9 research outputs found
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
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 3-16
years of age underwent eight walking/running activities, including five 25
meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1
to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run
(100MRW), and a free walk (FW). For clinical anchoring purposes, participants
completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial
gait clinical features (CFs) and applied multiple machine learning (ML)
approaches to differentiate between DMD and TD children using extracted
temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed
reduced step length and a greater mediolateral component of total power (TP)
consistent with shorter strides and Trendelenberg-like gait commonly observed
in DMD. ML approaches using temporospatial gait CFs and raw data varied in
effectiveness at differentiating between DMD and TD controls at different
speeds, with an accuracy of up to 100%. We demonstrate that by using ML with
accelerometer data from a consumer-grade smartphone, we can capture
DMD-associated gait characteristics in toddlers to teens
Percent-Predicted 6-Minute Walk Distance in Duchenne Muscular Dystrophy to Account for Maturational Influences
We recently described a modified version of the 6-minute walk test (6MWT) for Duchenne muscular dystrophy (DMD) based partly on the American Thoracic Society (ATS) guidelines. This measure has shown reliability, validity and utility as a primary outcome measure in DMD clinical trials. Because loss of muscle function in DMD occurs against the background of normal childhood growth and development, younger children with DMD can show increase in distance walked during 6MWT over ~1 year despite progressive muscular impairment. In this study, we compare 6-minute walk distance (6MWD) data from DMD boys (n=17) and typically developing control subjects (n=22) to existing normative data from age- and sex-matched children and adolescents. An age- and height-based equation fitted to normative data by Geiger and colleagues was used to convert 6MWD to a percent-predicted (%-predicted) value in boys with DMD. Analysis of %-predicted 6MWD data represents a method to account for normal growth and development, and shows that gains in function at early ages represents stable rather than improving abilities in boys with DMD. Boys with DMD from 4-7 years of age maintain a stable 6MWD approximately 80% of that of typically developing peers, with the deficit progressing at a variable rate thereafter
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Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach.
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. 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 participants 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 bodys 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. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearsons r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individuals stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers
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(-)-Epicatechin induces mitochondrial biogenesis and markers of muscle regeneration in adults with Becker muscular dystrophy.
INTRODUCTION: We conducted an open-label study to examine the effects of the flavonoid (-)-epicatechin in seven ambulatory adult patients with Becker muscular dystrophy (BMD). METHODS: Seven participants received (-)-epicatechin 50 mg twice per day for 8 weeks. Pre- and postprocedures included biceps brachii biopsy to assess muscle structure and growth-relevant endpoints by western blotting, mitochondria volume measurement, and cristae abundance by electron microscopy, graded exercise testing, and muscle strength and function tests. RESULTS: Western blotting showed significantly increased levels of enzymes modulating cellular bioenergetics (liver kinase B1 and 5-adenosine monophosphate-activated protein kinase). Peroxisome proliferator-activated receptor gamma coactivator-1alpha, a transcriptional coactivator of genes involved in mitochondrial biogenesis and cristae-associated mitofilin levels, increased as did cristae abundance. Muscle and plasma follistatin increased significantly while myostatin decreased. Markers of skeletal muscle regeneration myogenin, myogenic regulatory factor-5, myoblast determination protein 1, myocyte enhancer factor-2, and structure-associated proteins, including dysferlin, utrophin, and intracellular creatine kinase, also increased. Exercise testing demonstrated decreased heart rate, maximal oxygen consumption per kilogram, and plasma lactate levels at defined workloads. Tissue saturation index improved in resting and postexercise states. DISCUSSION: (-)-Epicatechin, an exercise mimetic, appears to have short-term positive effects on tissue biomarkers indicative of mitochondrial biogenesis and muscle regeneration, and produced improvements in graded exercise testing parameters in patients with BMD
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Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches.
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). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens
Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach
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. 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. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson’s r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual’s stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers
Automated Detection of Gait Events and Travel Distance Using Waist-worn Accelerometers Across a Typical Range of Walking and Running Speeds
Background: 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, challenges arising from
device complexity and availability, cost and analytical methodology have
limited widespread application of such tools. Research Question: Can
accelerometer data from commercially-available smartphones be used to extract
gait CFs across a broad range of attainable gait velocities in children with
Duchenne muscular dystrophy (DMD) and typically developing controls (TDs) using
machine learning (ML)-based methods Methods: Fifteen children with DMD and 15
TDs underwent supervised clinical testing across a range of gait speeds using
10 or 25m run/walk (10MRW, 25MRW), 100m run/walk (100MRW), 6-minute walk (6MWT)
and free-walk (FW) evaluations while wearing a mobile phone-based accelerometer
at the waist near the body's center of mass. Gait CFs were extracted from the
accelerometer data using a multi-step machine learning-based process and
results were compared to ground-truth observation data. Results: Model
predictions vs. observed values for step counts, distance traveled, and step
length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p<0.0001).
The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for
step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step
length compared to ground truth observations for the combined 6MWT, 100MRW, and
FW tasks. Significance: The study findings indicate that a single accelerometer
placed near the body's center of mass can accurately measure CFs across
different gait speeds in both TD and DMD peers, suggesting that there is
potential for accurately measuring CFs in the community with consumer-level
smartphones
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
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). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens