Publications by authors named "Daniel Aranki"

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. This paper proposes a novel calibration method.

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Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW).

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Studies have shown that about half of the injuries sustained during long-distance running involve the knee. Cadence (steps per minute) has been identified as a factor that is strongly associated with these running-related injuries, making it a worthwhile candidate for further study. As such, it is critical for long-distance runners to minimize their risk of injury by running at an appropriate running cadence.

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Synopsis of recent research by authors named "Daniel Aranki"

  • - Daniel Aranki's research primarily focuses on the assessment and characterization of gait, particularly in individuals with Duchenne muscular dystrophy (DMD), using wearable sensor technology to measure various gait-related features.
  • - His recent work includes the development of automated calibration methods to accurately estimate temporospatial clinical features of gait and the application of both classical machine learning and deep learning approaches to analyze gait patterns in children with DMD compared to their typically developing peers.
  • - Additionally, Aranki has investigated innovative systems such as RunningCoach, aimed at improving usability and feasibility for remote coaching of long-distance runners, emphasizing injury prevention through optimizing running cadence.