Gait-phase-dependent control using a smart walker for physical training

Li, Pengcheng; Yamada, Yoji; Wan, Xianglong; Uchiyama, Yasushi; Sato, Wakako; Yamada, Kazunori; Yokoya, Mayu

doi:10.1109/icorr.2019.8779563

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…In fact, obtaining and analyzing gait phase information can help develop rehabilitation strategies. For example, function-related muscles can be strengthened at the precise timing as when the corresponding muscle groups are activated, as demonstrated in a previous study, in which we proposed gait-phase-dependent control for rehabilitation [11].…”

Section: Introductionmentioning

confidence: 60%

“…The real-time gait phase estimation method will be used to enhance the performance of lower limb physical training for elderly people. By using the proposed real-time gait phase estimation method, we tried to exert different levels of resistance to young healthy subjects at different gait phases to achieve better training outcomes for certain lower limb muscles [11]. As an example, we increased the level of resistance to a user at 10% to 50% of a gait cycle to train the muscle of gastrocnemius.…”

Section: Discussionmentioning

confidence: 99%

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Gait Phase Estimation Based on User–Walker Interaction Force

Akiyama

Wan

et al. 2021

Applied Sciences

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Smart walkers have been developed for assistance and rehabilitation of elderly people and patients with physical health conditions. A force sensor mounted under the handle is widely used in smart walkers to establish a human–machine interface. The interaction force can be used to control the walker and estimate gait parameters using methods such as the Kalman filter for real-time estimation. However, the estimation performance decreases when the peaks of the interaction force are not captured. To improve the stability and accuracy of gait parameter estimation, we propose an online estimation method to continuously estimate the gait phase and cadence. A multiple model switching mechanism is introduced to improve the estimation performance when gait is asymmetric, and an adaptive rule is proposed to improve the estimation robustness and accuracy. Simulations and experiments demonstrate the effectiveness and accuracy of the proposed gait parameter estimation method. Here, the average estimation error for the gait phase is 0.691 rad when the gait is symmetric and 0.722 rad when it is asymmetric.

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Section: Introductionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Gait Phase Estimation Based on User–Walker Interaction Force

Akiyama

Wan

et al. 2021

Applied Sciences

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“…Force [5], [6], [8], [11], [13], [27], [29], [30], [31], [33], [35], [47], [59] Steering Low None Low Low Low-High Users with a variety of physical and neurological disabilities (e.g. Parkinson's disease, Dementia, Ischemia, Intellectual disability) [5], [6], [8], [35], [59].…”

Section: Force Sensorsmentioning

confidence: 99%

“…People with moderate to mild mobility impairment [11]. Elderly people [13], [27], [31], [47] [27], [29], [30], [33] Force/torque sensors Force and torque…”

Section: Force Sensorsmentioning

confidence: 99%

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Smart Rollators Aid Devices: Current Trends and Challenges

Verdezoto

Ballesteros

Urdiales

2022

IEEE Trans. Human-Mach. Syst.

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Mobility loss has a major impact on autonomy. Smart rollators have been proposed to enhance human abilities when conventional devices are not enough. Many human-robot interaction systems have been proposed in the last decade in this area. Comparative analysis shows that mechanical issues aside, they mainly differ in i) equipped sensors and actuators; ii) input interface; iii) operation modes, and iv) adaptation capabilities. This paper presents a review and a tentative taxonomy of approaches during the last 6 years. In total, 92 papers have been reviewed. We have discarded works not focused on humanrobot interaction or focused only on mechanical adaptation. A critical analysis is provided after the review and classification, highlighting systems tested with their target population.

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