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We introduce FingerSound, an input technology to recognize unistroke thumb gestures, which are easy to learn and can be performed through eyes-free interaction. The gestures are performed using a thumb-mounted ring comprising a contact microphone and a gyroscope sensor. A K-Nearest-Neighbor(KNN) model with a distance function of Dynamic Time Warping (DTW) is built to recognize up to 42 common unistroke gestures. A user study, where the real-time classification results were given, shows an accuracy of 92%-98% by a machine learning model built with only 3 training samples per gesture. Based on the user study results, we further discuss the opportunities, challenges and practical limitations of FingerSound when deploying it to real-world applications in the future.
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The small size of wearable devices limits the efficiency and scope of possible user interactions, as inputs are typically constrained to two dimensions: the touchscreen surface. We present SoundTrak, an active acoustic sensing technique that enables a user to interact with wearable devices in the surrounding 3D space by continuously tracking the finger position with high resolution. The user wears a ring with an embedded miniature speaker sending an acoustic signal at a specific frequency (e.g., 11 kHz), which is captured by an array of miniature, inexpensive microphones on the target wearable device. A novel algorithm is designed to localize the finger’s position in 3D space by extracting phase information from the received acoustic signals. We evaluated SoundTrak in a volume of space (20cm × 16cm × 11cm) around a smartwatch, and show an average accuracy of 1.3 cm. We report on results from a Fitts’ Law experiment with 10 participants as the evaluation of the real-time prototype. We also present a set of applications which are supported by this 3D input technique, and show the practical challenges that need to be addressed before widespread use.
Blood glucose measurement is commonly used to screen for and monitor diabetes, a chronic condition characterized by the inability to effectively modulate blood glucose that can lead to heart disease, vision loss, and kidney failure. Early detection of prediabetes can forestall or reverse more serious illness if healthy lifestyle adjustments or medical interventions are made in a timely manner. Current diabetes screening methods require visits to a healthcare facility and use of over-the-counter glucose-testing devices (glucometers), both of which are costly or inaccessible for many populations, reducing the chances of early disease detection. We therefore developed GlucoScreen, a readerless glucose test strip that enables affordable, single-use, at-home glucose testing, leveraging the user's touchscreen cellphone for reading and displaying results. By integrating minimal, low-cost electronics with commercially available blood glucose testing strips, the GlucoScreen prototype introduces a new type of low-cost, battery-free glucose testing tool that works with any smartphone, obviating the need to purchase a separate dedicated reader. Our key innovation is using the phone's capacitive touchscreen for the readout of the minimally modified commercially available glucose test strips. In an in vitro evaluation with artificial glucose solutions, we tested GlucoScreen with five different phones and compared the findings to two common glucometers, AccuChek and True Metrix. The mean absolute error (MAE) for our GlucoScreen prototype was 4.52 mg/dl (Accu-Chek test strips) and 3.7 mg/dl (True Metrix test strips), compared to 4.98 mg/dl and 5.44 mg/dl for the AccuChek glucometer and True Metrix glucometer, respectively. In a clinical investigation with 75 patients, GlucoScreen had a MAE of 10.47 mg/dl, while the AccuChek glucometer had a 9.88 mg/dl MAE. These results indicate that GlucoScreen's performance is comparable to that of commonly available over-the-counter blood glucose testing devices. With further development and validation, GlucoScreen has the potential to facilitate large-scale and lower cost diabetes screening. This work employs GlucoScreen's smartphone-based technology for glucose testing, but it could be extended to build other readerless electrochemical assays in the future.
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