Wearable sensors are an emerging technology, with growing evidence supporting their application in sport performance enhancement. This study utilized data collected from a tri-axial inertial sensor on the wrist of ten recreational and eight professional basketball players while they performed free-throws, to classify their skill levels. We employed a fully connected convolutional neural network (CNN) for the classification task, using 64% of the data for training, 16% for validation, and the remaining 20% for testing the model’s performance. In the case of considering a single parameter from the inertial sensor, the most accurate individual components were upward acceleration (AX), with an accuracy of 82% (sensitivity = 0.79; specificity = 0.84), forward acceleration (AZ), with an accuracy of 80% (sensitivity = 0.78; specificity = 0.83), and wrist angular velocity in the sagittal plane (GY), with an accuracy of 77% (sensitivity = 0.73; specificity = 0.79). The highest accuracy of the classification was achieved when these CNN inputs utilized a stack-up matrix of these three axes, resulting in an accuracy of 88% (sensitivity = 0.87, specificity = 0.90). Applying the CNN to data from a single wearable sensor successfully classified basketball players as recreational or professional with an accuracy of up to 88%. This study represents a step towards the development of a biofeedback device to improve free-throw shooting technique.

Several researchers have found that a user's mouse position gives an indication of the user's gaze during web search and other tasks. As part of a user study that involved relevance judging of document summaries and full documents, we recorded users' mouse movements. We found that in a large number of cases, the users did nothing more with their mouse than move it to the buttons used for recording the relevance decision. In addition, we found that different search topics can result in large differences in the amount of mouse movement that is indicative of user attention. For simple reading tasks, such as short document summaries, mouse-tracking does not appear to be an effective means of discerning user attention. While more complex tasks may allow mouse movements to provide information regarding user attention, on average, indications of user attention existed in only 59% of the relevance judgments made for full documents.

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