Intelligent Assistive Exoskeleton with Vision Based Interface

Baklouti, Malek; Guitteny, Vincent; Couvet, Serge

doi:10.1007/978-3-540-69916-3_15

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…However, teleceptive sensing for wearable assistive robots is a very new area of research that only began within the last decade or so. In fact, several early proposals for teleceptive sensing in wearable assistive robotics used sensors that were fixed in the environment to remotely sense user behavior [57,58,59]. However, to be truly wearable, sensors must be portable, and algorithms must function appropriately in an egocentric perspective.…”

Section: Teleception In Wearable Roboticsmentioning

confidence: 99%

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Krausz

Hargrove

2019

Sensors

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Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires feedforward models of desired movement, but also the environmental or contextual awareness that could be provided by teleception. Several recent publications present teleception modalities integrated into control systems and provide preliminary results, for example, for performing hand grasp prediction or endpoint control of an arm assistive device; and gait segmentation, forward prediction of desired locomotion mode, and activity-specific control of a prosthetic leg or exoskeleton. Collectively, several different approaches to incorporating teleception have been used, including sensor fusion, geometric segmentation, and machine learning. In this paper, we summarize the recent and ongoing published work in this promising new area of research.

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Section: Teleception In Wearable Roboticsmentioning

confidence: 99%

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Krausz

Hargrove

2019

Sensors

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“…Alternative sensors include ultrasonic sensors [ 74 ] and camera-based interfaces [ 64 , 73 ]. An important disadvantage of camera-based interfaces is that their functionality largely depends on the light conditions that result in the need of repetitive calibrations during the day [ 152 ].…”

Section: Reviewmentioning

confidence: 99%

Non-invasive control interfaces for intention detection in active movement-assistive devices

Lobo-Prat

Kooren

Stienen

et al. 2014

J NeuroEngineering Rehabil

134

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Active movement-assistive devices aim to increase the quality of life for patients with neuromusculoskeletal disorders. This technology requires interaction between the user and the device through a control interface that detects the user’s movement intention. Researchers have explored a wide variety of invasive and non-invasive control interfaces. To summarize the wide spectrum of strategies, this paper presents a comprehensive review focused on non-invasive control interfaces used to operate active movement-assistive devices. A novel systematic classification method is proposed to categorize the control interfaces based on: (I) the source of the physiological signal, (II) the physiological phenomena responsible for generating the signal, and (III) the sensors used to measure the physiological signal. The proposed classification method can successfully categorize all the existing control interfaces providing a comprehensive overview of the state of the art. Each sensing modality is briefly described in the body of the paper following the same structure used in the classification method. Furthermore, we discuss several design considerations, challenges, and future directions of non-invasive control interfaces for active movement-assistive devices.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-168) contains supplementary material, which is available to authorized users.

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“…mata.khalili@lisv.uvsq.fr benefit from Ambient Intelligence to provide the assistance, like [7] and [8]. There also exists the ones which can be commanded using dedicated interfaces [9] and [10].…”

Section: Introductionmentioning

confidence: 99%

Identification of pedestrian characteristics for assistive systems

Khalili

Hirata

2011

2011 IEEE/SICE International Symposium on System Integration (SII)

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To provide the adapted mobility assistance to people who are in need of, information of specific physical characteristics of users are essential. In this work, we focus on extracting approximate human physique using low-cost sensors. We use Microsoft Kinect to detect and follow human in an indoor environment and measure her/his height and movement speed. Measured height could be used for adjustment of the assistance tools to the proper position for the user. From the movement speed, we can estimate the time that user will reach to the assistance module. It indicates at what moment it should be ready for providing the assistance. In this work, we use an autonomous electric wheelchair for the seat providing task. Wheelchair moves in the environment using the information provided by Kinect, detect and extract the human information and provides the necessary assistance.

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Intelligent Assistive Exoskeleton with Vision Based Interface

Cited by 16 publications

References 8 publications

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Non-invasive control interfaces for intention detection in active movement-assistive devices

Identification of pedestrian characteristics for assistive systems

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