Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Paths to Power: The Ascendance of Contemporary Democratic Congressional Leaders

Congressional leadership is a constantly changing phenomenon. New factors and actors are constantly affecting and altering which members ascend to positions of leadership and how that leadership is exercised. A critical change that has occurred in recent times is the inclusion of women in the congressional leadership for the first time. While there has been a great deal of theoretical work on gender and on congressional leadership, there have not been enough actual female leaders in Congress to perform a study until now. The present study examines the impact of gender, committee/legislative performance, ideology, and fundraising ability on leadership ascendancy. The variables are investigated through a comparative case study of Rep. Nancy Pelosi, Rep. Rosa DeLauro, Sen. Hillary Clinton and Sen. Harry Reid.

Sergei Rachmaninoff's Portraits from the collection of the State Central Museum of Musical Culture (Glinka Museum)

Signatur des Originals: S 36/F12277

Sergei Rachmaninoff's Portraits from the collection of the State Central Museum of Musical Culture (Glinka Museum)

Signatur des Originals: S 36/F12278

Sergei Rachmaninoff's Portraits from the collection of the State Central Museum of Musical Culture (Glinka Museum)

Signatur des Originals: S 36/F12279

Sergei Rachmaninoff's Portraits from the collection of the State Central Museum of Musical Culture (Glinka Museum)

Signatur des Originals: S 36/F12280

Sergei Rachmaninoff's Portraits from the collection of the State Central Museum of Musical Culture (Glinka Museum)

Signatur des Originals: S 36/F12281

Sergei Rachmaninoff's Portraits from the collection of the State Central Museum of Musical Culture (Glinka Museum)

Signatur des Originals: S 36/F12282