A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis

• Published in: PloS one Vol. 12; no. 6; p. e0178366
• Main Authors: McGinnis, Ryan S, Mahadevan, Nikhil, Moon, Yaejin, Seagers, Kirsten, Sheth, Nirav, Wright, Jr, John A, DiCristofaro, Steven, Silva, Ikaro, Jortberg, Elise, Ceruolo, Melissa, Pindado, Jesus A, Sosnoff, Jacob, Ghaffari, Roozbeh, Patel, Shyamal
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2017; Public Library of Science (PLoS)
• Subjects: Accelerometers; Adult; Arrays; Artificial intelligence; Assessments; Aversion learning; Biology and Life Sciences; Biomedical engineering; Biosensing Techniques; Caregivers; Case-Control Studies; Computer and Information Sciences; Constraints; Correlation analysis; Design analysis; Design engineering; Disorders; Ecological monitoring; Engineering and Technology; Error analysis; Estimation; Estimation errors; Female; Gait; Ground truth; Humans; Impairment; International conferences; Joint surgery; Kinematics; Laboratories; Learning algorithms; Machine Learning; Male; Medicine and Health Sciences; Mobility; Monitoring; Multiple sclerosis; Multiple Sclerosis - physiopathology; Neurological diseases; Neurological disorders; Patients; Physical Sciences; Posture; Quality of life; Sensors; Serial learning; Skin; Speed estimation; Studies; Velocity; Walking; Wearable technology; Young Adult; United States > US; Illinois; Massachusetts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178366

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.