Modification of a simplified hip joint simulator into an ISO 14242-1 compliant design and a comparison of wear test results

The ISO 14242-1 standard for hip joint simulator wear testing specifies a set of test conditions for the simulation of normal level walking in optimal conditions. Since some of the established simulators, such as the 12-station HUT-4, are not ISO 14242-1 compliant, the present study was carried out to answer the following question. Does wear produced in ISO 14242-1 conditions differ from that obtained earlier with the simplified HUT-4 hip joint simulator for similar specimens in normal level walking, optimal conditions? The HUT-4 hip joint simulator was made ISO compliant by an implementation of a number of modifications. One of the modifications was the design and construction of a novel servo-electric load actuator with proven dynamic response. The other modifications were related to the Euler sequence of motions, acetabular abduction angle, enclosure of the lubricant chamber, and temperature control. A 5 million-cycle wear test with thin, large-diameter VEXLPE liners resulted in a wear rate close to that obtained earlier with the HUT-4. The burnished bearing surface in both tests was in agreement with clinical retrieval studies. It appeared that a more simple, inexpensive hip joint simulator can reproduce clinical wear mechanisms. However, the simulator must meet certain basic requirements, such as the correct type of multidirectional relative motion, for which biaxial motion is sufficient. The present study was not intended to show a similarity in wear produced by the ISO 14242-1 and HUT-4 wear test systems.

Assessing the Transferability of Physical Activity Type Detection Models: Influence of Age Group Is Underappreciated

Increasing the amount of physical activity (PA) in older adults that have shifted to a sedentary lifestyle is a determining factor in decreasing health and social costs. It is, therefore, imperative to develop objective methods that accurately detect daily PA types and provide detailed PA guidance for healthy aging. Most of the existing techniques have been applied in the younger generation or validated in the laboratory. To what extent, these methods are transferable to real-life and older adults are a question that this paper aims to answer. Sixty-three participants, including 33 younger and 30 older healthy adults, participated in our study. Each participant wore five devices mounted on the left and right hips, right knee, chest, and left pocket and collected accelerometer and GPS data in both semi-structured and real-life environments. Using this dataset, we developed machine-learning models to detect PA types walking, non-level walking, jogging/running, sitting, standing, and lying. Besides, we examined the accuracy of the models within-and between-age groups applying different scenarios and validation approaches. The within-age models showed convincing classification results. The findings indicate that due to age-related behavioral differences, there are more confusion errors between walking, non-level walking, and running in older adults’ results. Using semi-structured training data, the younger adults’ models outperformed older adults’ models. However, using real-life training data alone or in combination with semi-structured data generated better results for older adults who had high real-life data quality. Assessing the transferability of the models to older adults showed that the models trained with younger adults’ data were only weakly transferable. However, training the models with a combined dataset of both age groups led to reliable transferability of results to the data of the older subgroup. We show that age-related behavioral differences can alter the PA classification performance. We demonstrate that PA type detection models that rely on combined datasets of young and older adults are strongly transferable to real-life and older adults’ data. Our results yield significant time and cost savings for future PA studies by reducing the overall volume of training data required.

An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression.

Dynamic alignment changes during level walking in patients with dropped head syndrome: analyses using a three-dimensional motion analysis system

In patients with dropped head syndrome (DHS), cervical malalignment is one of the risk factors for impaired horizontal gaze and restrictions to ambulation. The characteristics of gait in patients with DHS have not been clarified biomechanically from the viewpoint of dynamic alignment and lower limb kinematics. This study aimed to clarify kinematic and kinetic differences during level walking in patients with DHS compared to the healthy elderly. Twelve patients with DHS and healthy elderly individuals performed level walking at a self-selected speed. Spatiotemporal, kinematic, and kinetic data were recorded using a three-dimensional motion analysis system. Statistical analysis was performed to compare these data between the two groups, respectively. Compared with the healthy elderly, stride length and peak hip-joint extension angle in patients with DHS were significantly shorter and smaller. The thorax was also significantly tilted backwards. Peak ankle-joint plantar-flexion moment was significantly smaller despite larger dorsiflexion angle compared with the healthy elderly. The walking of DHS patients demonstrated kinematic and kinetic characteristics of the lower limb joints and alignment of the thorax and pelvis corresponding to their short stride and walking speed.