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JP Onnela wins NIH Director’s New Innovator Award

first_imgJukka-Pekka “JP” Onnela, assistant professor of biostatistics at Harvard School of Public Health, has won a prestigious Director’s New Innovator Award from the National Institutes of Health (NIH) for a proposal to collect and analyze cell phone communication and sensor data to monitor social and behavioral functioning of individuals with mood disorders. One of 41 scientists around the country receiving the award in 2013, Onnela will receive $1.5 million over the course of five years.“I have wanted to do this particular project for many years, but it has taken time for the technology to mature,” Onnela said. “The final missing piece was funding, and I am extremely grateful and deeply honored to receive this award from the NIH.”The New Innovator Award is part of the NIH’s High Risk-High Reward program, which provides support for exceptional innovation in biomedical research. These awards are given to early-stage investigators working on highly creative research approaches that may be at too early a stage to qualify for more traditional NIH funding, but which have the potential to produce a major impact on broad, important problems in biomedical and behavioral research. Read Full Storylast_img read more

Gallery: Syracuse bulldozes Central Connecticut State, 50-7, in season-opening victory

first_img Facebook Twitter Google+ UPDATED: Sept. 10, 2017 at 10:49 p.m.Syracuse (1-0) mauled Central Connecticut State (0-1), 50–7, in the Carrier Dome on Friday night in front of an announced 30,273, shifting into cruise control after its first three possessions.  Here are the best images from the game. Comments Published on September 1, 2017 at 11:50 pmlast_img

ST: motion sensor with machine learning for high-accuracy, battery-friendly activity tracking

first_img Continue Reading Previous RTI announces medical-grade connectivity frameworkNext Kontron and Lynx partner on secure connected commercial vehicles STMicroelectronics has integrated machine-learning technology into its advanced inertial sensors to improve activity-tracking performance and battery life in mobiles and wearables. The LSM6DSOX iNEMO sensor contains a machine-learning core to classify motion data based on known patterns. Relieving this first stage of activity tracking from the main processor saves energy and accelerates motion-based apps such as fitness logging, wellness monitoring, personal navigation, and fall detection.Devices equipped with ST’s LSM6DSOX can deliver a convenient and responsive “always-on” user experience without trading battery runtime. The sensor also has more internal memory than conventional sensors, and a state-of-the-art high-speed I3C digital interface, allowing longer periods between interactions with the main controller and shorter connection times for extra energy savings.The sensor is easy to integrate with popular mobile platforms such as Android and iOS, simplifying use in smart devices for consumer, medical, and industrial markets.The LSM6DSOX contains a 3D MEMS accelerometer and 3D MEMS gyroscope, and tracks complex movements using the machine-learning core at low typical current consumption of just 0.55mA to minimize load on the battery.The machine-learning core works in conjunction with the sensor’s integrated finite-state machine logic to handle motion pattern recognition or vibration detection. Customers creating activity-tracking products with the LSM6DSOX can train the core for decision-tree based classification using Weka, an open-source PC-based application, to generate settings and limits from sample data such as acceleration, speed, and magnetic angle that characterize the types of movements to be detected. Support for free-fall, wakeup, 6D/4D orientation, click and double-click interrupts allows a wide variety of applications such as user-interface management and laptop protection in addition to activity tracking. Auxiliary outputs and configuration options also simplify use in optical image stabilization (OIS).Share this:TwitterFacebookLinkedInMoreRedditTumblrPinterestWhatsAppSkypePocketTelegram Tags: Chips & Components last_img read more