Competitive Live Evaluation of Activity-recognition Systems

In order to ensure the validity and usability of activity recognition approaches, an agreement on a set of standard evaluation methods is needed. Due to the diversity of the sensors and other hardware employed, designing and accepting standard tests is a difficult task. This article presents an initiative to evaluate activity recognition systems: a living-lab evaluation established through an annual competition − EvAAL-AR (Evaluating Ambient Assisted Living Systems through Competitive Benchmarking − Activity Recognition). In the competition, each team brings their own activity-recognition system, which is evaluated live on the same activity scenario performed by an actor. The evaluation criteria attempt to capture the practical usability: recognition accuracy, user acceptance, recognition delay, installation complexity, and interoperability with ambient assisted living systems. The article also presents the competing systems with emphasis on two best-performing ones: (i) a system that achieved the best recognition accuracy, and (ii) a system that was evaluated as the best overall. Finally, the article presents lessons learned from the competition and ideas for future development of the competition and of the activity recognition field in general

Ultrafast disordering of vanadium dimers in photoexcited VO2

Time-resolved x-ray scattering can be used to investigate the dynamics of materials during the switch from one structural phase to another. So far, methods provide an ensemble average and may miss crucial aspects of the detailed mechanisms at play. Wall et al. used a total-scattering technique to probe the dynamics of the ultrafast insulator-to-metal transition of vanadium dioxide (VO2) (see the Perspective by Cavalleri). Femtosecond x-ray pulses provide access to the time- and momentum-resolved dynamics of the structural transition. Their results show that the photoinduced transition is of the order-disorder type, driven by an ultrafast change in the lattice potential that suddenly unlocks the vanadium atoms and yields large-amplitude uncorrelated motions, rather than occurring through a coherent displacive mechanism.Peer ReviewedPostprint (author's final draft

Fall Detection with Unobtrusive Infrared Array Sensors

As the world’s aging population grows, fall is becoming a major problem in public health. It is one of the most vital risks to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor

Evacetrapib and Cardiovascular Outcomes in High-Risk Vascular Disease

BACKGROUND: The cholesteryl ester transfer protein inhibitor evacetrapib substantially raises the high-density lipoprotein (HDL) cholesterol level, reduces the low-density lipoprotein (LDL) cholesterol level, and enhances cellular cholesterol efflux capacity. We sought to determine the effect of evacetrapib on major adverse cardiovascular outcomes in patients with high-risk vascular disease. METHODS: In a multicenter, randomized, double-blind, placebo-controlled phase 3 trial, we enrolled 12,092 patients who had at least one of the following conditions: an acute coronary syndrome within the previous 30 to 365 days, cerebrovascular atherosclerotic disease, peripheral vascular arterial disease, or diabetes mellitus with coronary artery disease. Patients were randomly assigned to receive either evacetrapib at a dose of 130 mg or matching placebo, administered daily, in addition to standard medical therapy. The primary efficacy end point was the first occurrence of any component of the composite of death from cardiovascular causes, myocardial infarction, stroke, coronary revascularization, or hospitalization for unstable angina. RESULTS: At 3 months, a 31.1% decrease in the mean LDL cholesterol level was observed with evacetrapib versus a 6.0% increase with placebo, and a 133.2% increase in the mean HDL cholesterol level was seen with evacetrapib versus a 1.6% increase with placebo. After 1363 of the planned 1670 primary end-point events had occurred, the data and safety monitoring board recommended that the trial be terminated early because of a lack of efficacy. After a median of 26 months of evacetrapib or placebo, a primary end-point event occurred in 12.9% of the patients in the evacetrapib group and in 12.8% of those in the placebo group (hazard ratio, 1.01; 95% confidence interval, 0.91 to 1.11; P=0.91). CONCLUSIONS: Although the cholesteryl ester transfer protein inhibitor evacetrapib had favorable effects on established lipid biomarkers, treatment with evacetrapib did not result in a lower rate of cardiovascular events than placebo among patients with high-risk vascular disease. (Funded by Eli Lilly; ACCELERATE ClinicalTrials.gov number, NCT01687998 .)

