Inertial Measurement Unit-Based Estimation of Foot Trajectory for Clinical Gait Analysis

Hori, Kentaro; Mao, Yufeng; Ono, Yoshiyasu; Ora, Hiroki; Hirobe, Yuki; Sawada, Hiroyuki; Inaba, Akira; Orimo, Satoshi; Miyake, Yoichi

doi:10.3389/fphys.2019.01530

Cited by 53 publications

(73 citation statements)

References 21 publications

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“…For example, the stride lengths were estimated using the principle of double integration and an error state Kalman filter. Similar trends were also found in other studies on IMU gait analysis [ 37 ]. Issues with calibration could also cause inaccurate analysis results, as data from IMUs that are known to require custom calibration (MMR, Portabiles, and Shimmer) generally produced less accurate stride length estimations.…”

Section: Discussionsupporting

confidence: 91%

“…Apart from the type of activities, the requirement of sampling rate also depends on the type of analysis. For example, classification of activities usually requires lower sampling rates compared to high precision movement trajectory reconstruction [36,37]. Using a sampling rate higher than necessary will increase the computation load without improving the results.…”

Section: Proposed Guidelines For Imu Device Selectionmentioning

confidence: 99%

“…The sampling rates were selected based on the available options that are closest to 100 Hz for each IMU. This sampling rate is sufficient for most types of human motion tracking, and is commonly used for human gait analysis studies which are similar to the example use case [32,37]. The IMUs were configured to record with accelerometer and gyroscope using their mobile apps whenever possible.…”

Section: Device Inclusion/exclusion Criteriamentioning

confidence: 99%

See 2 more Smart Citations

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

Zhou

Fischer

Tunca

et al. 2020

Sensors

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Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.

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Section: Discussionsupporting

confidence: 91%

Section: Proposed Guidelines For Imu Device Selectionmentioning

confidence: 99%

Section: Device Inclusion/exclusion Criteriamentioning

confidence: 99%

See 1 more Smart Citation

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

Zhou

Fischer

Tunca

et al. 2020

Sensors

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“…Since studies measured different populations and gait paramters, and various metrics could be used for evaluation of the gait parameters, one-to-one comparisons between studies are not always possible. As examples of comparable results for young healthy subjects, Yeo et al reported average LoA of 0.08 m for stride length and 0.08 s for stride time [16], and Hori et al reported correlation coefficient of 0.98 for stride length and 0.99 for stride time [22].…”

Section: Resultsmentioning

confidence: 88%

Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations

Zhou

Tunca

Fischer

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person's cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life, individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.

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“…Stride length, velocity, and CV of stride duration were estimated using the IMU sensors to evaluate the lower limb movements (Hori et al, 2020 ). IMU sensors (TSND121, ATR-Promotions) were equipped on both of the participant's shanks.…”

Section: Methodsmentioning

confidence: 99%

Synchronized Tactile Stimulation on Upper Limbs Using a Wearable Robot for Gait Assistance in Patients With Parkinson's Disease

et al. 2020

Self Cite

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This study aimed to investigate whether using a wearable robot applying interactive rhythmic stimulation on the upper limbs of patients with Parkinson's disease (PD) could affect their gait. The wearable robot presented tactile stimuli on the patients' upper limbs, which was mutually synchronized with the swing of their upper limbs. We conducted an evaluation experiment with PD patients (n = 30, Modified Hoehn-Yahr = 1-3, on-state) to investigate the assistance effect by the robot and the immediate after-effect of intervention. The participants were instructed to walk 30 m under four different conditions: (1) not wearing the robot before the intervention (Pre-condition), (2) wearing the robot without the rhythm assistance (RwoA condition), (3) wearing the robot with rhythm assistance (RwA condition), and (4) not wearing the robot immediately after the intervention (Post-condition). These conditions were conducted in this order over a single day. The third condition was performed three times and the others, once. The arm swing amplitude, stride length, and velocity were increased in the RwA condition compared to the RwoA condition. The coefficient of variance (CV) of the stride duration was decreased in the RwA condition compared to the RwoA condition. These results revealed that the assistance by the robot increased the gait performance of PD patients. In addition, the stride length and velocity were increased and the stride duration CV was decreased in the Post-condition compared to the Pre-condition. These results show that the effect of robot assistance on the patient's gait remained immediately after the intervention. These findings suggest that synchronized rhythmic stimulation on the upper limbs could influence the gait of PD patients and that the robot may assist with gait rehabilitation in these patients.

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Inertial Measurement Unit-Based Estimation of Foot Trajectory for Clinical Gait Analysis

Cited by 53 publications

References 21 publications

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations

Synchronized Tactile Stimulation on Upper Limbs Using a Wearable Robot for Gait Assistance in Patients With Parkinson's Disease

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