Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. However, mechanisms for tracking free weight exercises have not yet been explored. In this paper, we study methods that automatically recognize what type of exercise you are doing and how many repetitions you have done so far. We incorporated a three-axis accelerometer into a workout glove to track hand movements and put another accelerometer on a user's waist to track body posture. To recognize types of exercises, we tried two methods: a Naïve Bayes Classifier and Hidden Markov Models. To count repetitions developed and tested two algorithms: a peak counting algorithm and a method using the Viterbi algorithm with a Hidden Markov Model. Our experimental results showed overall recognition accuracy of around 90% over nine different exercises, and overall miscount rate of around 5%. We believe that the promising results will potentially contribute to the vision of a digital personal trainer, create a new experience for exercising, and enable physical and psychological well-being.

Abstract.We are what we eat. Our everyday food choices affect our long-term and short-term health. In the traditional health care, professionals assess and weigh each individual's dietary intake using intensive labor at high cost. In this paper, we design and implement a diet-aware dining table that can track what and how much we eat. To enable automated food tracking, the dining table is augmented with two layers of weighing and RFID sensor surfaces. We devise a weight-RFID matching algorithm to detect and distinguish how people eat. To validate our diet-aware dining table, we have performed experiments, including live dining scenarios (afternoon tea and Chinese-style dinner), multiple dining participants, and concurrent activities chosen randomly. Our experimental results have shown encouraging recognition accuracy, around 80%. We believe monitoring the dietary behaviors of individuals potentially contribute to dietaware healthcare.

The excellent mechanical properties of powder metallurgy (P/M) superalloys strongly depend on the microstructure, such as grain size, and morphology and size distribution of the ␥ Ј precipitates. The microstructure is, in turn, determined by the heat treatment, viz., solution annealing, quenching, and subsequent aging. To study the effect of the quenching process, two types of quenching methods were used to produce different quenched microstructures in a UDIMET 720LI (U720LI) alloy. One was a continuous quenching method, where samples were cooled along linearly controlled cooling profiles, each at a fixed cooling rate. This test studied the effect of cooling rate on the size of cooling ␥Ј precipitates (formed during quenching) and the consequent strengthening effect. The other test was the interrupted quenching test, which allowed tracking the growth of cooling ␥Ј precipitates with decreasing temperature during quenching at a given cooling rate. The strengthening response at each interrupt temperature was also studied. Results from the continuous cooling tests showed that the relationship between the size of the cooling ␥Ј precipitate and the cooling rate obeys a power law, with an exponential being about 0.35. The tensile strength was found to increase linearly with the cooling rate. Strengthening due to the subsequent aging treatment occurred regardless of cooling rates. The interrupted cooling tests showed that ␥ Ј growth is a linear function of decreasing temperature for a given cooling rate. A nonmonotonic degradation of tensile strength against interrupt temperature was discovered.

These findings support the importance of sleep for healthy emotional functioning in adults, and further suggest that adolescents are differentially vulnerable to the emotional consequences of sleep deprivation.