Research on healthy subject gait cycle phase at different walking speeds

Zhang, Huifeng; Qian, Jinwu; Shen, Linyong; Zhang, Yanan

doi:10.1109/robio.2012.6491156

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…The estimation value of the un-scanned angle is γ b is π/6 ≤ γ b ≤ π/3 [20]. We can see intuitively this estimation from Fig Depending on the walking speed, the un-scanned angle of the Smart Shoe can be from π/6 to π/3.…”

Section: Stance Phasementioning

confidence: 74%

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Development of a Smart Shoe for Building a Real-Time 3D Map

Nguyen¹,

La²

2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Abstract-Three dimensional (3D) mapping of environments has gained significant research interest over the last decades because of its important need for environmental modeling and monitoring. There are many successful research efforts in this field, and even it has been turned into commercial products such as Velodyne Lidar [1]. However, due to natural localization challenges, not much research for 3D mapping wearable sensor devices has been successfully reported. In this paper, we are interested in building a smart and wearable shoe which integrates multiple laser scanners and an inertial measurement unit (IMU) to build a 3D map of the environments. The proposed Smart Shoe can collect data and build a real-time 3D map during human walking. Such a smart shoe can support disabled people (blind people) to easily navigate and avoid obstacles in the environment. Additionally, this shoe can help firefighters quickly model and recognize objects in the fired and dark smoke buildings where the cameras may not be useful. The developed localization algorithm using IMU can output a smooth and accurate pose and trajectory of the human walking. This key importance of the shoe localization enables 3D mapping successfully while minimizing data registration error from the laser point cloud.

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Section: Stance Phasementioning

confidence: 74%

“…4), we discovered that the changing angle of the foot could help the shoe scan a maximum 5π/6 of P itch angle above the background. This angle change depends on the walking speeds [20] [22].…”

Section: B Scanning Field Of the Smart Shoementioning

confidence: 99%

Development of a Smart Shoe for Building a Real-Time 3D Map

Nguyen¹,

La²

2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…The general lifted update (22) has a form which makes it difficult to choose L(q) transparently. The next theorem shows how it can be selected by modifying any of the many existing operators that have been proposed in traditional RC.…”

Section: Point-to-point Repetitive Controlmentioning

confidence: 99%

Point-to-point repetitive control of functional electrical stimulation for drop-foot

Page

Freeman

2020

Control Engineering Practice

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Drop-Foot is a common problem resulting from a range of neurological conditions, and prevents normal leg swing during gait, leading to abnormal, inefficient motion with an increased risk of falling. It damages the quality of life of over 122,000 people in the US and 11,400 people in the UK every year.Functional electrical stimulation (FES) addresses drop-foot by artificially contracting the tibialis anterior, and has had considerable success both clinically and commercially. However current commercial controllers are open loop and have long set-up times. The few controllers in the research domain are predominantly open-loop, lack accuracy, and struggle with muscle delays, non-linearities and the onset of fatigue. More advanced controllers require extensive sensor data and/or are highly dependent on an identified model. Recent developments have shown model based controllers combined with learning can facilitate higher accuracy, however previous attempts employed batch-wise learning, and led to disjointed control signals.This paper applies repetitive control (RC) to drop-foot for the first time, facilitating a continuous, smooth process of learning with no resetting. To maximise performance, a comprehensive extension to the standard RC framework is undertaken to enable only isolated time points to be tracked, improving robustness and reducing memory and communication requirements. Experimental data confirms that RC can achieve normal gait when applied to FES-assisted gait with no voluntary effort. The new 'point-to-point' RC framework outperfrmed standard RC, while using only 5 data points per gait cycle and minimal control effort.

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“…3), we discovered that the changing angle of foot motion could help the front and rear laser scanners scan a maximum angle of 5π/6 around the Pitch axis. This maximum angle, of course, depends on walking speeds [16] [17]. Hence, by integrating two laser range scanners on the rear and front of a shoe, the scanning field can reach to a maximum covered angle 5π/6 such as in Fig.…”

Section: Scanning Field Of the Smart Shoementioning

confidence: 99%

“…The 3D space volume covered by the yellow and green color areas is the scanning field of the Smart Shoe moving from the stance phase to the swing phase in one step. Furthermore, the gait and positions of this movement can be divided into two phases and 8 positions [16] [17] as shown in Fig. 3.…”

Section: Scanning Field Of the Smart Shoementioning

confidence: 99%

A Smart Shoe for building a real-time 3D map

Nguyen

Sánchez

et al. 2016

Automation in Construction

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Three dimensional (3D) mapping of environments has gained tremendous attention, from academic to industry to military, owing to the ever increasing needs of environmental modeling as well as monitoring. While many highly effective techniques have been reported thus far, a few even turned into commercial products, none has explored the use of wearable sensors to capture human foot motion, gait, and phase for 3D map construction, especially in the Architecture, Engineering, and Construction (AEC) domain. In this work, we propose a smart (and wearable) shoe, called "Smart Shoe", which integrates multiple laser scanners and an inertial measurement unit (IMU) to build a 3D map of environments in real time. Such a Smart Shoe can be a potential tool for floor plan surveying, in-construction monitoring, planning renovations, space usage planning, managing building maintenance and other tasks in the AEC domain. Besides, this Smart Shoe could assist disabled people (blind people) to navigate and avoid obstacles in the unknown environment. In another case, the shoes may help firefighters quickly model and recognize objects in the firing, dark, and smoky buildings where traditional camera-based approaches might not be applicable. We integrate this shoe with a novel foot localization algorithm that produces a smooth and accurate pose and trajectory of human walking, which is the key enabling technique to minimize data registration errors from laser point cloud.

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Research on healthy subject gait cycle phase at different walking speeds

Cited by 10 publications

References 9 publications

Development of a Smart Shoe for Building a Real-Time 3D Map

Development of a Smart Shoe for Building a Real-Time 3D Map

Point-to-point repetitive control of functional electrical stimulation for drop-foot

A Smart Shoe for building a real-time 3D map

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