Background: Numerous multidimensional assessment tools have been developed to measure frailty; however, the clinical feasibility of these tools is limited. We previously developed and validated an upper-extremity function (UEF) assessment method that incorporates wearable motion sensors. The purpose of the current study was to: 1) cross-sectionally validate the UEF method in a larger sample in comparison with the Fried index; 2) develop a UEF frailty index to predict frailty categories including non-frail, pre-frail, and frail based on UEF parameters and demographic information, using the Fried index as the gold standard; and 3) develop a UEF continuous score (points scores for each UEF parameter and a total frailty score) based on UEF parameters and demographic information, using the Fried index as the gold standard. Methods: We performed a cross-sectional validation and index development study within the Banner Medical Center, Tucson, and Banner Sun Health Research Institute, Sun City, Arizona. Community-dwelling and outpatient older adults (>= 60 years; n = 352; 132 non-frail, 175 pre-frail, and 45 frail based on Fried criteria) were recruited. For the UEF test, each participant performed a 20-s elbow flexion, within which they repetitively and rapidly flexed and extended their dominant elbow. Using elbow motion outcomes two UEF indexes were developed (categorical and score). The Fried index was measured as the gold standard. Results: For the categorical index, speed of elbow flexion, elbow range of motion, elbow moment, number of flexion, speed variability and reduction within 20 s, as well as body mass index (BMI) were included as the pre-frailty/frailty predictor parameters. Results from 10-fold cross-validation showed receiver operator characteristic area under the curve of 0.77 +/- 0.07 and 0.80 +/- 0.12 for predicting Fried pre-frailty and frailty, respectively. UEF score (0.1 to 1.0) was developed using similar UEF parameters. Conclusions: We present an objective, sensor-based frailty assessment tool based on physical frailty features including slowness, weakness, exhaustion (muscle fatigue), and flexibility of upper-extremity movements. Within the current study, the method was validated cross-sectionally using the Fried index as the gold standard and the UEF categorical index and UEF frailty score were developed for research purposes and potentially for future clinical use.

The purpose of this project was to analyze the coordination between gaze and the upper limb movement while performing a predefined task. We implemented a method to simultaneously compute and visualize recorded gaze data from a head mounted eye tracker and motion data from a motion capture system in the same coordinate system.  A python script was implemented to temporarily synchronize the two systems and then proceed with the spatial/coordinate transformation which was validated with the data acquired while the subject was asked to perform specific tasks. Task 1 was to fixate his gaze on a block placed in the center of a table and the Task 2 was to stack the blocks by picking it up and placing it on top of each other. Wrist and elbow flexion-extension angles were tracked simultaneously based on reflective markers trajectories while performing the task. This was visualized and discussed on how the results of our study suggest that the eye movements play a vital role in planning, estimating, coordinating and providing feedback for the body to perform a motor task.