Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis

Tulipani, Lindsey J.; Meyer, Brett; Larie, Dale; Solomon, Andrew; McGinnis, Ryan S.

doi:10.1016/j.gaitpost.2020.06.014

Cited by 24 publications

(23 citation statements)

References 35 publications

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“…Inertial measurement units (IMUs) could enable biomechanics and rehabilitation researchers to measure kinematics in a variety of populations, in natural environments and over long durations. From detecting functional improvement in patients post-stroke to monitoring fall-risk in older adults [ 1 ], continuous sensing of kinematics could improve our understanding of human movement pathology by providing many repetitions of a movement in home or community settings, in contrast with the limited number of trials and highly-controlled environment of a laboratory experiment. IMUs could also enable early detection of disease or injury-risk.…”

Section: Introductionmentioning

confidence: 99%

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations

Borno

O’Day

Ibarra

et al. 2022

J NeuroEngineering Rehabil

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Background The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift. Methods We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject’s RMS differences over time. Results IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60–0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (− 0.14–0.17 deg/min). Conclusions Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

show abstract

Section: Introductionmentioning

confidence: 99%

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations

Borno

O’Day

Ibarra

et al. 2022

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…Inertial measurement units (IMUs) could enable biomechanics and rehabilitation researchers to measure kinematics in a variety of populations, in natural environments and over long durations. From detecting functional improvement in patients post-stroke to monitoring fallrisk in older adults (1), continuous sensing of kinematics could improve our understanding of human movement pathology by providing many repetitions of a movement in home or community settings, in contrast with the limited number of trials and highly-controlled environment of a laboratory experiment. IMUs could also enable the early detection of disease or injury-risk.…”

Section: Introductionmentioning

confidence: 99%

OpenSense: An open-source toolbox for Inertial-Measurement-Unit-based measurement of lower extremity kinematics over long durations

O’Day

Ibarra³

et al. 2021

Preprint

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Background: The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift. Methods: We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject's RMS differences over time. Results: IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3-6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60 to 0.87). We observed minimal drift in the RMS differences over ten minutes; the average slopes of the linear fits to these data were near zero (-0.14 to 0.17 deg/min). Conclusions: Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, obviating the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

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“…In recent years, many scholars have begun to use objective instruments to assess the risk of falls in older adults. These include a triaxial accelerometer and inertial sensor system 17,[33][34][35] , fall risk assessment based on a pressure platform 15,[36][37][38] , gait acquisition system based on marker points 18 , and gait acquisition systems without marking points 20,39 . This study attempted to use a gait analysis instrument to collect objective data and study its reliability, providing a new method for the clinical risk assessment of falls.…”

Section: Discussionmentioning

confidence: 99%

A new approach for fall risk assessment in hospitalized older adults based on gait data

Xie

Liu

Huang

et al. 2022

Preprint

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Background:Falling in older adults is one of the most common and serious problems leading to disability. Therefore, it is necessary to assess the risk of falls in older adults and take preventive measures in advance. The traditional risk assessment depends on the scale, which may be affected by the subjective factors of patients. However, in recent years, instruments have been developed to collect objective data related to gait in older adults.The aim of this study was to use objective gait data to predict fall risk in older adults. Methods:In this study, a total of 207 hospitalized older adults were recruited, and the Morse Fall Scale (MFS) and six-degrees-of-freedom (6-DOF) gait kinematic parameters of the lower limb joints were collected using a marker-based instrument. Based on the gait data, two important tasks in fall risk assessment were conducted, analysis of abnormal gait patterns and risk level classification. There were three fall risk levels corresponding to the scale, and an end-to-end attention-based convolution model was proposed to analyze gait kinematic data. Results: The model achieved an accuracy score of 0.878 and a recall score of 0.897 on the test set. In addition, we applied an attention-based heatmap to visualize the input data and features across the model. The color bars in the heatmap highly correlate with the level of fall risk and can serve as an indicator of the abnormal gait pattern. Conclusions: An end-to-end attention-based convolution model achieved a favorable result.Besides, the heatmap could serve as the indicator of risk level for each step and also provide further clues to the mechanism of falling. It has the potential to assist doctors in clinical work and contribute to further knowledge discovery.

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Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis

Cited by 24 publications

References 35 publications

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations

OpenSense: An open-source toolbox for Inertial-Measurement-Unit-based measurement of lower extremity kinematics over long durations

A new approach for fall risk assessment in hospitalized older adults based on gait data

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