a conceptual stakeholder-led engagement methodology, developed from value led business modelling approaches, for determining barriers and facilitators to translational global healthcare research in a country-specific context, in this case the Cambodian prosthetics and orthotics sector.Subsequent research and development work in this area needs to carefully manage and negotiate influencing factors identified through the application of the described methodology, to ensure initiatives (whether research or wider national development work) are sustainable and successful.

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This study assessed the accuracy of marker-based kinematic analysis of the fingers, considering soft tissue artefacts (STA) and marker imaging uncertainty. We collected CT images of the hand from healthy volunteers with fingers in full extension, mid- and full-flexion, including motion capture markers. Bones and markers were segmented and meshed. The bone meshes for each volunteer's scans were aligned using the proximal phalanx to study the proximal interphalangeal joint (PIP), and using the middle phalanx to study the distal interphalangeal joint (DIP). The angle changes between positions were extracted. The HAWK protocol was used to calculate PIP and DIP joint flexion angles in each position based on the marker centroids. Finally the marker locations were 'corrected' relative to the underlying bones, and the flexion angles recalculated. Static and dynamic marker imaging uncertainty was evaluated using a wand. A strong positive correlation was observed between marker- and CT-based joint angle changes with 0.980 and 0.892 regression slopes for PIP and DIP, respectively, and Root Mean Squared Errors below 4°. Notably for the PIP joint, correlation was worsened by STA correction. The 95% imaging uncertainty interval was < ± 1° for joints, and < ± 0.25 mm for segment lengths. In summary, the HAWK marker set's accuracy was characterised for finger joint flexion angle changes in a small group of healthy individuals and static poses, and was found to benefit from skin movements during flexion.

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The primary objective of the experiments was to investigate the wrist motion of a person while they were carrying out a prehensile task from a clinical hand function test. A six-camera movement system was used to observe the wrist motion of 10 participants. A very light sphere and a heavy sphere were used in the experiments to study any mass effects. While seated at a table, a participant moved a sphere over a small obstacle using their dominant hand. The participants were observed to move their wrist at a constant angular velocity. This phenomenon has not been reported previously. Theoretically, the muscles of the wrist provide an impulse of force at the start of the rotation while the forearm maintains a constant vertical force on a sphere. Light-heavy mean differences for the velocities, absolute velocities, angles and times taken showed no significant differences (p = 0.05).

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