Analysis of 1.2 million foot scans from North America, Europe and Asia.

For decades, footwear brands have developed products using outdated methods and measurements, working with limited insight into the foot shapes and dimensions of their target customers. The integration of 3D scanning technology into footwear retail stores has made it possible for this research to analyze a database containing a large number of male and female 3D foot scans collected across North America, Europe, and Asia. Foot scans were classified into length classes with 5mm length increments; mean width, instep height, and heel width were calculated for each length class. This study confirms the existence of many statistically significant differences in mean foot measurements amongst the regions and between the sexes, and a large dispersion of foot measurements within each group of customers. Therefore, shoes should be developed separately for each group, region, and sex, and at least 3 shoe widths per length class are required to provide a proper fit for 90% of customers. Beyond this, our analysis asserts that a shoe designed for a single group will fit a different segment of the population in another group, and that existing last grading tables should be updated to reflect the foot dimensions of current consumers.

Feasibility of spirography features for objective assessment of motor function in Parkinson's disease.

OBJECTIVE: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). MATERIALS AND METHODS: This work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the "cause" (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification. RESULTS: Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases. CONCLUSIONS: The relatively high classification accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.

Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson's Disease.

A challenge for the clinical management of advanced Parkinson's disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.