首页|Study Findings on Machine Learning Are Outlined in Reports from University of California (Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach)
Study Findings on Machine Learning Are Outlined in Reports from University of California (Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach)
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Current study results on artificial intelligence have been published. According to news originating from Davis, California, by NewsRx correspondents, research stated, “Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities.” Funders for this research include Us Department of Defense; Muscular Dystrophy Association; University of California Center For Information Technology Research in The Interest of Society (Citris) And The Banatao Institute. The news reporters obtained a quote from the research from University of California: “This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant’s level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body’s center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data.”
University of CaliforniaDavisCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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