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.

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.

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. Using these determinants, a smartphone-based application was designed to assess the individual risk of falling with a score that combines multiple fall risk factors into one comprehensive metric. OBJECTIVE This study provides a descriptive evaluation of the designed fall risk score as well as an analysis of its 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 provide 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) are 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 non-fallers. For the sake of completeness, specificity, precision and overall accuracy were provided for each model as well. RESULTS Out of 242 participants with a mean age of 84.6 ± 6.7 years, 139 (57.4%) reported no previous falls (non-faller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 ± 12.4 points. The performance metrics for the Logistic Regression Model were AUC = 0.9; Sensitivity = 100%; Specificity = 52%; Accuracy = 73%. The performance metrics for the Gaussian Naive Bayes Model were AUC = 0.9; Sensitivity = 100%; Specificity = 52%; Accuracy = 73%. The performance metrics for the Gradient Boosting Model were AUC = 0.85; Sensitivity = 88%; Specificity = 62%; Accuracy = 73%. The performance metrics for the Support Vector Classification Model are AUC = 0.84; Sensitivity = 88%; Specificity = 67%; Accuracy = 76%. The performance metrics for the Random Forest Model were AUC = 0.84; Sensitivity = 88%; Specificity = 57%; 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 non-fallers, 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%. Hence, 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.