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Acute and planned transportations of patients are major tasks for emergency medical services (EMS) and often result in substantial physical strains with a major impact on the workers’ health, because current transportation aids cannot provide sufficient support, especially on stairs. A new stair-climbing and self-balancing approach (SEBARES) has been developed and its usability is evaluated in the context of this paper. Twelve participants operated a prototype in a transportation scenario and user forces, user joint angles and the perceived usability were evaluated. Results show that user forces were within long-term acceptable ergonomic limits for over 90% of the transportation time and a mainly healthy upright posture of the back could be maintained. This resulted in a healthy working posture for 85% of the time, according to the OWAS method, and a good perceived usability. A comparison to the most ergonomic aid according to literature, a caterpillar stair chair, reveals that similar upright postures are assumed, while the operation of SEBARES required only 47% of the forces to operate the caterpillar stair chair. A comparison to a previous field study indicates a reduction of strenuous working postures by a factor of three, which further confirms the ergonomic advantages of this concept.

A novel approach for a patient transportation aid for emergency medical services bases on a wheel hub stair-climbing mechanism, which currently requires a manual adjustment relative to the stair edges. In this paper, an approach for an automation is presented which utilizes two distance sensors to characterize stairs and determine the relative position to them. A controller can then adjust the system’s position automatically. A user supervision concept copes with sensor inaccuracies or errors, resulting in a semi-automatic process. Within a formative usability study ( n = 11 n=11 users) the algorithm was able to reconstruct the stairs and drive the system neither falling down nor colliding with steps. The semi-automatic process reduced climbing time by 59 % and the participants reported a higher subjective usability compared to manual stair climbing.

The number of total knee arthroplasties performed world-wide are on a rise. Patient-specific planning and implants may improve surgical outcomes, but require 3D models of the bones involved. Ultrasound may become a cheap and non-harmful imaging modality, if the short comings of segmentation techniques in terms of automation, accuracy and robustness are overcome. Furthermore, any kind of ultrasound-based bone reconstruction must involve some kind of model completion, in order to handle occluded areas, e.g., the frontal femur. A fully-automatic and robust processing pipeline is proposed, generating full bone models from 3D freehand ultrasound scanning. A convolutional neural network is combined with a statistical shape model to segment and extrapolate the bone surface. We evaluate the method in-vivo on 10 subjects, comparing the ultrasound-based model to a magnetic resonance imaging (MRI) reference. The partial freehand 3D record of the femur and tibia bones deviate by 0.7 to 0.8mm from the MRI reference. After completion and on average, the full bone model shows sub-millimetric error in case of the femur and 1.24mm in case of the tibia. Processing of the images is performed in real-time, and the final model fitting step is computed in less than one minute. On average, it took 22 minutes for a full record per subject.

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