Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units

Zhang, Xingxuan; Zhang, Haojian; Hu, Jianhua; Zheng, Jun; Wang, Xinbo; Deng, Jieren; Wan, Zhongmin; Wang, Haotian; Wang, Yunkuan

doi:10.1109/jsen.2022.3175823

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…Two studies [ 51 , 82 ] only presented results from an experiment without any participants. In four studies [ 52 , 67 , 78 , 81 ], the number, health status, age, and sex of participants were not mentioned. The remaining 43 studies included a total of 631 participants.…”

Section: Resultsmentioning

confidence: 99%

“…In a validation experiment with seven healthy and young participants, they showed that their approach has large deviations, for example, the RMSE ranged between 5.15% (treadmill walking) and 5.53% (level ground walking) compared to an FSR based approach. Also Zhang et al [ 67 ] presented a hip exoskeleton with two IMUs at the thigh to estimate gait phases. Furthermore, Crea et al [ 68 ] carried out a study on their active pelvis orthosis (APO).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

Moeller

Moehler

Krell‐Roesch

et al. 2023

Sensors

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Exoskeletons are a promising tool to support individuals with a decreased level of motor performance. Due to their built-in sensors, exoskeletons offer the possibility of continuously recording and assessing user data, for example, related to motor performance. The aim of this article is to provide an overview of studies that rely on using exoskeletons to measure motor performance. Therefore, we conducted a systematic literature review, following the PRISMA Statement guidelines. A total of 49 studies using lower limb exoskeletons for the assessment of human motor performance were included. Of these, 19 studies were validity studies, and six were reliability studies. We found 33 different exoskeletons; seven can be considered stationary, and 26 were mobile exoskeletons. The majority of the studies measured parameters such as range of motion, muscle strength, gait parameters, spasticity, and proprioception. We conclude that exoskeletons can be used to measure a wide range of motor performance parameters through built-in sensors, and seem to be more objective and specific than manual test procedures. However, since these parameters are usually estimated from built-in sensor data, the quality and specificity of an exoskeleton to assess certain motor performance parameters must be examined before an exoskeleton can be used, for example, in a research or clinical setting.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

Moeller

Moehler

Krell‐Roesch

et al. 2023

Sensors

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show abstract

“…On the other hand, the phase recognition of the human gait is also a hot topic [ 29 ]. Zhang et al proposed a multi-degree-of-freedom gait-detection method based on the kinematic information collected by IMUs on the dorsum of the foot, ankle, and thigh, and pursued the real-time recognition of the five phase features in a gait cycle [ 30 ]. Luo et al identified the swinging and standing phases using a quadratic discriminant analysis classifier [ 31 ] based on the information of IMUs located on the thigh and shank.…”

Section: Related Workmentioning

confidence: 99%

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

Xiang,

Wang,

Liu

et al. 2024

Bioengineering

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The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

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“…Exoskeleton robots have been widely and effectively utilized in diverse fields of medical rehabilitation, military training, and civilian scenarios ( Zhang et al, 2022 ). Currently, lower limb exoskeletons serve as assistive devices for individuals with disabilities, functioning similarly to medical robots.…”

Section: Introductionmentioning

confidence: 99%

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton

Tang,

Zhao,

et al. 2024

PeerJ Computer Science

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Locomotion mode recognition in humans is fundamental for flexible control in wearable-powered exoskeleton robots. This article proposes a hybrid model that combines a dense convolutional network (DenseNet) and long short-term memory (LSTM) with a channel attention mechanism (SENet) for locomotion mode recognition. DenseNet can automatically extract deep-level features from data, while LSTM effectively captures long-dependent information in time series. To evaluate the validity of the hybrid model, inertial measurement units (IMUs) and pressure sensors were used to obtain motion data from 15 subjects. Five locomotion modes were tested for the hybrid model, such as level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending. Furthermore, the data features of the ramp were inconspicuous, leading to large recognition errors. To address this challenge, the SENet module was incorporated, which improved recognition rates to some extent. The proposed model automatically extracted the features and achieved an average recognition rate of 97.93%. Compared with known algorithms, the proposed model has substantial recognition results and robustness. This work holds promising potential for applications such as limb support and weight bearing.

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Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units

Cited by 15 publications

References 42 publications

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton

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