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Ramli AA, Liu X, Berndt K, Chuah CN, Goude E, Kaethler LB, Lopez A, Nicorici A, Owens C, Rodriguez D, Wang J, Aranki D, McDonald CM, Henricson EK. Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach. Sensors (Basel) 2024; 24:1155. [PMID: 38400313 PMCID: PMC10891633 DOI: 10.3390/s24041155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/03/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
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.
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Affiliation(s)
- Albara Ah Ramli
- Department of Computer Science, School of Engineering, University of California, 1 Shields Ave, Davis, CA 95616, USA; (A.A.R.); (X.L.)
| | - Xin Liu
- Department of Computer Science, School of Engineering, University of California, 1 Shields Ave, Davis, CA 95616, USA; (A.A.R.); (X.L.)
| | - Kelly Berndt
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Chen-Nee Chuah
- Department of Electrical and Computer Engineering, School of Engineering, University of California, 1 Shields Ave, Davis, CA 95616, USA
| | - Erica Goude
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Lynea B. Kaethler
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Amanda Lopez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Alina Nicorici
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Corey Owens
- UC Davis Center for Health and Technology, School of Medicine, University of California Davis, 1 Shields Ave, Davis, CA 95616, USA;
| | - David Rodriguez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Jane Wang
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Daniel Aranki
- Berkeley School of Information, University of California Berkeley, 1 Shields Ave, Berkeley, CA 94720, USA;
| | - Craig M. McDonald
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Erik K. Henricson
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
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Ramli AA, Liu X, Berndt K, Goude E, Hou J, Kaethler LB, Liu R, Lopez A, Nicorici A, Owens C, Rodriguez D, Wang J, Zhang H, Aranki D, McDonald CM, Henricson EK. Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches. Sensors (Basel) 2024; 24:1123. [PMID: 38400281 PMCID: PMC10892016 DOI: 10.3390/s24041123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
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.
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Affiliation(s)
- Albara Ah Ramli
- Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA; (A.A.R.); (X.L.); (R.L.)
| | - Xin Liu
- Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA; (A.A.R.); (X.L.); (R.L.)
| | - Kelly Berndt
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Erica Goude
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Jiahui Hou
- Department of Electrical and Computer Engineering, School of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Lynea B. Kaethler
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Rex Liu
- Department of Computer Science, School of Engineering, University of California, Davis, CA 95616, USA; (A.A.R.); (X.L.); (R.L.)
| | - Amanda Lopez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Alina Nicorici
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Corey Owens
- UC Davis Center for Health and Technology, University of California, Davis, CA 95616, USA;
| | - David Rodriguez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Jane Wang
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Huanle Zhang
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Daniel Aranki
- Berkeley School of Information, University of California Berkeley, Berkeley, CA 94720, USA;
| | - Craig M. McDonald
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
| | - Erik K. Henricson
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (H.Z.); (C.M.M.)
- Graduate Group in Computer Science (GGCS), University of California, Davis, CA 95616, USA
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