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Accuracy of a custom physical activity and knee angle measurement sensor system for patients with neuromuscular disorders and gait abnormalities.

Feldhege F, Mau-Moeller A, Lindner T, Hein A, Markschies A, Zettl UK, Bader R - Sensors (Basel) (2015)

Bottom Line: Step detection with our sensor system was more accurate.The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer.The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

View Article: PubMed Central - PubMed

Affiliation: Department of Orthopaedics, University Medicine Rostock, Doberaner Str. 142, 18057 Rostock, Germany. frank.feldhege@med.uni-rostock.de.

ABSTRACT
Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

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Related in: MedlinePlus

Difference of summarized activity time for our algorithm and the activPAL system compared to video data, shown as Bland-Altman plot (o: healthy subjects, X: MS patients, black line: mean error, gray line: mean error ± 1.96 SD).
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sensors-15-10734-f004: Difference of summarized activity time for our algorithm and the activPAL system compared to video data, shown as Bland-Altman plot (o: healthy subjects, X: MS patients, black line: mean error, gray line: mean error ± 1.96 SD).

Mentions: The boxplots in Figure 3 show the total durations for each activity class and the step count from both monitoring systems normalized to ground truth data without regard to chronological sequence. Outliers and extreme values were excluded from further analysis. The Bland-Altman plots in Figure 4 demonstrate the percentage difference between the recognized activity duration and the step count data from the video annotation data. Mean and SD values are listed in Table 5. While the total walking time and summarized step count were underestimated by both systems, our algorithm showed a smaller deviation from the ground truth data for step counts.


Accuracy of a custom physical activity and knee angle measurement sensor system for patients with neuromuscular disorders and gait abnormalities.

Feldhege F, Mau-Moeller A, Lindner T, Hein A, Markschies A, Zettl UK, Bader R - Sensors (Basel) (2015)

Difference of summarized activity time for our algorithm and the activPAL system compared to video data, shown as Bland-Altman plot (o: healthy subjects, X: MS patients, black line: mean error, gray line: mean error ± 1.96 SD).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482003&req=5

sensors-15-10734-f004: Difference of summarized activity time for our algorithm and the activPAL system compared to video data, shown as Bland-Altman plot (o: healthy subjects, X: MS patients, black line: mean error, gray line: mean error ± 1.96 SD).
Mentions: The boxplots in Figure 3 show the total durations for each activity class and the step count from both monitoring systems normalized to ground truth data without regard to chronological sequence. Outliers and extreme values were excluded from further analysis. The Bland-Altman plots in Figure 4 demonstrate the percentage difference between the recognized activity duration and the step count data from the video annotation data. Mean and SD values are listed in Table 5. While the total walking time and summarized step count were underestimated by both systems, our algorithm showed a smaller deviation from the ground truth data for step counts.

Bottom Line: Step detection with our sensor system was more accurate.The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer.The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

View Article: PubMed Central - PubMed

Affiliation: Department of Orthopaedics, University Medicine Rostock, Doberaner Str. 142, 18057 Rostock, Germany. frank.feldhege@med.uni-rostock.de.

ABSTRACT
Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

Show MeSH
Related in: MedlinePlus