Validation of a Wearable System for Lower Extremity Assessment
Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haohua Zhang [verfasserIn] Yang Song [verfasserIn] Cheng Li [verfasserIn] Yong Dou [verfasserIn] Dacheng Wang [verfasserIn] Yinyue Wu [verfasserIn] Xiaoyi Chen [verfasserIn] Di Liu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Orthopaedic Surgery - Wiley, 2019, 15(2023), 11, Seite 2911-2917 |
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| Übergeordnetes Werk: |
volume:15 ; year:2023 ; number:11 ; pages:2911-2917 |
| Links: |
Link aufrufen |
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| DOI / URN: |
10.1111/os.13836 |
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| Katalog-ID: |
DOAJ096912731 |
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| 520 | |a Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. | ||
| 650 | 4 | |a Gait Analysis | |
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| 700 | 0 | |a Di Liu |e verfasserin |4 aut | |
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10.1111/os.13836 doi (DE-627)DOAJ096912731 (DE-599)DOAJ04cb28e693fa4fadb403d6d7af4fbf1d DE-627 ger DE-627 rakwb eng RD701-811 Haohua Zhang verfasserin aut Validation of a Wearable System for Lower Extremity Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. Gait Analysis Knee; Osteoarthritis Remote Monitor Orthopedic surgery Yang Song verfasserin aut Cheng Li verfasserin aut Yong Dou verfasserin aut Dacheng Wang verfasserin aut Yinyue Wu verfasserin aut Xiaoyi Chen verfasserin aut Di Liu verfasserin aut In Orthopaedic Surgery Wiley, 2019 15(2023), 11, Seite 2911-2917 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:15 year:2023 number:11 pages:2911-2917 https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/article/04cb28e693fa4fadb403d6d7af4fbf1d kostenfrei https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11 2911-2917 |
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10.1111/os.13836 doi (DE-627)DOAJ096912731 (DE-599)DOAJ04cb28e693fa4fadb403d6d7af4fbf1d DE-627 ger DE-627 rakwb eng RD701-811 Haohua Zhang verfasserin aut Validation of a Wearable System for Lower Extremity Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. Gait Analysis Knee; Osteoarthritis Remote Monitor Orthopedic surgery Yang Song verfasserin aut Cheng Li verfasserin aut Yong Dou verfasserin aut Dacheng Wang verfasserin aut Yinyue Wu verfasserin aut Xiaoyi Chen verfasserin aut Di Liu verfasserin aut In Orthopaedic Surgery Wiley, 2019 15(2023), 11, Seite 2911-2917 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:15 year:2023 number:11 pages:2911-2917 https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/article/04cb28e693fa4fadb403d6d7af4fbf1d kostenfrei https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11 2911-2917 |
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10.1111/os.13836 doi (DE-627)DOAJ096912731 (DE-599)DOAJ04cb28e693fa4fadb403d6d7af4fbf1d DE-627 ger DE-627 rakwb eng RD701-811 Haohua Zhang verfasserin aut Validation of a Wearable System for Lower Extremity Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. Gait Analysis Knee; Osteoarthritis Remote Monitor Orthopedic surgery Yang Song verfasserin aut Cheng Li verfasserin aut Yong Dou verfasserin aut Dacheng Wang verfasserin aut Yinyue Wu verfasserin aut Xiaoyi Chen verfasserin aut Di Liu verfasserin aut In Orthopaedic Surgery Wiley, 2019 15(2023), 11, Seite 2911-2917 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:15 year:2023 number:11 pages:2911-2917 https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/article/04cb28e693fa4fadb403d6d7af4fbf1d kostenfrei https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11 2911-2917 |
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10.1111/os.13836 doi (DE-627)DOAJ096912731 (DE-599)DOAJ04cb28e693fa4fadb403d6d7af4fbf1d DE-627 ger DE-627 rakwb eng RD701-811 Haohua Zhang verfasserin aut Validation of a Wearable System for Lower Extremity Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. Gait Analysis Knee; Osteoarthritis Remote Monitor Orthopedic surgery Yang Song verfasserin aut Cheng Li verfasserin aut Yong Dou verfasserin aut Dacheng Wang verfasserin aut Yinyue Wu verfasserin aut Xiaoyi Chen verfasserin aut Di Liu verfasserin aut In Orthopaedic Surgery Wiley, 2019 15(2023), 11, Seite 2911-2917 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:15 year:2023 number:11 pages:2911-2917 https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/article/04cb28e693fa4fadb403d6d7af4fbf1d kostenfrei https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11 2911-2917 |
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10.1111/os.13836 doi (DE-627)DOAJ096912731 (DE-599)DOAJ04cb28e693fa4fadb403d6d7af4fbf1d DE-627 ger DE-627 rakwb eng RD701-811 Haohua Zhang verfasserin aut Validation of a Wearable System for Lower Extremity Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. Gait Analysis Knee; Osteoarthritis Remote Monitor Orthopedic surgery Yang Song verfasserin aut Cheng Li verfasserin aut Yong Dou verfasserin aut Dacheng Wang verfasserin aut Yinyue Wu verfasserin aut Xiaoyi Chen verfasserin aut Di Liu verfasserin aut In Orthopaedic Surgery Wiley, 2019 15(2023), 11, Seite 2911-2917 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:15 year:2023 number:11 pages:2911-2917 https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/article/04cb28e693fa4fadb403d6d7af4fbf1d kostenfrei https://doi.org/10.1111/os.13836 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 11 2911-2917 |
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This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. 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Haohua Zhang |
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Haohua Zhang misc RD701-811 misc Gait Analysis misc Knee; Osteoarthritis misc Remote Monitor misc Orthopedic surgery Validation of a Wearable System for Lower Extremity Assessment |
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validation of a wearable system for lower extremity assessment |
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Validation of a Wearable System for Lower Extremity Assessment |
| abstract |
Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. |
| abstractGer |
Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. |
| abstract_unstemmed |
Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA. |
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2024-07-03T22:55:35.814Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ096912731</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413164206.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/os.13836</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096912731</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ04cb28e693fa4fadb403d6d7af4fbf1d</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RD701-811</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Haohua Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Validation of a Wearable System for Lower Extremity Assessment</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objective Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients’ functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor‐based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold‐standard motion capture and gait measurement systems. Methods Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor‐based wearable system, a gold‐standard motion capture system, and a pressure‐based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t‐test was conducted for inter‐group comparison to analyze the data. Results The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold‐standard pressure‐based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold‐standard video‐based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. Conclusions This IMU‐based wearable system delivered precise measuring results to evaluate patients with knee OA. 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