Wearable sensors objectively measure gait parameters in Parkinson’s disease

• Published in: PloS one Vol. 12; no. 10; p. e0183989
• Main Authors: Schlachetzki, Johannes C. M., Barth, Jens, Marxreiter, Franz, Gossler, Julia, Kohl, Zacharias, Reinfelder, Samuel, Gassner, Heiko, Aminian, Kamiar, Eskofier, Bjoern M., Winkler, Jürgen, Klucken, Jochen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.10.2017; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Aged, 80 and over; Analysis; Biology and Life Sciences; Biomechanical engineering; Biomechanics; Care and treatment; Case-Control Studies; Computer science; Cross-sectional studies; Diagnosis; Diagnostic software; Diagnostic systems; Disease control; Feasibility studies; Female; Fractals; Gait; Hospitals; Humans; Impairment; Inertial sensing devices; Laboratories; Longitudinal Studies; Male; Management; Measurement; Medical care; Medicine and Health Sciences; Middle Aged; Monitoring, Physiologic - instrumentation; Neurology; Parameter sensitivity; Parkinson disease; Parkinson Disease - physiopathology; Parkinson's disease; Patients; Pattern recognition; People and Places; Physical Sciences; Postural Balance; Posture; Research and Analysis Methods; Sensors; Stability; Time Factors; Toe; Wearable technology; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0183989

Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson's disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson's disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson's disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects' preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson's disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson's disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson's disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson's disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.