Wearable sensors objectively measure gait parameters in Parkinson's disease
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 meas...
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| creator | 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 |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0183989 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0183989</identifier><identifier>PMID: 29020012</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2017-10, Vol.12 (10), p.e0183989-e0183989</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Schlachetzki et al. 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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.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Biomechanical engineering</subject><subject>Biomechanics</subject><subject>Care and treatment</subject><subject>Case-Control Studies</subject><subject>Computer science</subject><subject>Cross-sectional studies</subject><subject>Diagnosis</subject><subject>Diagnostic software</subject><subject>Diagnostic systems</subject><subject>Disease control</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Fractals</subject><subject>Gait</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Impairment</subject><subject>Inertial sensing 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sensors objectively measure gait parameters in Parkinson's disease</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-a170de3cf2b65853928ff1fb43cf06d1391ea34925233111071bf5e0ce3c94ac3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Biomechanical engineering</topic><topic>Biomechanics</topic><topic>Care and treatment</topic><topic>Case-Control Studies</topic><topic>Computer science</topic><topic>Cross-sectional studies</topic><topic>Diagnosis</topic><topic>Diagnostic software</topic><topic>Diagnostic 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one</jtitle><addtitle>PLoS One</addtitle><date>2017-10-11</date><risdate>2017</risdate><volume>12</volume><issue>10</issue><spage>e0183989</spage><epage>e0183989</epage><pages>e0183989-e0183989</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29020012</pmid><doi>10.1371/journal.pone.0183989</doi><tpages>e0183989</tpages><orcidid>https://orcid.org/0000-0002-7801-9743</orcidid><oa>free_for_read</oa></addata></record> |
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| issn | 1932-6203 1932-6203 |
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| 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 |
| title | Wearable sensors objectively measure gait parameters in Parkinson's disease |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T16%3A35%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Wearable%20sensors%20objectively%20measure%20gait%20parameters%20in%20Parkinson's%20disease&rft.jtitle=PloS%20one&rft.au=Schlachetzki,%20Johannes%20C%20M&rft.date=2017-10-11&rft.volume=12&rft.issue=10&rft.spage=e0183989&rft.epage=e0183989&rft.pages=e0183989-e0183989&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0183989&rft_dat=%3Cgale_plos_%3EA509130642%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1950120278&rft_id=info:pmid/29020012&rft_galeid=A509130642&rft_doaj_id=oai_doaj_org_article_83d1b789453f4ea88897e357f0733732&rfr_iscdi=true |