5 research outputs found
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
With the rapid development of the internet of things (IoT) and artificial
intelligence (AI) technologies, human activity recognition (HAR) has been
applied in a variety of domains such as security and surveillance, human-robot
interaction, and entertainment. Even though a number of surveys and review
papers have been published, there is a lack of HAR overview papers focusing on
healthcare applications that use wearable sensors. Therefore, we fill in the
gap by presenting this overview paper. In particular, we present our projects
to illustrate the system design of HAR applications for healthcare. Our
projects include early mobility identification of human activities for
intensive care unit (ICU) patients and gait analysis of Duchenne muscular
dystrophy (DMD) patients. We cover essential components of designing HAR
systems including sensor factors (e.g., type, number, and placement location),
AI model selection (e.g., classical machine learning models versus deep
learning models), and feature engineering. In addition, we highlight the
challenges of such healthcare-oriented HAR systems and propose several research
opportunities for both the medical and the computer science community
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
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