Development of a system for monitoring changes in human gait

On the market, there are more and more di®erent systems for monitoring elderly people. Most of the systems are expensive and inaccessible since they use very sophisticated monitoring equipment. Basic functions of monitoring could be done by reasonably priced sensors such as accelerometers. The object of this work was to develop the system for monitoring changes in human gait. We used data from accelerometers attached to the left and right ankle. Accelerations are represented in three-dimensional coordinate system. We realized the best solution to our problem was to divide data set into ¯xed time intervals. Without this assumption it would have been di±cult to deter- mine the right attributes for machine learning. To better understand the data in one interval, we divided walking into its primitive elements - steps. By using some gait characteristics we were able to conceive two algorithms. Together they enabled us to divide data into single steps. Eight di®erent attributes distinctive for walking were chosen. The next thing was an attempt to create universal training data set that could recognize the di®erence between normal walking and limping. Testing was done on di®erent time intervals. It proved successful to divide data into longer time intervals. By doing so we got better information about gait in one time interval. SVM proved to be the optimal machine learning model for the presented classi¯cation problem

Development of a system for monitoring changes in human gait

Na tržišču je vse več sistemov za oddaljeno nadzorovanje starejših ljudi. Večina teh sistemov je dragih in ljudem nedostopnih, saj uporabljajo zelo napredno opremo za nadzor. Osnovne funkcije nadzora bi lahko opravljali s cenovno dostopnimi senzorji, kot so pospeškomeri. Namen diplomskega dela je bila izdelava sistema za nadzor sprememb v hoji človeka. Pri delu smo uporabljali pospeške leve in desne noge v vseh treh koordinatnih smereh. Ugotovili smo, da je najboljši pristop k rešitvi problema delitev naše množice podatkov na fiksne časovne intervale. Brez te predpostavke bi težko določili pravilne atribute potrebne za strojno učenje. Da bi bolje razumeli podatke enega časovnega intervala, smo hojo razdelili na njene primitivne elemente - korake. S pomočjo splošne karakteristike hoje smo zasnovali dva algoritma. Skupaj nam razdelita podatke na posamezne korake. Izbrali smo osem različnih atributov značilnih za hojo. Nato smo poskusili ustvariti univerzalni model strojnega učenja, kateri bi prepoznal razlike med normalno hojo in šepanjem. Testiranje smo opravili na različnih časovnih intervalih. Izkazalo se je, da je podatke bolje razrezati na daljše množice. Tako dobimo bolj zanesljive informacije o hoji človeka na obravnavanem intervalu. Ugotovili smo, da je SVM najboljši model strojnega učenja za predstavljen klasifikacijski problem.On the market, there are more and more di®erent systems for monitoring elderly people. Most of the systems are expensive and inaccessible since they use very sophisticated monitoring equipment. Basic functions of monitoring could be done by reasonably priced sensors such as accelerometers. The object of this work was to develop the system for monitoring changes in human gait. We used data from accelerometers attached to the left and right ankle. Accelerations are represented in three-dimensional coordinate system. We realized the best solution to our problem was to divide data set into ¯xed time intervals. Without this assumption it would have been di±cult to deter- mine the right attributes for machine learning. To better understand the data in one interval, we divided walking into its primitive elements - steps. By using some gait characteristics we were able to conceive two algorithms. Together they enabled us to divide data into single steps. Eight di®erent attributes distinctive for walking were chosen. The next thing was an attempt to create universal training data set that could recognize the di®erence between normal walking and limping. Testing was done on di®erent time intervals. It proved successful to divide data into longer time intervals. By doing so we got better information about gait in one time interval. SVM proved to be the optimal machine learning model for the presented classi¯cation problem