Algorithm based on one monocular video delivers highly valid and reliable gait parameters.

Despite its paramount importance for manifold use cases (e.g., in the health care industry, sports, rehabilitation and fitness assessment), sufficiently valid and reliable gait parameter measurement is still limited to high-tech gait laboratories mostly. Here, we demonstrate the excellent validity and test-retest repeatability of a novel gait assessment system which is built upon modern convolutional neural networks to extract three-dimensional skeleton joints from monocular frontal-view videos of walking humans. The validity study is based on a comparison to the GAITRite pressure-sensitive walkway system. All measured gait parameters (gait speed, cadence, step length and step time) showed excellent concurrent validity for multiple walk trials at normal and fast gait speeds. The test-retest-repeatability is on the same level as the GAITRite system. In conclusion, we are convinced that our results can pave the way for cost, space and operationally effective gait analysis in broad mainstream applications. Most sensor-based systems are costly, must be operated by extensively trained personnel (e.g., motion capture systems) or-even if not quite as costly-still possess considerable complexity (e.g., wearable sensors). In contrast, a video sufficient for the assessment method presented here can be obtained by anyone, without much training, via a smartphone camera.

Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study.

BACKGROUND: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants. OBJECTIVE: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app's discriminative ability based on real-world data. METHODS: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score's ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model. RESULTS: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%. CONCLUSIONS: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score's prospective accuracy will be further validated in a prospective trial.

From nonlinear reaction-diffusion processes to permanent microscale structures.

Biomorphs are polycrystalline aggregates that self-assemble during inorganic precipitation reactions. The shape repertoire of these microstructures include hemispherical objects with complicated internal features such as radial spikes and cones as well as folded sheets reminiscent of corals. We propose that at the microscale, some of these patterns are caused by nonlinear reaction-diffusion processes and present a simple model for this unconventional type of precipitation. The model consists of three reaction steps that convert a reactant species autocatalytically into an intermediate and eventually into a solid, immobile product. Numerical simulations of the model in three space dimensions reveal product structures that are similar to the experimentally observed biomorphs.

A novel technique to initiate and investigate scroll waves in thin layers of the photosensitive Belousov-Zhabotinsky reaction.

While free scroll rings are non-stationary objects that either grow or contract with time, spatial confinement can have a large impact on their evolution reaching from significant lifetime extension (J.F. Totz, H. Engel, O. Steinbock, New J. Phys. 17, 093043 (2015)) up to formation of stable stationary and breathing pacemakers (A. Azhand, J.F. Totz, H. Engel, EPL 108, 10004 (2014)). Here, we explore the parameter range in which the interaction between an axis-symmetric scroll ring and a confining planar no-flux boundary can be studied experimentally in transparent gel layers supporting chemical wave propagation in the photosensitive variant of the Belousov-Zhabotinsky medium. Based on full three-dimensional simulations of the underlying modified complete Oregonator model for experimentally realistic parameters, we determine the conditions for successful initiation of scroll rings in a phase diagram spanned by the layer thickness and the applied light intensity. Furthermore, we discuss whether the illumination-induced excitability gradient due to Lambert-Beer's law as well as a possible inclination of the filament plane with respect to the no-flux boundary can destabilize the scroll ring.